From 23597bfcef36efd001f6997735eb6e73eb8fee8f Mon Sep 17 00:00:00 2001 From: Brioch Date: Fri, 21 Nov 2025 13:36:01 +0000 Subject: [PATCH 1/2] mostly spelling --- 00_model.ipynb | 1077 ++-------------------------------------- 01_setup_pstfrom.ipynb | 40 +- 2 files changed, 84 insertions(+), 1033 deletions(-) diff --git a/00_model.ipynb b/00_model.ipynb index f87b2ea..9e16ade 100755 --- a/00_model.ipynb +++ b/00_model.ipynb @@ -7,18 +7,19 @@ "source": [ "# Background\n", "\n", - "This series of tutorials are based on the is based on the GMDSI Open Loop Low Temperature Geothermal System (non-python) tutorials, which can be found here: https://gmdsi.org/blog/gwe_slideshow/\n", + "This series of tutorials are based on the GMDSI Open Loop Low Temperature Geothermal System (non-python) tutorials, which can be found here:\n", "\n", - "The purpose of these `notebooks` is to demonstrate how to:\n", + "https://gmdsi.org/blog/gwe_slideshow/\n", "\n", - " - go about programatically settting up a `PEST++` interface with an unstructured grid model;\n", + "The purpose of these `notebooks` is to demonstrate how to:\n", + " - go about programatically setting up a `PEST++` interface with an unstructured grid model;\n", " - parameterize parameter fields with non-stationary geostatistics informed by conceptual knowledge; and\n", " - undertake surrogate-based data assimilation and predictive uncertainty analysis using data space inversion" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "1050417f", "metadata": {}, "outputs": [], @@ -43,9 +44,9 @@ "\n", "The image below is extracted from the [slideshow](https://gmdsi.org/wp-content/uploads/2025/04/gwf_gwe_model.pptx) file that accompanies the original GMDSI tutorial. It shows the model grid and boundary conditions.\n", "\n", - "The model represents a shallow, channelized alluviual deposits close to a river (North boundary), which pinch out where bedrock outcrops to the South. The system is represented using a single layer model. North boundary is simulated using the RIV package. East and Western boundaries are specified as general head boundaries. \n", + "The model represents a shallow, channelized aluviual deposits close to a river (North boundary), which pinch out where bedrock outcrops to the South. The system is represented using a single layer model. North boundary is simulated using the RIV package. East and Western boundaries are specified as general head boundaries. \n", "\n", - "The model is used to simulate a (past) historical period and a (future) forecast period. In the forecast period an open loop shalow geothermal system is active. The flow model is simulated as steady-state for both historical and forecast periods. The heat transport model is transient. In the foreacst period it simulates periods of seasonaly varying injection temperature. The background temperature is 16°C.\n", + "The model is used to simulate a (past) historical period and a (future) forecast period. In the forecast period an open loop shallow geothermal system is active. The flow model is simulated as steady-state for both historical and forecast periods. The heat transport model is transient. The forecast period simulates periods of seasonaly varying injection temperature. The background temperature is 16°C.\n", "\n", "\n", "![image info](./data/Pmodel.png)\n" @@ -61,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "d4bf9283", "metadata": {}, "outputs": [], @@ -74,26 +75,15 @@ "id": "ae9cdc79", "metadata": {}, "source": [ - "Run the next cell to call a helper function we have prepared, just to amke sure the correct `modflow6` and `pest` executables are in the model filder:" + "Run the next cell to call a helper function we have prepared, just to make sure the correct `modflow6` and `pest` executables are in the model folder:" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "0f75a04c", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'bin/mac'" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "hbd.get_bins(ws)" ] @@ -104,7 +94,7 @@ "metadata": {}, "source": [ "## Run the model once\n", - "Unusualy for our tutorials, this model actualy takes a while to run. We need to run it once to make sure all the model output files are generated.\n", + "Unusually for our tutorials, this model actually takes a while to run. We need to run it once to make sure all the model output files are generated.\n", "\n", "### Warning: this might take a few minutes. \n", "For context, it takes ~5min on a MacBook Pro.\n" @@ -112,772 +102,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "b5505d87", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "./mf6\n", - " MODFLOW 6\n", - " U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n", - " VERSION 6.7.0.dev3 (preliminary) 10/16/2025\n", - " ***DEVELOP MODE***\n", - "\n", - " MODFLOW 6 compiled Oct 16 2025 14:05:53 with Intel(R) Fortran Intel(R) 64\n", - " Compiler Classic for applications running on Intel(R) 64, Version 2021.7.0\n", - " Build 20220726_000000\n", - "\n", - "This software is preliminary or provisional and is subject to \n", - "revision. It is being provided to meet the need for timely best \n", - "science. The software has not received final approval by the U.S. \n", - "Geological Survey (USGS). No warranty, expressed or implied, is made \n", - "by the USGS or the U.S. Government as to the functionality of the \n", - "software and related material nor shall the fact of release \n", - "constitute any such warranty. The software is provided on the \n", - "condition that neither the USGS nor the U.S. Government shall be held \n", - "liable for any damages resulting from the authorized or unauthorized \n", - "use of the software.\n", - "\n", - " \n", - " MODFLOW runs in SEQUENTIAL mode\n", - " \n", - " Run start date and time (yyyy/mm/dd hh:mm:ss): 2025/11/13 18:02:58\n", - " \n", - " Writing simulation list file: mfsim.lst\n", - " Using Simulation name file: mfsim.nam\n", - " \n", - " Solving: Stress period: 1 Time step: 1\n", - " Solving: Stress period: 2 Time step: 1\n", - " Solving: Stress period: 2 Time step: 2\n", - " Solving: Stress period: 2 Time step: 3\n", - " Solving: Stress period: 2 Time step: 4\n", - " Solving: Stress period: 2 Time step: 5\n", - " Solving: Stress period: 2 Time step: 6\n", - " Solving: Stress period: 2 Time step: 7\n", - " Solving: Stress period: 2 Time step: 8\n", - " Solving: Stress period: 2 Time step: 9\n", - " Solving: Stress period: 2 Time step: 10\n", - " 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Solving: Stress period: 34 Time step: 1\n", - " Solving: Stress period: 34 Time step: 2\n", - " Solving: Stress period: 34 Time step: 3\n", - " Solving: Stress period: 34 Time step: 4\n", - " Solving: Stress period: 34 Time step: 5\n", - " Solving: Stress period: 34 Time step: 6\n", - " Solving: Stress period: 34 Time step: 7\n", - " Solving: Stress period: 34 Time step: 8\n", - " Solving: Stress period: 34 Time step: 9\n", - " Solving: Stress period: 34 Time step: 10\n", - " Solving: Stress period: 34 Time step: 11\n", - " Solving: Stress period: 34 Time step: 12\n", - " Solving: Stress period: 34 Time step: 13\n", - " Solving: Stress period: 34 Time step: 14\n", - " Solving: Stress period: 35 Time step: 1\n", - " Solving: Stress period: 35 Time step: 2\n", - " Solving: Stress period: 35 Time step: 3\n", - " Solving: Stress period: 35 Time step: 4\n", - " Solving: Stress period: 35 Time step: 5\n", - " Solving: Stress period: 35 Time step: 6\n", - " Solving: Stress period: 35 Time step: 7\n", - " Solving: Stress period: 35 Time step: 8\n", - " Solving: Stress period: 35 Time step: 9\n", - " Solving: Stress period: 35 Time step: 10\n", - " Solving: Stress period: 36 Time step: 1\n", - " Solving: Stress period: 36 Time step: 2\n", - " Solving: Stress period: 36 Time step: 3\n", - " Solving: Stress period: 36 Time step: 4\n", - " Solving: Stress period: 36 Time step: 5\n", - " Solving: Stress period: 36 Time step: 6\n", - " Solving: Stress period: 36 Time step: 7\n", - " Solving: Stress period: 36 Time step: 8\n", - " Solving: Stress period: 36 Time step: 9\n", - " Solving: Stress period: 36 Time step: 10\n", - " Solving: Stress period: 36 Time step: 11\n", - " Solving: Stress period: 36 Time step: 12\n", - " Solving: Stress period: 36 Time step: 13\n", - " Solving: Stress period: 36 Time step: 14\n", - " Solving: Stress period: 37 Time step: 1\n", - " Solving: Stress period: 37 Time step: 2\n", - " Solving: Stress period: 37 Time step: 3\n", - " Solving: Stress period: 37 Time step: 4\n", - " Solving: Stress period: 37 Time step: 5\n", - " Solving: Stress period: 37 Time step: 6\n", - " Solving: Stress period: 37 Time step: 7\n", - " Solving: Stress period: 37 Time step: 8\n", - " Solving: Stress period: 37 Time step: 9\n", - " Solving: Stress period: 37 Time step: 10\n", - " Solving: Stress period: 38 Time step: 1\n", - " Solving: Stress period: 38 Time step: 2\n", - " Solving: Stress period: 38 Time step: 3\n", - " Solving: Stress period: 38 Time step: 4\n", - " Solving: Stress period: 38 Time step: 5\n", - " Solving: Stress period: 38 Time step: 6\n", - " Solving: Stress period: 38 Time step: 7\n", - " Solving: Stress period: 38 Time step: 8\n", - " Solving: Stress period: 38 Time step: 9\n", - " Solving: Stress period: 38 Time step: 10\n", - " Solving: Stress period: 38 Time step: 11\n", - " Solving: Stress period: 38 Time step: 12\n", - " Solving: Stress period: 38 Time step: 13\n", - " Solving: Stress period: 38 Time step: 14\n", - " Solving: Stress period: 39 Time step: 1\n", - " Solving: Stress period: 39 Time step: 2\n", - " Solving: Stress period: 39 Time step: 3\n", - " Solving: Stress period: 39 Time step: 4\n", - " Solving: Stress period: 39 Time step: 5\n", - " Solving: Stress period: 39 Time step: 6\n", - " Solving: Stress period: 39 Time step: 7\n", - " Solving: Stress period: 39 Time step: 8\n", - " Solving: Stress period: 39 Time step: 9\n", - " Solving: Stress period: 39 Time step: 10\n", - " Solving: Stress period: 40 Time step: 1\n", - " Solving: Stress period: 40 Time step: 2\n", - " Solving: Stress period: 40 Time step: 3\n", - " Solving: Stress period: 40 Time step: 4\n", - " Solving: Stress period: 40 Time step: 5\n", - " Solving: Stress period: 40 Time step: 6\n", - " Solving: Stress period: 40 Time step: 7\n", - " Solving: Stress period: 40 Time step: 8\n", - " Solving: Stress period: 40 Time step: 9\n", - " Solving: Stress period: 40 Time step: 10\n", - " Solving: Stress period: 40 Time step: 11\n", - " Solving: Stress period: 40 Time step: 12\n", - " Solving: Stress period: 40 Time step: 13\n", - " Solving: Stress period: 40 Time step: 14\n", - " Solving: Stress period: 41 Time step: 1\n", - " Solving: Stress period: 41 Time step: 2\n", - " Solving: Stress period: 41 Time step: 3\n", - " Solving: Stress period: 41 Time step: 4\n", - " Solving: Stress period: 41 Time step: 5\n", - " Solving: Stress period: 41 Time step: 6\n", - " Solving: Stress period: 41 Time step: 7\n", - " Solving: Stress period: 41 Time step: 8\n", - " Solving: Stress period: 41 Time step: 9\n", - " Solving: Stress period: 41 Time step: 10\n", - " Solving: Stress period: 42 Time step: 1\n", - " Solving: Stress period: 42 Time step: 2\n", - " Solving: Stress period: 42 Time step: 3\n", - " Solving: Stress period: 42 Time step: 4\n", - " Solving: Stress period: 42 Time step: 5\n", - " Solving: Stress period: 42 Time step: 6\n", - " Solving: Stress period: 42 Time step: 7\n", - " Solving: Stress period: 42 Time step: 8\n", - " Solving: Stress period: 42 Time step: 9\n", - " Solving: Stress period: 42 Time step: 10\n", - " Solving: Stress period: 42 Time step: 11\n", - " Solving: Stress period: 42 Time step: 12\n", - " Solving: Stress period: 42 Time step: 13\n", - " Solving: Stress period: 42 Time step: 14\n", - " Solving: Stress period: 43 Time step: 1\n", - " Solving: Stress period: 43 Time step: 2\n", - " Solving: Stress period: 43 Time step: 3\n", - " Solving: Stress period: 43 Time step: 4\n", - " Solving: Stress period: 43 Time step: 5\n", - " Solving: Stress period: 43 Time step: 6\n", - " Solving: Stress period: 43 Time step: 7\n", - " Solving: Stress period: 43 Time step: 8\n", - " Solving: Stress period: 43 Time step: 9\n", - " Solving: Stress period: 43 Time step: 10\n", - " Solving: Stress period: 44 Time step: 1\n", - " Solving: Stress period: 44 Time step: 2\n", - " Solving: Stress period: 44 Time step: 3\n", - " Solving: Stress period: 44 Time step: 4\n", - " Solving: Stress period: 44 Time step: 5\n", - " Solving: Stress period: 44 Time step: 6\n", - " Solving: Stress period: 44 Time step: 7\n", - " Solving: Stress period: 44 Time step: 8\n", - " Solving: Stress period: 44 Time step: 9\n", - " Solving: Stress period: 44 Time step: 10\n", - " Solving: Stress period: 44 Time step: 11\n", - " Solving: Stress period: 44 Time step: 12\n", - " Solving: Stress period: 44 Time step: 13\n", - " Solving: Stress period: 44 Time step: 14\n", - " Solving: Stress period: 45 Time step: 1\n", - " Solving: Stress period: 45 Time step: 2\n", - " Solving: Stress period: 45 Time step: 3\n", - " Solving: Stress period: 45 Time step: 4\n", - " Solving: Stress period: 45 Time step: 5\n", - " Solving: Stress period: 45 Time step: 6\n", - " Solving: Stress period: 45 Time step: 7\n", - " Solving: Stress period: 45 Time step: 8\n", - " Solving: Stress period: 45 Time step: 9\n", - " Solving: Stress period: 45 Time step: 10\n", - " Solving: Stress period: 46 Time step: 1\n", - " Solving: Stress period: 46 Time step: 2\n", - " Solving: Stress period: 46 Time step: 3\n", - " Solving: Stress period: 46 Time step: 4\n", - " Solving: Stress period: 46 Time step: 5\n", - " Solving: Stress period: 46 Time step: 6\n", - " Solving: Stress period: 46 Time step: 7\n", - " Solving: Stress period: 46 Time step: 8\n", - " Solving: Stress period: 46 Time step: 9\n", - " Solving: Stress period: 46 Time step: 10\n", - " Solving: Stress period: 46 Time step: 11\n", - " Solving: Stress period: 46 Time step: 12\n", - " Solving: Stress period: 46 Time step: 13\n", - " Solving: Stress period: 46 Time step: 14\n", - " Solving: Stress period: 47 Time step: 1\n", - " Solving: Stress period: 47 Time step: 2\n", - " Solving: Stress period: 47 Time step: 3\n", - " Solving: Stress period: 47 Time step: 4\n", - " Solving: Stress period: 47 Time step: 5\n", - " Solving: Stress period: 47 Time step: 6\n", - " Solving: Stress period: 47 Time step: 7\n", - " Solving: Stress period: 47 Time step: 8\n", - " Solving: Stress period: 47 Time step: 9\n", - " Solving: Stress period: 47 Time step: 10\n", - " Solving: Stress period: 48 Time step: 1\n", - " Solving: Stress period: 48 Time step: 2\n", - " Solving: Stress period: 48 Time step: 3\n", - " Solving: Stress period: 48 Time step: 4\n", - " Solving: Stress period: 48 Time step: 5\n", - " Solving: Stress period: 48 Time step: 6\n", - " Solving: Stress period: 48 Time step: 7\n", - " Solving: Stress period: 48 Time step: 8\n", - " Solving: Stress period: 48 Time step: 9\n", - " Solving: Stress period: 48 Time step: 10\n", - " Solving: Stress period: 48 Time step: 11\n", - " Solving: Stress period: 48 Time step: 12\n", - " Solving: Stress period: 48 Time step: 13\n", - " Solving: Stress period: 48 Time step: 14\n", - " Solving: Stress period: 49 Time step: 1\n", - " Solving: Stress period: 49 Time step: 2\n", - " Solving: Stress period: 49 Time step: 3\n", - " Solving: Stress period: 49 Time step: 4\n", - " Solving: Stress period: 49 Time step: 5\n", - " Solving: Stress period: 49 Time step: 6\n", - " Solving: Stress period: 49 Time step: 7\n", - " Solving: Stress period: 49 Time step: 8\n", - " Solving: Stress period: 49 Time step: 9\n", - " Solving: Stress period: 49 Time step: 10\n", - " Solving: Stress period: 50 Time step: 1\n", - " Solving: Stress period: 50 Time step: 2\n", - " Solving: Stress period: 50 Time step: 3\n", - " Solving: Stress period: 50 Time step: 4\n", - " Solving: Stress period: 50 Time step: 5\n", - " Solving: Stress period: 50 Time step: 6\n", - " Solving: Stress period: 50 Time step: 7\n", - " Solving: Stress period: 50 Time step: 8\n", - " Solving: Stress period: 50 Time step: 9\n", - " Solving: Stress period: 50 Time step: 10\n", - " Solving: Stress period: 50 Time step: 11\n", - " Solving: Stress period: 50 Time step: 12\n", - " Solving: Stress period: 50 Time step: 13\n", - " Solving: Stress period: 50 Time step: 14\n", - " Solving: Stress period: 51 Time step: 1\n", - " Solving: Stress period: 51 Time step: 2\n", - " Solving: Stress period: 51 Time step: 3\n", - " Solving: Stress period: 51 Time step: 4\n", - " Solving: Stress period: 51 Time step: 5\n", - " Solving: Stress period: 51 Time step: 6\n", - " Solving: Stress period: 51 Time step: 7\n", - " Solving: Stress period: 51 Time step: 8\n", - " Solving: Stress period: 51 Time step: 9\n", - " Solving: Stress period: 51 Time step: 10\n", - " Solving: Stress period: 52 Time step: 1\n", - " Solving: Stress period: 52 Time step: 2\n", - " Solving: Stress period: 52 Time step: 3\n", - " Solving: Stress period: 52 Time step: 4\n", - " Solving: Stress period: 52 Time step: 5\n", - " Solving: Stress period: 52 Time step: 6\n", - " Solving: Stress period: 52 Time step: 7\n", - " Solving: Stress period: 52 Time step: 8\n", - " Solving: Stress period: 52 Time step: 9\n", - " Solving: Stress period: 52 Time step: 10\n", - " Solving: Stress period: 52 Time step: 11\n", - " Solving: Stress period: 52 Time step: 12\n", - " Solving: Stress period: 52 Time step: 13\n", - " Solving: Stress period: 52 Time step: 14\n", - " Solving: Stress period: 53 Time step: 1\n", - " Solving: Stress period: 53 Time step: 2\n", - " Solving: Stress period: 53 Time step: 3\n", - " Solving: Stress period: 53 Time step: 4\n", - " Solving: Stress period: 53 Time step: 5\n", - " Solving: Stress period: 53 Time step: 6\n", - " Solving: Stress period: 53 Time step: 7\n", - " Solving: Stress period: 53 Time step: 8\n", - " Solving: Stress period: 53 Time step: 9\n", - " Solving: Stress period: 53 Time step: 10\n", - " Solving: Stress period: 54 Time step: 1\n", - " Solving: Stress period: 54 Time step: 2\n", - " Solving: Stress period: 54 Time step: 3\n", - " Solving: Stress period: 54 Time step: 4\n", - " Solving: Stress period: 54 Time step: 5\n", - " Solving: Stress period: 54 Time step: 6\n", - " Solving: Stress period: 54 Time step: 7\n", - " Solving: Stress period: 54 Time step: 8\n", - " Solving: Stress period: 54 Time step: 9\n", - " Solving: Stress period: 54 Time step: 10\n", - " Solving: Stress period: 54 Time step: 11\n", - " Solving: Stress period: 54 Time step: 12\n", - " Solving: Stress period: 54 Time step: 13\n", - " Solving: Stress period: 54 Time step: 14\n", - " Solving: Stress period: 55 Time step: 1\n", - " Solving: Stress period: 55 Time step: 2\n", - " Solving: Stress period: 55 Time step: 3\n", - " Solving: Stress period: 55 Time step: 4\n", - " Solving: Stress period: 55 Time step: 5\n", - " Solving: Stress period: 55 Time step: 6\n", - " Solving: Stress period: 55 Time step: 7\n", - " Solving: Stress period: 55 Time step: 8\n", - " Solving: Stress period: 55 Time step: 9\n", - " Solving: Stress period: 55 Time step: 10\n", - " Solving: Stress period: 56 Time step: 1\n", - " Solving: Stress period: 56 Time step: 2\n", - " Solving: Stress period: 56 Time step: 3\n", - " Solving: Stress period: 56 Time step: 4\n", - " Solving: Stress period: 56 Time step: 5\n", - " Solving: Stress period: 56 Time step: 6\n", - " Solving: Stress period: 56 Time step: 7\n", - " Solving: Stress period: 56 Time step: 8\n", - " Solving: Stress period: 56 Time step: 9\n", - " Solving: Stress period: 56 Time step: 10\n", - " Solving: Stress period: 56 Time step: 11\n", - " Solving: Stress period: 56 Time step: 12\n", - " Solving: Stress period: 56 Time step: 13\n", - " Solving: Stress period: 56 Time step: 14\n", - " Solving: Stress period: 57 Time step: 1\n", - " Solving: Stress period: 57 Time step: 2\n", - " Solving: Stress period: 57 Time step: 3\n", - " Solving: Stress period: 57 Time step: 4\n", - " Solving: Stress period: 57 Time step: 5\n", - " Solving: Stress period: 57 Time step: 6\n", - " Solving: Stress period: 57 Time step: 7\n", - " Solving: Stress period: 57 Time step: 8\n", - " Solving: Stress period: 57 Time step: 9\n", - " Solving: Stress period: 57 Time step: 10\n", - " Solving: Stress period: 58 Time step: 1\n", - " Solving: Stress period: 58 Time step: 2\n", - " Solving: Stress period: 58 Time step: 3\n", - " Solving: Stress period: 58 Time step: 4\n", - " Solving: Stress period: 58 Time step: 5\n", - " Solving: Stress period: 58 Time step: 6\n", - " Solving: Stress period: 58 Time step: 7\n", - " Solving: Stress period: 58 Time step: 8\n", - " Solving: Stress period: 58 Time step: 9\n", - " Solving: Stress period: 58 Time step: 10\n", - " Solving: Stress period: 58 Time step: 11\n", - " Solving: Stress period: 58 Time step: 12\n", - " Solving: Stress period: 58 Time step: 13\n", - " Solving: Stress period: 58 Time step: 14\n", - " Solving: Stress period: 59 Time step: 1\n", - " Solving: Stress period: 59 Time step: 2\n", - " Solving: Stress period: 59 Time step: 3\n", - " Solving: Stress period: 59 Time step: 4\n", - " Solving: Stress period: 59 Time step: 5\n", - " Solving: Stress period: 59 Time step: 6\n", - " Solving: Stress period: 59 Time step: 7\n", - " Solving: Stress period: 59 Time step: 8\n", - " Solving: Stress period: 59 Time step: 9\n", - " Solving: Stress period: 59 Time step: 10\n", - " Solving: Stress period: 60 Time step: 1\n", - " Solving: Stress period: 60 Time step: 2\n", - " Solving: Stress period: 60 Time step: 3\n", - " Solving: Stress period: 60 Time step: 4\n", - " Solving: Stress period: 60 Time step: 5\n", - " Solving: Stress period: 60 Time step: 6\n", - " Solving: Stress period: 60 Time step: 7\n", - " Solving: Stress period: 60 Time step: 8\n", - " Solving: Stress period: 60 Time step: 9\n", - " Solving: Stress period: 60 Time step: 10\n", - " Solving: Stress period: 60 Time step: 11\n", - " Solving: Stress period: 60 Time step: 12\n", - " Solving: Stress period: 60 Time step: 13\n", - " Solving: Stress period: 60 Time step: 14\n", - " Solving: Stress period: 61 Time step: 1\n", - " Solving: Stress period: 61 Time step: 2\n", - " Solving: Stress period: 61 Time step: 3\n", - " Solving: Stress period: 61 Time step: 4\n", - " Solving: Stress period: 61 Time step: 5\n", - " Solving: Stress period: 61 Time step: 6\n", - " Solving: Stress period: 61 Time step: 7\n", - " Solving: Stress period: 61 Time step: 8\n", - " Solving: Stress period: 61 Time step: 9\n", - " Solving: Stress period: 61 Time step: 10\n", - " \n", - " Run end date and time (yyyy/mm/dd hh:mm:ss): 2025/11/13 18:07:45\n", - " Elapsed run time: 4 Minutes, 47.135 Seconds\n", - " \n", - " Normal termination of simulation.\n" - ] - } - ], + "outputs": [], "source": [ "pyemu.os_utils.run(f\"mf6\", cwd=ws)" ] @@ -894,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "2767116e", "metadata": {}, "outputs": [], @@ -910,74 +138,30 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "1cff249d", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['DISV',\n", - " 'IC',\n", - " 'NPF',\n", - " 'CHD',\n", - " 'RIV_OBS',\n", - " 'RIV',\n", - " 'RCH',\n", - " 'WELL_INJECT',\n", - " 'WELL_EXTRACT',\n", - " 'OC',\n", - " 'OBS']" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "gwf.get_package_list()" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "162f94b7", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['DISV', 'OBS', 'OC', 'SSM_SPC', 'SSM', 'EST', 'CND', 'ADV', 'IC']" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "gwe.get_package_list()" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "ed1159cb", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[2.58e-04, 2.57e-04, 2.60e-04, ..., 1.05e-04, 3.80e-05, 1.32e-05]],\n", - " shape=(1, 13417))" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "k = gwf.npf.k.get_data()\n", "k" @@ -993,21 +177,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "02ffee08", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2, 1, 1, 13417)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "heads = gwf.output.head().get_alldata()\n", "heads.shape" @@ -1023,21 +196,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "676e12cd", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(61, 1, 1, 13417)" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "temp = gwe.output.temperature().get_alldata()\n", "temp.shape" @@ -1045,21 +207,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "f2ba0db5", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "gwf.npf.k.plot()" + "gwf.npf.k.plot(colorbar=True)" ] }, { @@ -1153,13 +283,13 @@ "source": [ "# Conceptual model\n", "\n", - "A key aspect of this case is that, we have some conceptual knowledge about the geostatistics of the hydraulic conducvitiy. \n", + "A key aspect of this case is that, we have some conceptual knowledge about the geostatistics of the hydraulic conductivity. \n", "\n", "We sat down with the project geologist and translated their conceptual knowledge on \"how K is expected to vary in space\" into a set of hyperparameters assigned to pilot points. These include:\n", "\n", " - mean K value\n", " - variance\n", - " - spatial correlatin length\n", + " - spatial correlation length\n", " - anisotropy ratio\n", " - bearing\n", "\n", @@ -1168,120 +298,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "e6bdcbd4", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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xymeanvarahanisbearing
point
11.549167e+064.364592e+060.000150.15600.03.0120.0
21.549794e+064.364694e+060.000150.15600.03.0130.0
31.550443e+064.364913e+060.000150.15600.03.0130.0
41.551101e+064.364781e+060.000150.15600.03.0113.0
51.551591e+064.364520e+060.000150.15600.03.0113.0
\n", - "
" - ], - "text/plain": [ - " x y mean var a hanis bearing\n", - "point \n", - "1 1.549167e+06 4.364592e+06 0.00015 0.15 600.0 3.0 120.0\n", - "2 1.549794e+06 4.364694e+06 0.00015 0.15 600.0 3.0 130.0\n", - "3 1.550443e+06 4.364913e+06 0.00015 0.15 600.0 3.0 130.0\n", - "4 1.551101e+06 4.364781e+06 0.00015 0.15 600.0 3.0 113.0\n", - "5 1.551591e+06 4.364520e+06 0.00015 0.15 600.0 3.0 113.0" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df = pd.read_csv(os.path.join(\"data\",\"conceptual_kh.pts\"), sep=r'\\s+', index_col=0)\n", "df.rename(columns={'easting':\"x\",\"northing\":\"y\"}, inplace=True)\n", @@ -1293,28 +313,17 @@ "id": "8de5b284", "metadata": {}, "source": [ - "In the plot below, at each pilot point, we have plotted the mean K value (color of the point), the line of bearing, the length of which reflects the correlation length (longer lines means greater correlation), and the perpendicular line showing the transversal bearing&correlation length.\n", + "In the plot below, at each pilot point, we have plotted the mean K value (color of the point), the line of bearing, the length of which reflects the correlation length (longer lines means greater correlation), and the perpendicular line showing the transversal bearing and correlation length.\n", "\n", - "Thi sinformation will be used later to generate random feilds of K which respect these patterns of heterogeinty. Strap in!" + "This information will be used later to generate random fields of K which respect these patterns of heterogeneity. Strap in!" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "7c67548f", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig,ax = plt.subplots(1,1,figsize=(6,4))\n", "ax.set_aspect('equal')\n", @@ -1356,11 +365,19 @@ "\n", "fig.tight_layout();" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "52fb9a67-d853-46a3-8f33-d088a5591243", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "gwe", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, diff --git a/01_setup_pstfrom.ipynb b/01_setup_pstfrom.ipynb index 93ed591..d93ea7e 100755 --- a/01_setup_pstfrom.ipynb +++ b/01_setup_pstfrom.ipynb @@ -27,9 +27,11 @@ "source": [ "# Getting started\n", "\n", - "These notebooks will be less verbose than other GMDSI tutorial notebooks. We assume the reader is already familiar with many of the topics. We focus specificaly on aspects that relate to:\n", + "These notebooks will be less verbose than other GMDSI tutorial notebooks. We assume the reader is already familiar with many of the topics. \n", + "\n", + "We focus specificaly on aspects that relate to:\n", "- `PstFrom` with a `mf6` `disv` grid\n", - "- use of hyper parameters\n", + "- use of hyperparameters with `pypestutils`\n", "- passing in prior knowledge to hyperparameters\n", "- (subsequent notebooks) use of DSI\n", "\n", @@ -1265,11 +1267,43 @@ "print(pe.shape,pf.pst.npar,pf.pst.npar_adj)\n", "assert pe.shape[1] == pf.pst.npar_adj" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4820fc80-8927-41d1-b922-a4ca2f095d2d", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "803c84f4-2650-4e47-92d6-02875b2e6f3f", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "789fca27-c7fc-4f5e-9c44-f64ed05639d8", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "43ee11f4-8431-4641-a5af-9b6aa9f1a72f", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "gwe", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, From 366973a090127cbfd11c6de75ea8f9ac09afa6d3 Mon Sep 17 00:00:00 2001 From: Brioch Date: Sat, 22 Nov 2025 16:05:28 +0000 Subject: [PATCH 2/2] more sp and minor things --- 00_model.ipynb | 8 +- 01_setup_pstfrom.ipynb | 139 +- 02_priomc.ipynb | 28 +- 03_dsi.ipynb | 36 +- 04_dsiae.ipynb | 44 +- model/mfsim.lst | 3088 ---------------------------------------- 6 files changed, 105 insertions(+), 3238 deletions(-) delete mode 100644 model/mfsim.lst diff --git a/00_model.ipynb b/00_model.ipynb index 4908e3b..322090f 100755 --- a/00_model.ipynb +++ b/00_model.ipynb @@ -12,7 +12,7 @@ "https://gmdsi.org/blog/gwe_slideshow/\n", "\n", "The purpose of these `notebooks` is to demonstrate how to:\n", - " - go about programatically setting up a `PEST++` interface with an unstructured grid model;\n", + " - go about programmatically setting up a `PEST++` interface with an unstructured grid model;\n", " - parameterize parameter fields with non-stationary geostatistics informed by conceptual knowledge; and\n", " - undertake surrogate-based data assimilation and predictive uncertainty analysis using data space inversion" ] @@ -44,9 +44,9 @@ "\n", "The image below is extracted from the [slideshow](https://gmdsi.org/wp-content/uploads/2025/04/gwf_gwe_model.pptx) file that accompanies the original GMDSI tutorial. It shows the model grid and boundary conditions.\n", "\n", - "The model represents a shallow, channelized aluviual deposits close to a river (North boundary), which pinch out where bedrock outcrops to the South. The system is represented using a single layer model. North boundary is simulated using the RIV package. East and Western boundaries are specified as general head boundaries. \n", + "The model represents a shallow, channelized alluvial deposits close to a river (North boundary), which pinch out where bedrock outcrops to the South. The system is represented using a single layer model. North boundary is simulated using the RIV package. East and Western boundaries are specified as general head boundaries. \n", "\n", - "The model is used to simulate a (past) historical period and a (future) forecast period. In the forecast period an open loop shallow geothermal system is active. The flow model is simulated as steady-state for both historical and forecast periods. The heat transport model is transient. The forecast period simulates periods of seasonaly varying injection temperature. The background temperature is 16°C.\n", + "The model is used to simulate a (past) historical period and a (future) forecast period. In the forecast period an open loop shallow geothermal system is active. The flow model is simulated as steady-state for both historical and forecast periods. The heat transport model is transient. The forecast period simulates periods of seasonally varying injection temperature. The background temperature is 16°C.\n", "\n", "\n", "![image info](./data/Pmodel.png)\n" @@ -252,7 +252,7 @@ "id": "2d371601", "metadata": {}, "source": [ - "A look at everyones favourite paramter..." + "A look at everyone's favorite parameter..." ] }, { diff --git a/01_setup_pstfrom.ipynb b/01_setup_pstfrom.ipynb index 2f0a1ae..d7a1a03 100755 --- a/01_setup_pstfrom.ipynb +++ b/01_setup_pstfrom.ipynb @@ -29,7 +29,7 @@ "\n", "These notebooks will be less verbose than other GMDSI tutorial notebooks. We assume the reader is already familiar with many of the topics. \n", "\n", - "We focus specificaly on aspects that relate to:\n", + "We focus specifically on aspects that relate to:\n", "- `PstFrom` with a `mf6` `disv` grid\n", "- use of hyperparameters with `pypestutils`\n", "- passing in prior knowledge to hyperparameters\n", @@ -62,8 +62,10 @@ { "cell_type": "code", "execution_count": null, - "id": "cd6a229b", - "metadata": {}, + "id": "e781d5bb-56a0-4889-b6e2-fe29b88ed21b", + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ "# load simulation\n", @@ -75,29 +77,21 @@ { "cell_type": "code", "execution_count": null, - "id": "f60f390d", - "metadata": {}, - "outputs": [], - "source": [ - "tdis = sim.tdis\n", - "perioddata = tdis.perioddata.get_data()\n", - "\n", - "perioddata['nstp'] = 1\n", - "\n", - "perioddata\n", - "tdis.perioddata.set_data(perioddata)\n", - "tdis.write()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f0eef6a3", - "metadata": {}, + "id": "a2348537-8f67-45f8-af43-2338e980a79d", + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ - "# run the model once to make sure it works\n", - "pyemu.os_utils.run(\"mf6\",cwd=tmp_d)" + "# because this model is a little slow... only run if starting fresh (hopefully we ran in the last notebook?):\n", + "if 'mfsim.lst' in os.listdir(tmp_d):\n", + " with open(os.path.join(tmp_d, 'mfsim.lst'), 'r') as fp:\n", + " lines = fp.readlines()\n", + "else:\n", + " lines = ['no']\n", + "if \"Normal termination\" not in lines[-1]:\n", + " # run the model once to make sure it works\n", + " pyemu.os_utils.run(\"mf6\", cwd=tmp_d)" ] }, { @@ -105,7 +99,7 @@ "id": "8ca97268", "metadata": {}, "source": [ - "This model is a `DISV` grid. `PstFrom` is going to require the `flopy` model grid object to setup pilot points and spatialy varying covariance:" + "This model is a `DISV` grid. `PstFrom` is going to require the `flopy` model grid object to setup pilot points and spatially varying covariance:" ] }, { @@ -155,7 +149,7 @@ "id": "e7fa6d8a", "metadata": {}, "source": [ - "We are going to keep things super simple for this tutorial. We are only going to parameterize hydrualic conductivity. As we are all sofisticated, we understand that in a real-world application other parameters and boundary conditions would likely also be important aspects to consider for data assimilation and uncertianty analysis...." + "We are going to keep things super simple for this tutorial. We are only going to parameterize hydraulic conductivity. As we are all sofisticated, we understand that in a real-world application other parameters and boundary conditions would likely also be important aspects to consider for data assimilation and uncertainty analysis...." ] }, { @@ -177,7 +171,7 @@ "id": "9da02034", "metadata": {}, "source": [ - "Unfortunatley `flopy` doesnt write tidy model input files...so we need to fix them..." + "Unfortunately `flopy` doesn't write tidy model input files...so we need to fix them..." ] }, { @@ -210,7 +204,7 @@ "id": "5666c6b4", "metadata": {}, "source": [ - "No one specifgied the `idomain` in the original model setup, so lets just create a \"zone array\". `PstFrom` expects this when we setup pilot points and so on later. Note that the shape of `ib` is the same as the shape of the `k`:" + "No one specified the `idomain` in the original model setup, so lets just create a \"zone array\". `PstFrom` expects this when we setup pilot points and so on later. Note that the shape of `ib` is the same as the shape of the `k`:" ] }, { @@ -249,7 +243,7 @@ "id": "980b482b", "metadata": {}, "source": [ - "However, `PstFrom` and `pypestutils` expect a strict format in terms of column anmes and information. So first we need to make that. A pilot point file must have the following columns: `['name','zone','x','y','parval1']`" + "However, `PstFrom` and `pypestutils` expect a strict format in terms of column names and information. So first we need to make that. A pilot point file must have the following columns: `['name','zone','x','y','parval1']`" ] }, { @@ -293,7 +287,7 @@ "id": "dca6384e", "metadata": {}, "source": [ - "We spatialy varying mean values of K in the conceptaul pilot points..." + "We spatially varying mean values of K in the conceptual pilot points..." ] }, { @@ -360,7 +354,7 @@ "id": "0d530d9c", "metadata": {}, "source": [ - "Now, setup a goestatistical structure to pass to `PstFrom`. This is the geostatiscs for the \"hyper parameter\". Inception much...\n", + "Now, setup a geostatistical structure to pass to `PstFrom`. This is the geostatiscs for the \"hyper parameter\". Inception much...\n", "\n", "Lets just use the median from the conceptual pilot points:" ] @@ -435,7 +429,7 @@ " pp_options={\"prep_hyperpars\":True, \"pp_space\":ppfname}\n", "```\n", "\n", - "Where we have specfied that `PstFrom` shoudl setup hyper parameters, and use the `ppfname` file for pilot point locations." + "Where we have specified that `PstFrom` should setup hyper parameters, and use the `ppfname` file for pilot point locations." ] }, { @@ -567,7 +561,7 @@ "id": "be4a3d0b", "metadata": {}, "source": [ - "OK, so lets go through each of the hyper parameters an dparameterize them using pilot points...same as before..." + "OK, so lets go through each of the hyper parameters and parameterize them using pilot points...same as before..." ] }, { @@ -575,7 +569,7 @@ "id": "f5bd86e6", "metadata": {}, "source": [ - "Lets start with anisotropy. We are going to make these hyperparameters all \"additive\" type paraemters. " + "Lets start with anisotropy. We are going to make these hyperparameters all \"additive\" type parameters. " ] }, { @@ -603,22 +597,6 @@ "ppdf" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "3a0ce04f", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "08794faa", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -626,12 +604,8 @@ "metadata": {}, "outputs": [], "source": [ - "\n", "afile = tag+'.aniso.dat'\n", - "\n", - "\n", "tidy_array(os.path.join(template_ws,afile))\n", - "\n", "atag = afile.split('.')[0].replace(\"_\",\"-\")+\"-aniso\"\n", "_df = pf.add_parameters(afile,\n", " par_type=\"pilotpoints\",\n", @@ -650,14 +624,6 @@ "_ = pf.add_observations(afile, prefix=atag, obsgp=atag)" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "4567fbd4", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "id": "03471fa7", @@ -734,9 +700,6 @@ "metadata": {}, "outputs": [], "source": [ - "\n", - "\n", - "\n", "afile = tag+'.corrlen.dat'\n", "tidy_array(os.path.join(template_ws,afile))\n", "atag = afile.split('.')[0].replace(\"_\",\"-\")+\"-corrlen\"\n", @@ -784,7 +747,7 @@ "source": [ "# Check the prior K fields\n", "\n", - "An extremly useful check can be done now. Note that we have not yet added a model run to the `PstFrom`. All that is in the forward run workflow up to now is the interpolation from pilot points to the model grid. In other words, the interpolation is our \"forward run\" at the moment. \n", + "An extremely useful check can be done now. Note that we have not yet added a model run to the `PstFrom`. All that is in the forward run workflow up to now is the interpolation from pilot points to the model grid. In other words, the interpolation is our \"forward run\" at the moment. \n", "\n", "Build the pest control file and forward run .py file to see:" ] @@ -814,11 +777,11 @@ "id": "127d561c", "metadata": {}, "source": [ - "This is super powerful. We can generate a prior ensemble of pilot point values, run that ensemble and collate all the generater model input parameter fields. We can then check them and make sure they make sense and look pretty :) \n", + "This is super powerful. We can generate a prior ensemble of pilot point values, run that ensemble and collate all the generated model input parameter fields. We can then check them and make sure they make sense and look pretty :) \n", "\n", "All we need to do is generate the ensemble and run it once. Because we are not running modflow, this will be super fast!\n", "\n", - "Lets just make sure wverything is working first:" + "Lets just make sure everything is working first:" ] }, { @@ -988,7 +951,7 @@ "id": "639b24a1", "metadata": {}, "source": [ - "Read in the results of the `pestpp-ies` prior monte carlo. We can use some the `Pst` inbuilt helpers for this. Start b reading the .pst file form the mater dir:" + "Read in the results of the `pestpp-ies` Prior Monte Carlo. We can use some the `Pst` inbuilt helpers for this. Start by reading the .pst file form the mater dir:" ] }, { @@ -1006,7 +969,7 @@ "id": "7ba450ff", "metadata": {}, "source": [ - "Now, if parmeter or observation ensemble files are avialable in the folder, `pyemu` will try and load those:" + "Now, if parameter or observation ensemble files are available in the folder, `pyemu` will try and load those:" ] }, { @@ -1102,7 +1065,7 @@ "id": "c24eb449", "metadata": {}, "source": [ - "This provides a practical way of checking that all the plumibg works...that there arent silly mistakes with parameter values and bounds etc...and that you are happy with how the prior knowledge is being expressed through parameterisation. It can be quite helpfull to show results at this stage to stakeholders for example, to ensure that everyone agrees on the \"reasonableness\" of parameter values and distirbutions." + "This provides a practical way of checking that all the plumbing works...that there aren't silly mistakes with parameter values and bounds etc...and that you are happy with how the prior knowledge is being expressed through parameterisation. It can be quite helpful to show results at this stage to stakeholders for example, to ensure that everyone agrees on the \"reasonableness\" of parameter values and distributions." ] }, { @@ -1112,9 +1075,9 @@ "source": [ "# Finishing up the PEST setup\n", "\n", - "Once we are happy with that, we can go through the process of adding in the rest of the PEST setup. Such as observcations, other paramters...and importnatly the mf6 model run.\n", + "Once we are happy with that, we can go through the process of adding in the rest of the PEST setup. Such as observations, other parameters...and importantly the mf6 model run.\n", "\n", - "To make our lives easier later on when we use DSI, we are simply going to track the model simualted heads and temperatures in all cells and all stressperiods. We have prepared a utulity function that processes `mf6` outpfiles and writes observations to clean .txt files (see `herebedragons.py` for details; its pretty simple). \n", + "To make our lives easier later on when we use DSI, we are simply going to track the model simulated heads and temperatures in all cells and all stressperiods. We have prepared a utility function that processes `mf6` output files and writes observations to clean .txt files (see `herebedragons.py` for details; it's pretty simple). \n", "\n", "Lets just call it and run here:" ] @@ -1267,9 +1230,9 @@ "id": "eaeff0cf", "metadata": {}, "source": [ - "Now we can draw the prior. Lets draw a large number of reals so we can play around with DSI later. \n", + "Now we can draw the prior. Let's draw a large number of reals so we can play around with DSI later. \n", "\n", - "And that is it...pest setup ready to run a prior monte carlo." + "And that is it...pest setup ready to run a Prior Monte Carlo." ] }, { @@ -1296,31 +1259,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4820fc80-8927-41d1-b922-a4ca2f095d2d", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "803c84f4-2650-4e47-92d6-02875b2e6f3f", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "789fca27-c7fc-4f5e-9c44-f64ed05639d8", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "43ee11f4-8431-4641-a5af-9b6aa9f1a72f", + "id": "9db9494d-d251-4f09-a108-1a2ab4eca552", "metadata": {}, "outputs": [], "source": [] diff --git a/02_priomc.ipynb b/02_priomc.ipynb index 3fde6be..0ba699c 100644 --- a/02_priomc.ipynb +++ b/02_priomc.ipynb @@ -24,10 +24,10 @@ "source": [ "# Intro\n", "\n", - "This notebook runs a prior monte carlo using the pest setup generated in the `setup_pstfrom` notebook. We will run the prior parameter ensemble once and stop. We will take a look at some of the results.\n", + "This notebook runs a Prior Monte Carlo using the pest setup generated in the `setup_pstfrom` notebook. We will run the prior parameter ensemble once and stop. We will take a look at some of the results.\n", "\n", "### Warning\n", - "This notebook can take some time to run. That is the cost of a >5min model run time...imagine if your model takes longer than that (#suffering). For context, this takes about 60min on a MacBook Pro. Expect longer on Windows. We recommend setting this to run over night or when you go out for lunch, before proceeding to the next notebooks. " + "This notebook can take some time to run. That is the cost of a >5 min model run time...imagine if your model takes longer than that (#suffering). For context, this takes about 600 min on a MacBook Pro. Expect longer on Windows. We recommend setting this to run over night or when you go out for lunch, before proceeding to the next notebooks. " ] }, { @@ -117,7 +117,7 @@ "id": "98fd3310", "metadata": {}, "source": [ - "### Warning: set num_workers according to your reserouces\n", + "### Warning: set num_workers according to your resources\n", "\n", "The model took ~1 min to run... so that would be about 16.7h for the full 100 runs in serial. Gonna need to run them in parallel to be reasonable. \n", "\n", @@ -144,11 +144,11 @@ "source": [ "\n", "pyemu.os_utils.start_workers(t_d, # the folder which contains the \"template\" PEST dataset\n", - " 'pestpp-ies', #the PEST software version we want to run\n", - " 'pest.pst', # the control file to use with PEST\n", - " num_workers=num_workers, #how many agents to deploy\n", - " worker_root='.', #where to deploy the agent directories; relative to where python is running\n", - " master_dir=m_d, #the manager directory\n", + " 'pestpp-ies', #the PEST software version we want to run\n", + " 'pest.pst', # the control file to use with PEST\n", + " num_workers=num_workers, #how many agents to deploy\n", + " worker_root='.', #where to deploy the agent directories; relative to where python is running\n", + " master_dir=m_d, #the manager directory\n", " )" ] }, @@ -210,7 +210,7 @@ "source": [ "## Make some figures\n", "\n", - "Lets look at some of the simualted max temperature fields." + "Lets look at some of the simulated max temperature fields." ] }, { @@ -255,11 +255,19 @@ " plt.show()\n", " plt.close();" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "30ca6c2a-225c-4407-809a-9f246f95f10f", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "gwe", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, diff --git a/03_dsi.ipynb b/03_dsi.ipynb index c30c216..4c0b2ed 100755 --- a/03_dsi.ipynb +++ b/03_dsi.ipynb @@ -25,17 +25,17 @@ "source": [ "# Intro\n", "\n", - "We saw that this awfully long running model (~5min) is going to be a major pain to history match because it takes too long and we dont have a huge (and free) cluster at our disposal...so what can we do? Surrogate models to the rescue!\n", + "We saw that this awfully long running model (~5min) is going to be a major pain to history match because it takes too long and we don't have a huge (and free) cluster at our disposal...so what can we do? Surrogate models to the rescue!\n", "\n", "We wont go into the details on data space inversion (DSI). See the various GMDSI videos and tutorial notebooks for more technical details and background. Here we focus on showing how to implement it with `pyemu`.\n", "\n", "What we need:\n", - " - results from a prior monte carlo\n", + " - results from a Prior Monte Carlo\n", " - some data to assimilate\n", " - some prediction we care about\n", "\n", "## New things in this tutorial\n", - " - We introduce `pestpp` new (as of Nov 2025) external run manager. This allows the user to handle paralel forward runs without relying on pest \"workers\"." + " - We introduce `pestpp` new (as of Nov 2025) external run manager. This allows the user to handle parallel forward runs without relying on pest \"workers\"." ] }, { @@ -79,7 +79,7 @@ "source": [ "## Choose the truth\n", "\n", - "Lets choose one of the realizations as the rtuth, then remove if form the training data. We are going to select an inconvenient truth: one in which the max temperature plume extends a bit farther than most." + "Lets choose one of the realizations as the truth, then remove if form the training data. We are going to select an inconvenient truth: one in which the max temperature plume extends a bit farther than most." ] }, { @@ -136,7 +136,7 @@ "id": "31d09618", "metadata": {}, "source": [ - "Drop the truth form the training data set" + "Drop the truth from the training data set" ] }, { @@ -328,7 +328,7 @@ "source": [ "# Start DSI\n", "\n", - "For DSI, we dont need all the observations we have been tracking. We only really need the non-zero weighted obs and the predictions of interest. " + "For DSI, we don't need all the observations we have been tracking. We only really need the non-zero weighted obs and the predictions of interest. " ] }, { @@ -350,7 +350,7 @@ "id": "534a3b6e", "metadata": {}, "source": [ - "Lets use normal score transformation (usualy a good idea...)" + "Lets use normal score transformation (usually a good idea...)" ] }, { @@ -396,9 +396,9 @@ "source": [ "Nothing new so far. But now we can introduce some new functionality in `pyemu.DSI`.\n", "\n", - "Recall that the dsi \"model\" is really just a matrix multiplication. We are passing in the `pval` array of parameter values and mulplitying it by the projection matrix. This is easly vectorizable...\n", + "Recall that the dsi \"model\" is really just a matrix multiplication. We are passing in the `pval` array of parameter values and multiplying it by the projection matrix. This is easily vectorizable...\n", "\n", - "Instread of passing each realization of `pvals` once at a time, we can do the same matrix multiplication on an ensemble of `pvals` - all in one go. Traditionaly, `pest` and `pestpp` were not designed for this. However, `pestpp` now allows a user to handle the \"paralelisation\" externaly. \n", + "Instead of passing each realization of `pvals` once at a time, we can do the same matrix multiplication on an ensemble of `pvals` - all in one go. Traditionally, `pest` and `pestpp` were not designed for this. However, `pestpp` now allows a user to handle the \"parallelisation\" externally. \n", "\n", "Lets quickly go through what vectorization of `DSI` looks like, then demo how to run the external manager with `pestpp-ies`." ] @@ -408,7 +408,7 @@ "id": "258084e4", "metadata": {}, "source": [ - "First, genera te a dummy ensemble of DSi parameter values:" + "First, generate a dummy ensemble of DSi parameter values:" ] }, { @@ -480,7 +480,7 @@ "id": "6a01ae4a", "metadata": {}, "source": [ - "key detail now, spceify the `use_runstor=True` argument to setup the forward run using the external manager:" + "key detail now, specify the `use_runstor=True` argument to setup the forward run using the external manager:" ] }, { @@ -541,7 +541,7 @@ "metadata": {}, "source": [ "And we are good to go...sheesh that was hard.\n", - "Lets just run for 2 iterations. Why? Because DSI will usualy get a good fit pretty fast. Feel free to experiment with more or less if you like.\n", + "Lets just run for 2 iterations. Why? Because DSI will usually get a good fit pretty fast. Feel free to experiment with more or less if you like.\n", "\n", "Let's go a bit higher with the number of DSi reals that we had for the training data set:" ] @@ -614,7 +614,7 @@ "id": "cae57f5b", "metadata": {}, "source": [ - "Now we will run `pestpp-ies` with the external run manager option. We do this by calling `pestpp-ies [controlfile].pst /e`. The `/e` trigegrs the external run manager option.\n", + "Now we will run `pestpp-ies` with the external run manager option. We do this by calling `pestpp-ies [controlfile].pst /e`. The `/e` triggers the external run manager option.\n", "\n", "Note that this run is in \"serial\" as far as `pestpp` is concerned. We are handling the paralelization of the dsi forward runs.\n", "\n", @@ -937,11 +937,19 @@ "ax.axvline(obs.loc[target_col].obsval, color='r')\n", "fig.tight_layout();" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c51f949-4316-4a8e-af41-e0061037b6d6", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "gwe", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, diff --git a/04_dsiae.ipynb b/04_dsiae.ipynb index a77f321..f7d2d12 100644 --- a/04_dsiae.ipynb +++ b/04_dsiae.ipynb @@ -93,9 +93,9 @@ "\n", "## what we are going to do\n", "\n", - "In this noteook we are going to throught the mechanics of using the `pyemu.DSIAE` class and some of the caveats. This is a vanilla AE (think non-linear PCA), without any fancy LSTM or convolutional layers.\n", + "In this notebook we are going to through the mechanics of using the `pyemu.DSIAE` class and some of the caveats. This is a vanilla AE (think non-linear PCA), without any fancy LSTM or convolutional layers.\n", "\n", - "THe first section mirrors the same steps as in the DSI notebook.\n" + "The first section mirrors the same steps as in the DSI notebook.\n" ] }, { @@ -139,7 +139,7 @@ "source": [ "## Choose the truth\n", "\n", - "Lets choose one of the realizations as the rtuth, then remove if form the training data. We are going to select an inconvenient truth: one in which the max temperature plume extends a bit farther than most." + "Lets choose one of the realizations as the truth, then remove if form the training data. We are going to select an inconvenient truth: one in which the max temperature plume extends a bit farther than most." ] }, { @@ -388,7 +388,7 @@ "source": [ "# Start DSI-AE\n", "\n", - "For DSI, we dont need all the observations we have been tracking. We only really need the non-zero weighted obs and the prediction sof interest. " + "For DSI, we dont need all the observations we have been tracking. We only really need the non-zero weighted obs and the predictions of interest. " ] }, { @@ -410,7 +410,7 @@ "id": "534a3b6e", "metadata": {}, "source": [ - "Lets use normal score transformation (usualy a good choice..)" + "Lets use normal score transformation (usually a good choice..)" ] }, { @@ -454,12 +454,12 @@ "metadata": {}, "source": [ "## Training the AE\n", - "Here is where things start to differ. For `DSI`, \"fitting\" the model just meant doing SVD. For `DSIAE` it means training the neural net to encode/decode the data set. There is a bit more user input and options for hyperparameter tunning available.\n", + "Here is where things start to differ. For `DSI`, \"fitting\" the model just meant doing SVD. For `DSIAE` it means training the neural net to encode/decode the data set. There is a bit more user input and options for hyperparameter tuning available.\n", "\n", "### Machine learning dependency\n", "The `DSIAE` class uses `tensorflow` in the background. `Tensorflow` is an open-source machine learning framework developed by Google, widely used for building and training neural networks, including autoencoders. It provides flexible tools for both research and production, supporting CPUs, GPUs, and TPUs for scalable computation. While powerful, `tensorflow`s performance and installation can vary across operating systems—GPU acceleration, for instance, often requires careful setup of compatible CUDA and cuDNN libraries on Windows or Linux. Additionally, version compatibility between `tensorflow`, Python, and hardware drivers can sometimes pose challenges in maintaining stable environments.\n", "\n", - "It is not our intention here to provide traning on machine learning (there are many btter resources out there to lear from...). We assume the reader has some knowledge on machine learning, and the use of tensorflow. Here are some basic insghts:\n", + "It is not our intention here to provide training on machine learning (there are many better resources out there to lear from...). We assume the reader has some knowledge on machine learning, and the use of tensorflow. Here are some basic insights:\n", "\n", "* **validation_split=0.1** – Reserves 10% of the data for validation, helping monitor overfitting during training. Typical values range from 0.1 to 0.2; larger splits may be used when data are abundant.\n", "* **hidden_dims=(128, 64)** – Defines the number and size of hidden layers in the autoencoder. More layers or larger dimensions increase model capacity but may cause overfitting; smaller architectures are preferable for simpler data.\n", @@ -493,7 +493,7 @@ "id": "30acb4d0", "metadata": {}, "source": [ - "If you wish, pyemu has a built in hyperparameetr tuner (again, just using tensorflow in the background) that undertakes a grid serach to do hyperparameter tunning. Uncomment the code below if you wish to exeriment. It can take a few minutes..." + "If you wish, pyemu has a built in hyperparametr tuner (again, just using tensorflow in the background) that undertakes a grid search to do hyperparameter tuning. Uncomment the code below if you wish to experiment. It can take a few minutes..." ] }, { @@ -521,7 +521,7 @@ "source": [ "## Encoding/Decoding\n", "\n", - "A convenient aspect of DSI-AE versus vanilla DSI, is that we can directly project/encode the physics-based model outputs directly to latent space and back again. This allows us to estimate the \"error\" we incurr from simplification across data space. \n", + "A convenient aspect of DSI-AE versus vanilla DSI, is that we can directly project/encode the physics-based model outputs directly to latent space and back again. This allows us to estimate the \"error\" we incur from simplification across data space. \n", "\n", "Here is how we do it:" ] @@ -560,7 +560,7 @@ "id": "6e9abcb6", "metadata": {}, "source": [ - "We can project these back to data space by \"predciting\" (aka decoding) with the dsiae object:" + "We can project these back to data space by \"predicting\" (aka decoding) with the dsiae object:" ] }, { @@ -616,7 +616,7 @@ "source": [ "And setup the DSI pest dir...\n", "\n", - "key detail now, spceify the `use_runstor=True` argument to setup the forward run using the external manager:" + "key detail now, specify the `use_runstor=True` argument to setup the forward run using the external manager:" ] }, { @@ -660,7 +660,7 @@ "source": [ "## AE latent space prior\n", "\n", - "In vanilla DSI, the latent space prior is straightforward: Guasiian normal distributions with mean 0 and stdv 1 for all latent space parameters.\n", + "In vanilla DSI, the latent space prior is straightforward: Gaussian normal distributions with mean 0 and stdv 1 for all latent space parameters.\n", "\n", "In DSI-AE things get a bit more complicated. Autoencoder-based approaches learn a nonlinear latent space, and the resulting latent parameter distribution is generally non-Gaussian and data-driven. This flexibility allows autoencoders to capture more complex, multimodal relationships in the data, though it can make interpretation and sampling from the latent space less straightforward than in PCA-based methods.\n", "\n", @@ -686,11 +686,11 @@ "id": "357759c2", "metadata": {}, "source": [ - "If we are happy using the same number of realizations with DSIAE as we had realizations in the training data set, then we are fine. We can explicilty pass the encoded FOM prior values as the dsiae-prior parameter ensemble to pestpp-ies.\n", + "If we are happy using the same number of realizations with DSIAE as we had realizations in the training data set, then we are fine. We can explicitly pass the encoded FOM prior values as the dsiae-prior parameter ensemble to pestpp-ies.\n", "\n", - "If we want to use a larger ensemble (usualy good ideia if we can aford it..), then we need to do a bit more work to empirically sample the dsi-ae latent parameter prior distribution...\n", + "If we want to use a larger ensemble (usually good idia if we can aford it..), then we need to do a bit more work to empirically sample the dsi-ae latent parameter prior distribution...\n", "\n", - "Lets do that now, using some tricks built into pyemu. First, load the latent space prior as a `ParameterEnsemble` object. This was pre-preared when you called `.prepare_pespp()`" + "Lets do that now, using some tricks built into pyemu. First, load the latent space prior as a `ParameterEnsemble` object. This was pre-prepared when you called `.prepare_pespp()`" ] }, { @@ -760,7 +760,7 @@ "id": "4f87536c", "metadata": {}, "source": [ - "Lets comapre the distirbution of one of the parameters to see what this looks like:" + "Lets compare the distribution of one of the parameters to see what this looks like:" ] }, { @@ -808,7 +808,7 @@ "id": "633da096", "metadata": {}, "source": [ - "And write the extended parametr ensemble back to the pest dir, whislt also updateing the pespp options." + "And write the extended parameter ensemble back to the pest dir, whilst also updating the pestpp options." ] }, { @@ -830,7 +830,7 @@ "source": [ "And we are good to go...sheesh that was hard.\n", "\n", - "Lets run for 3 iterations. WThis is more that we diod for DSI. Why? Becasue the relationship is not as linear as for DSI, getting a good fit is more chalenging." + "Lets run for 3 iterations. This is more that we did for DSI. Why? Because the relationship is not as linear as for DSI, getting a good fit is more challenging." ] }, { @@ -913,9 +913,9 @@ "id": "cae57f5b", "metadata": {}, "source": [ - "Now we will run `pestpp-ies` with the external run manager option. We do this by calling `pestpp-ies [controlfile].pst /e`. The `/e` trigegrs the external run manager option.\n", + "Now we will run `pestpp-ies` with the external run manager option. We do this by calling `pestpp-ies [controlfile].pst /e`. The `/e` triggers the external run manager option.\n", "\n", - "Note that this run is in \"serial\" as far as `pestpp` is concerned. We are handling the paralelization of the dsi forward runs.\n", + "Note that this run is in \"serial\" as far as `pestpp` is concerned. We are handling the parallelization of the dsi forward runs.\n", "\n", "Here we go!" ] @@ -953,7 +953,7 @@ "id": "af837509", "metadata": {}, "source": [ - "See how it take slonger to get better fits?" + "See how it takes longer to get better fits?" ] }, { @@ -1248,7 +1248,7 @@ ], "metadata": { "kernelspec": { - "display_name": "gwe", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, diff --git a/model/mfsim.lst b/model/mfsim.lst deleted file mode 100644 index 6357846..0000000 --- a/model/mfsim.lst +++ /dev/null @@ -1,3088 +0,0 @@ - MODFLOW 6 - U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL - VERSION 6.7.0.dev3 (preliminary) 10/16/2025 - ***DEVELOP MODE*** - - MODFLOW 6 compiled Oct 16 2025 14:05:53 with Intel(R) Fortran Intel(R) 64 - Compiler Classic for applications running on Intel(R) 64, Version 2021.7.0 - Build 20220726_000000 - -This software is preliminary or provisional and is subject to -revision. It is being provided to meet the need for timely best -science. The software has not received final approval by the U.S. -Geological Survey (USGS). No warranty, expressed or implied, is made -by the USGS or the U.S. Government as to the functionality of the -software and related material nor shall the fact of release -constitute any such warranty. The software is provided on the -condition that neither the USGS nor the U.S. Government shall be held -liable for any damages resulting from the authorized or unauthorized -use of the software. - - -As a work of the United States Government, this USGS product is -in the public domain within the United States. You can copy, -modify, distribute, and perform the work, even for commercial -purposes, all without asking permission. Additionally, USGS -waives copyright and related rights in the work worldwide -through CC0 1.0 Universal Public Domain Dedication -(https://creativecommons.org/publicdomain/zero/1.0/). - -The following GNU Lesser General Public License (LGPL) libraries -are used in this USGS product: - - SPARSKIT version 2.0 - ilut, luson, and qsplit - (https://www-users.cse.umn.edu/~saad/software/SPARSKIT/) - - RCM - Reverse Cuthill McKee Ordering - (https://people.math.sc.edu/Burkardt/f_src/rcm/rcm.html) - - BLAS - Basic Linear Algebra Subprograms Level 1 - (https://people.math.sc.edu/Burkardt/f_src/blas1_d/blas1_d.html) - - SPARSEKIT - Sparse Matrix Utility Package - amux, dperm, dvperm, rperm, and cperm - (https://people.sc.fsu.edu/~jburkardt/f77_src/sparsekit/sparsekit.html) - -The following BSD-3 License libraries are used in this USGS product: - - Modern Fortran DAG Library - Copyright (c) 2018, Jacob Williams - All rights reserved. - (https://github.com/jacobwilliams/daglib) - -MODFLOW 6 compiler options: -Isrc/libmf6_external.a.p -Isrc/libmf6core.a.p --Isrc -I../src -warn general -warn truncated_source -stand=f18 -O2 -fpe0 --no-heap-arrays -traceback -diag-disable:7416 -diag-disable:7025 --diag-disable:5268 -module src/libmf6core.a.p --gen-dep=src/libmf6core.a.p/Utilities_compilerversion.F90.o --gen-depformat=make -o src/libmf6core.a.p/Utilities_compilerversion.F90.o -c - -System command used to initiate simulation: -./mf6 - -MODFLOW was compiled using uniform precision. - -Real Variables - KIND: 8 - TINY (smallest non-zero value): 2.225074-308 - HUGE (largest value): 1.797693+308 - PRECISION: 15 - SIZE IN BITS: 64 - -Integer Variables - KIND: 4 - HUGE (largest value): 2147483647 - SIZE IN BITS: 32 - -Long Integer Variables - KIND: 8 - HUGE (largest value): 9223372036854775807 - SIZE IN BITS: 64 - -Logical Variables - KIND: 4 - SIZE IN BITS: 32 - - - OPENED mfsim.nam - FILE TYPE:NAM6 UNIT 1001 STATUS:OLD - FORMAT:FORMATTED ACCESS:SEQUENTIAL - ACTION:READ - - # File generated by Flopy version 3.10.0.dev5 on 10/29/2025 at 13:54:46. - - OPENED mfsim.tdis - FILE TYPE:TDIS6 UNIT 1002 STATUS:OLD - FORMAT:FORMATTED ACCESS:SEQUENTIAL - ACTION:READ - - # File generated by Flopy version 3.10.0.dev5 on 10/29/2025 at 13:54:46. - - OPENED gwf.nam - FILE TYPE:GWF6 UNIT 1003 STATUS:OLD - FORMAT:FORMATTED ACCESS:SEQUENTIAL - ACTION:READ - - # File generated by Flopy version 3.10.0.dev5 on 10/29/2025 at 13:54:46. - - OPENED ./gwf.disv - FILE TYPE:DISV6 UNIT 1004 STATUS:OLD - FORMAT:FORMATTED ACCESS:SEQUENTIAL - ACTION:READ - - # File generated by Flopy version 3.10.0.dev5 on 10/29/2025 at 13:54:47. - - OPENED gwf.disv_vertices.txt - FILE TYPE:OPEN/CLOSE UNIT 1007 STATUS:OLD - FORMAT:FORMATTED ACCESS:SEQUENTIAL - ACTION:READ - - - OPENED gwf.disv_cell2d.txt - FILE TYPE:OPEN/CLOSE UNIT 1008 STATUS:OLD - FORMAT:FORMATTED ACCESS:SEQUENTIAL - ACTION:READ - - - OPENED ./gwf.npf - FILE TYPE:NPF6 UNIT 1009 STATUS:OLD - FORMAT:FORMATTED ACCESS:SEQUENTIAL - ACTION:READ - - # File generated by Flopy version 3.10.0.dev5 on 10/29/2025 at 17:56:37. - - OPENED ./gwf.ic - FILE TYPE:IC6 UNIT 1012 STATUS:OLD - FORMAT:FORMATTED ACCESS:SEQUENTIAL - ACTION:READ - - # File generated by Flopy version 3.10.0.dev5 on 10/29/2025 at 13:54:47. - - OPENED ./gwf.oc - FILE TYPE:OC6 UNIT 1014 STATUS:OLD - FORMAT:FORMATTED ACCESS:SEQUENTIAL - ACTION:READ - - - OPENED ./gwf.obs - FILE TYPE:OBS6 UNIT 1015 STATUS:OLD - FORMAT:FORMATTED ACCESS:SEQUENTIAL - ACTION:READ - - - OPENED ./gwf.inj.wel - FILE TYPE:WEL6 UNIT 1017 STATUS:OLD - FORMAT:FORMATTED ACCESS:SEQUENTIAL - ACTION:READ - - # File generated by Flopy version 3.10.0.dev5 on 10/29/2025 at 13:54:47. - AUXILIARY VARIABLE: TEMPERATURE - - OPENED ./gwf.ext.wel - FILE TYPE:WEL6 UNIT 1019 STATUS:OLD - FORMAT:FORMATTED ACCESS:SEQUENTIAL - ACTION:READ - - # File generated by Flopy version 3.10.0.dev5 on 10/29/2025 at 13:54:47. - AUXILIARY VARIABLE: TEMPERATURE - - OPENED ./gwf.riv - FILE TYPE:RIV6 UNIT 1021 STATUS:OLD - FORMAT:FORMATTED ACCESS:SEQUENTIAL - ACTION:READ - - AUXILIARY VARIABLE: TEMPERATURE - - OPENED ./gwf.rch - FILE TYPE:RCH6 UNIT 1023 STATUS:OLD - FORMAT:FORMATTED ACCESS:SEQUENTIAL - ACTION:READ - - # File generated by Flopy version 3.10.0.dev5 on 10/29/2025 at 13:54:47. - AUXILIARY VARIABLE: TEMPERATURE - - OPENED ./gwf.chd - FILE TYPE:CHD6 UNIT 1025 STATUS:OLD - FORMAT:FORMATTED ACCESS:SEQUENTIAL - ACTION:READ - - # File generated by Flopy version 3.10.0.dev5 on 10/29/2025 at 13:54:47. - AUXILIARY VARIABLE: TEMPERATURE - - OPENED gwe.nam - FILE TYPE:GWE6 UNIT 1026 STATUS:OLD - FORMAT:FORMATTED ACCESS:SEQUENTIAL - ACTION:READ - - # File generated by Flopy version 3.10.0.dev5 on 10/29/2025 at 13:54:47. - # FMI6 gwe.fmi fmi - - OPENED ./gwe.disv - FILE TYPE:DISV6 UNIT 1027 STATUS:OLD - FORMAT:FORMATTED ACCESS:SEQUENTIAL - ACTION:READ - - # File generated by Flopy version 3.10.0.dev5 on 10/29/2025 at 13:54:48. - - OPENED gwe.disv_vertices.txt - FILE TYPE:OPEN/CLOSE UNIT 1030 STATUS:OLD - FORMAT:FORMATTED ACCESS:SEQUENTIAL - ACTION:READ - - - OPENED gwe.disv_cell2d.txt - FILE TYPE:OPEN/CLOSE UNIT 1031 STATUS:OLD - FORMAT:FORMATTED ACCESS:SEQUENTIAL - ACTION:READ - - - OPENED ./gwe.ic - FILE TYPE:IC6 UNIT 1032 STATUS:OLD - FORMAT:FORMATTED ACCESS:SEQUENTIAL - ACTION:READ - - # File generated by Flopy version 3.10.0.dev5 on 10/29/2025 at 13:54:48. - - OPENED ./gwe.est - FILE TYPE:EST6 UNIT 1034 STATUS:OLD - FORMAT:FORMATTED ACCESS:SEQUENTIAL - ACTION:READ - - # File generated by Flopy version 3.10.0.dev5 on 10/29/2025 at 13:54:48. - - OPENED ./gwe.adv - FILE TYPE:ADV6 UNIT 1038 STATUS:OLD - FORMAT:FORMATTED ACCESS:SEQUENTIAL - ACTION:READ - - # File generated by Flopy version 3.10.0.dev5 on 10/29/2025 at 13:54:48. - - OPENED ./gwe.cnd - FILE TYPE:CND6 UNIT 1039 STATUS:OLD - FORMAT:FORMATTED ACCESS:SEQUENTIAL - ACTION:READ - - # File generated by Flopy version 3.10.0.dev5 on 10/29/2025 at 13:54:48. - - OPENED ./gwe.ssm - FILE TYPE:SSM6 UNIT 1044 STATUS:OLD - FORMAT:FORMATTED ACCESS:SEQUENTIAL - ACTION:READ - - # File generated by Flopy version 3.10.0.dev5 on 10/29/2025 at 13:54:48. - - OPENED gwe.ssm_sources.txt - FILE TYPE:OPEN/CLOSE UNIT 1045 STATUS:OLD - FORMAT:FORMATTED ACCESS:SEQUENTIAL - ACTION:READ - - - OPENED gwe.ssm_fileinput.txt - FILE TYPE:OPEN/CLOSE UNIT 1046 STATUS:OLD - FORMAT:FORMATTED ACCESS:SEQUENTIAL - ACTION:READ - - - OPENED ./gwe.oc - FILE TYPE:OC6 UNIT 1047 STATUS:OLD - FORMAT:FORMATTED ACCESS:SEQUENTIAL - ACTION:READ - - - OPENED ./gwe.obs - FILE TYPE:OBS6 UNIT 1048 STATUS:OLD - FORMAT:FORMATTED ACCESS:SEQUENTIAL - ACTION:READ - - - OPENED mfsim.gwfgwe - FILE TYPE:GWF6-GWE6 UNIT 1049 STATUS:OLD - FORMAT:FORMATTED ACCESS:SEQUENTIAL - ACTION:READ - - # File generated by Flopy version 3.10.0.dev5 on 10/29/2025 at 13:54:46. - - READING SIMULATION OPTIONS - MAXIMUM NUMBER OF ERRORS THAT WILL BE STORED IS 1000 - END OF SIMULATION OPTIONS - - READING SIMULATION TIMING - - TDIS -- TEMPORAL DISCRETIZATION PACKAGE, - VERSION 1 : 11/13/2014 - INPUT READ FROM MEMPATH: __INPUT__/SIM/TDIS - PROCESSING TDIS OPTIONS - SIMULATION TIME UNIT IS SECONDS - END OF TDIS OPTIONS - PROCESSING TDIS DIMENSIONS - 61 STRESS PERIOD(S) IN SIMULATION - END OF TDIS DIMENSIONS - PROCESSING TDIS PERIODDATA - - - STRESS PERIOD LENGTH TIME STEPS MULTIPLIER FOR DELT - ---------------------------------------------------------------------------- - 1 1.000000 1 1.000 - 2 1.8408600E+07 14 1.000 - 3 1.3149000E+07 10 1.000 - 4 1.8408600E+07 14 1.000 - 5 1.3149000E+07 10 1.000 - 6 1.8408600E+07 14 1.000 - 7 1.3149000E+07 10 1.000 - 8 1.8408600E+07 14 1.000 - 9 1.3149000E+07 10 1.000 - 10 1.8408600E+07 14 1.000 - 11 1.3149000E+07 10 1.000 - 12 1.8408600E+07 14 1.000 - 13 1.3149000E+07 10 1.000 - 14 1.8408600E+07 14 1.000 - 15 1.3149000E+07 10 1.000 - 16 1.8408600E+07 14 1.000 - 17 1.3149000E+07 10 1.000 - 18 1.8408600E+07 14 1.000 - 19 1.3149000E+07 10 1.000 - 20 1.8408600E+07 14 1.000 - 21 1.3149000E+07 10 1.000 - 22 1.8408600E+07 14 1.000 - 23 1.3149000E+07 10 1.000 - 24 1.8408600E+07 14 1.000 - 25 1.3149000E+07 10 1.000 - 26 1.8408600E+07 14 1.000 - 27 1.3149000E+07 10 1.000 - 28 1.8408600E+07 14 1.000 - 29 1.3149000E+07 10 1.000 - 30 1.8408600E+07 14 1.000 - 31 1.3149000E+07 10 1.000 - 32 1.8408600E+07 14 1.000 - 33 1.3149000E+07 10 1.000 - 34 1.8408600E+07 14 1.000 - 35 1.3149000E+07 10 1.000 - 36 1.8408600E+07 14 1.000 - 37 1.3149000E+07 10 1.000 - 38 1.8408600E+07 14 1.000 - 39 1.3149000E+07 10 1.000 - 40 1.8408600E+07 14 1.000 - 41 1.3149000E+07 10 1.000 - 42 1.8408600E+07 14 1.000 - 43 1.3149000E+07 10 1.000 - 44 1.8408600E+07 14 1.000 - 45 1.3149000E+07 10 1.000 - 46 1.8408600E+07 14 1.000 - 47 1.3149000E+07 10 1.000 - 48 1.8408600E+07 14 1.000 - 49 1.3149000E+07 10 1.000 - 50 1.8408600E+07 14 1.000 - 51 1.3149000E+07 10 1.000 - 52 1.8408600E+07 14 1.000 - 53 1.3149000E+07 10 1.000 - 54 1.8408600E+07 14 1.000 - 55 1.3149000E+07 10 1.000 - 56 1.8408600E+07 14 1.000 - 57 1.3149000E+07 10 1.000 - 58 1.8408600E+07 14 1.000 - 59 1.3149000E+07 10 1.000 - 60 1.8408600E+07 14 1.000 - 61 1.3149000E+07 10 1.000 - END OF TDIS PERIODDATA - END OF SIMULATION TIMING - - READING SIMULATION MODELS - GWF6 model 1 will be created - GWE6 model 2 will be created - END OF SIMULATION MODELS - - READING SIMULATION EXCHANGES - GWF6-GWE6 exchange 1 will be created to connect model 1 with model 2 - END OF SIMULATION EXCHANGES - - READING SOLUTIONGROUP - - Creating solution: SLN_1 - - OPENED gwf.ims - FILE TYPE:IMS UNIT 1052 STATUS:OLD - FORMAT:FORMATTED ACCESS:SEQUENTIAL - ACTION:READ - - - Creating solution: SLN_2 - - OPENED gwe.ims - FILE TYPE:IMS UNIT 1053 STATUS:OLD - FORMAT:FORMATTED ACCESS:SEQUENTIAL - ACTION:READ - - END OF SOLUTIONGROUP - -PROCESSING MODEL CONNECTIONS -END OF MODEL CONNECTIONS - - OPENED gwf.inj.wel_stress_period_data_2.txt - FILE TYPE:OPEN/CLOSE UNIT 1055 STATUS:OLD - FORMAT:FORMATTED ACCESS:SEQUENTIAL - ACTION:READ - - - OPENED gwf.ext.wel_stress_period_data_2.txt - FILE TYPE:OPEN/CLOSE UNIT 1056 STATUS:OLD - FORMAT:FORMATTED ACCESS:SEQUENTIAL - ACTION:READ - - - OPENED gwf.riv_stress_period_data_1.txt - FILE TYPE:OPEN/CLOSE UNIT 1057 STATUS:OLD - FORMAT:FORMATTED ACCESS:SEQUENTIAL - ACTION:READ - - - OPENED gwf.chd_stress_period_data_1.txt - FILE TYPE:OPEN/CLOSE UNIT 1058 STATUS:OLD - FORMAT:FORMATTED ACCESS:SEQUENTIAL - ACTION:READ - - - IMS -- ITERATIVE MODEL SOLUTION PACKAGE, VERSION 6, 4/28/2017 - INPUT READ FROM UNIT 1052 - # File generated by Flopy version 3.10.0.dev5 on 10/29/2025 at 13:54:46. - - PROCESSING IMS OPTIONS - MODERATE OPTION: - DEFAULT SOLVER INPUT VALUES REFLECT MODERATELY NONLINEAR MODEL - END OF IMS OPTIONS - - PROCESSING IMS NONLINEAR - END OF IMS NONLINEAR DATA - **UNDER-RELAXATION WILL BE USED*** - - - PROCESSING LINEAR DATA - END OF LINEAR DATA - ***IMS LINEAR SOLVER WILL BE USED*** - - IMSLINEAR -- UNSTRUCTURED LINEAR SOLUTION PACKAGE, VERSION 8, 04/28/2017 - A symmetric matrix will be solved - - OUTER ITERATION CONVERGENCE CRITERION (DVCLOSE) = 0.100000E-03 - MAXIMUM NUMBER OF OUTER ITERATIONS (MXITER) = 200 - SOLVER PRINTOUT INDEX (IPRIMS) = 0 - NONLINEAR ITERATION METHOD (NONLINMETH) = 3 - LINEAR SOLUTION METHOD (LINMETH) = 1 - UNDER-RELAXATION WEIGHT REDUCTION FACTOR (THETA) = 0.900000E+00 - UNDER-RELAXATION WEIGHT INCREASE INCREMENT (KAPPA) = 0.100000E-03 - UNDER-RELAXATION PREVIOUS HISTORY FACTOR (GAMMA) = 0.000000E+00 - UNDER-RELAXATION MOMENTUM TERM (AMOMENTUM) = 0.000000E+00 - - SOLUTION BY THE CONJUGATE-GRADIENT METHOD - ------------------------------------------------------------------ - MAXIMUM OF 200 CALLS OF SOLUTION ROUTINE - MAXIMUM OF 50 INTERNAL ITERATIONS PER CALL TO SOLUTION ROUTINE - LINEAR ACCELERATION METHOD = CG - MATRIX PRECONDITIONING TYPE = MOD. INCOMPLETE LU - MATRIX SCALING APPROACH = NO SCALING - MATRIX REORDERING APPROACH = ORIGINAL ORDERING - NUMBER OF ORTHOGONALIZATIONS = 0 - HEAD CHANGE CRITERION FOR CLOSURE = 0.10000E-03 - RESIDUAL CHANGE CRITERION FOR CLOSURE = 0.10000E-01 - RESIDUAL CONVERGENCE OPTION = 0 - RESIDUAL CONVERGENCE NORM = INFINITY NORM - RELAXATION FACTOR = 0.97000E+00 - - - - - IMS -- ITERATIVE MODEL SOLUTION PACKAGE, VERSION 6, 4/28/2017 - INPUT READ FROM UNIT 1053 - # File generated by Flopy version 3.10.0.dev5 on 10/29/2025 at 13:54:46. - - PROCESSING IMS OPTIONS - MODERATE OPTION: - DEFAULT SOLVER INPUT VALUES REFLECT MODERATELY NONLINEAR MODEL - END OF IMS OPTIONS - - PROCESSING IMS NONLINEAR - END OF IMS NONLINEAR DATA - **UNDER-RELAXATION WILL BE USED*** - - - PROCESSING LINEAR DATA - END OF LINEAR DATA - ***IMS LINEAR SOLVER WILL BE USED*** - - IMSLINEAR -- UNSTRUCTURED LINEAR SOLUTION PACKAGE, VERSION 8, 04/28/2017 - An asymmetric matrix will be solved - - OUTER ITERATION CONVERGENCE CRITERION (DVCLOSE) = 0.100000E-05 - MAXIMUM NUMBER OF OUTER ITERATIONS (MXITER) = 1000 - SOLVER PRINTOUT INDEX (IPRIMS) = 0 - NONLINEAR ITERATION METHOD (NONLINMETH) = 3 - LINEAR SOLUTION METHOD (LINMETH) = 1 - UNDER-RELAXATION WEIGHT REDUCTION FACTOR (THETA) = 0.700000E+00 - UNDER-RELAXATION WEIGHT INCREASE INCREMENT (KAPPA) = 0.800000E-01 - UNDER-RELAXATION PREVIOUS HISTORY FACTOR (GAMMA) = 0.500000E-01 - UNDER-RELAXATION MOMENTUM TERM (AMOMENTUM) = 0.000000E+00 - MAXIMUM NUMBER OF BACKTRACKS (NUMTRACK) = 20 - BACKTRACKING TOLERANCE FACTOR (BTOL) = 0.200000E+01 - BACKTRACKING REDUCTION FACTOR (BREDUC) = 0.200000E+00 - BACKTRACKING RESIDUAL LIMIT (RES_LIM) = 0.500000E-03 - - SOLUTION BY THE BICONJUGATE-GRADIENT STABILIZED METHOD - ------------------------------------------------------------------ - MAXIMUM OF 1000 CALLS OF SOLUTION ROUTINE - MAXIMUM OF 100 INTERNAL ITERATIONS PER CALL TO SOLUTION ROUTINE - LINEAR ACCELERATION METHOD = BCGS - MATRIX PRECONDITIONING TYPE = INCOMPLETE LUT - MATRIX SCALING APPROACH = NO SCALING - MATRIX REORDERING APPROACH = ORIGINAL ORDERING - NUMBER OF ORTHOGONALIZATIONS = 2 - HEAD CHANGE CRITERION FOR CLOSURE = 0.10000E-05 - RESIDUAL CHANGE CRITERION FOR CLOSURE = 0.10000E-05 - RESIDUAL CONVERGENCE OPTION = 1 - RESIDUAL CONVERGENCE NORM = INFINITY NORM S - RELAXATION FACTOR = 0.00000E+00 - NUMBER OF LEVELS = 8 - DROP TOLERANCE = 0.10000E-02 - - - - Solving: Stress period: 1 Time step: 1 - -1 - STRESS PERIOD NO. 1, LENGTH = 1.000000 - ------------------------------------------ - NUMBER OF TIME STEPS = 1 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1.000000 - - Solving: Stress period: 2 Time step: 1 - -1 - STRESS PERIOD NO. 2, LENGTH = 0.1840860E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 14 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 2 Time step: 2 - - - Solving: Stress period: 2 Time step: 3 - - - Solving: Stress period: 2 Time step: 4 - - - Solving: Stress period: 2 Time step: 5 - - - Solving: Stress period: 2 Time step: 6 - - - Solving: Stress period: 2 Time step: 7 - - - Solving: Stress period: 2 Time step: 8 - - - Solving: Stress period: 2 Time step: 9 - - - Solving: Stress period: 2 Time step: 10 - - - Solving: Stress period: 2 Time step: 11 - - - Solving: Stress period: 2 Time step: 12 - - - Solving: Stress period: 2 Time step: 13 - - - Solving: Stress period: 2 Time step: 14 - - - Solving: Stress period: 3 Time step: 1 - -1 - STRESS PERIOD NO. 3, LENGTH = 0.1314900E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 10 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 3 Time step: 2 - - - Solving: Stress period: 3 Time step: 3 - - - Solving: Stress period: 3 Time step: 4 - - - Solving: Stress period: 3 Time step: 5 - - - Solving: Stress period: 3 Time step: 6 - - - Solving: Stress period: 3 Time step: 7 - - - Solving: Stress period: 3 Time step: 8 - - - Solving: Stress period: 3 Time step: 9 - - - Solving: Stress period: 3 Time step: 10 - - - Solving: Stress period: 4 Time step: 1 - -1 - STRESS PERIOD NO. 4, LENGTH = 0.1840860E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 14 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 4 Time step: 2 - - - Solving: Stress period: 4 Time step: 3 - - - Solving: Stress period: 4 Time step: 4 - - - Solving: Stress period: 4 Time step: 5 - - - Solving: Stress period: 4 Time step: 6 - - - Solving: Stress period: 4 Time step: 7 - - - Solving: Stress period: 4 Time step: 8 - - - Solving: Stress period: 4 Time step: 9 - - - Solving: Stress period: 4 Time step: 10 - - - Solving: Stress period: 4 Time step: 11 - - - Solving: Stress period: 4 Time step: 12 - - - Solving: Stress period: 4 Time step: 13 - - - Solving: Stress period: 4 Time step: 14 - - - Solving: Stress period: 5 Time step: 1 - -1 - STRESS PERIOD NO. 5, LENGTH = 0.1314900E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 10 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 5 Time step: 2 - - - Solving: Stress period: 5 Time step: 3 - - - Solving: Stress period: 5 Time step: 4 - - - Solving: Stress period: 5 Time step: 5 - - - Solving: Stress period: 5 Time step: 6 - - - Solving: Stress period: 5 Time step: 7 - - - Solving: Stress period: 5 Time step: 8 - - - Solving: Stress period: 5 Time step: 9 - - - Solving: Stress period: 5 Time step: 10 - - - Solving: Stress period: 6 Time step: 1 - -1 - STRESS PERIOD NO. 6, LENGTH = 0.1840860E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 14 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 6 Time step: 2 - - - Solving: Stress period: 6 Time step: 3 - - - Solving: Stress period: 6 Time step: 4 - - - Solving: Stress period: 6 Time step: 5 - - - Solving: Stress period: 6 Time step: 6 - - - Solving: Stress period: 6 Time step: 7 - - - Solving: Stress period: 6 Time step: 8 - - - Solving: Stress period: 6 Time step: 9 - - - Solving: Stress period: 6 Time step: 10 - - - Solving: Stress period: 6 Time step: 11 - - - Solving: Stress period: 6 Time step: 12 - - - Solving: Stress period: 6 Time step: 13 - - - Solving: Stress period: 6 Time step: 14 - - - Solving: Stress period: 7 Time step: 1 - -1 - STRESS PERIOD NO. 7, LENGTH = 0.1314900E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 10 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 7 Time step: 2 - - - Solving: Stress period: 7 Time step: 3 - - - Solving: Stress period: 7 Time step: 4 - - - Solving: Stress period: 7 Time step: 5 - - - Solving: Stress period: 7 Time step: 6 - - - Solving: Stress period: 7 Time step: 7 - - - Solving: Stress period: 7 Time step: 8 - - - Solving: Stress period: 7 Time step: 9 - - - Solving: Stress period: 7 Time step: 10 - - - Solving: Stress period: 8 Time step: 1 - -1 - STRESS PERIOD NO. 8, LENGTH = 0.1840860E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 14 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 8 Time step: 2 - - - Solving: Stress period: 8 Time step: 3 - - - Solving: Stress period: 8 Time step: 4 - - - Solving: Stress period: 8 Time step: 5 - - - Solving: Stress period: 8 Time step: 6 - - - Solving: Stress period: 8 Time step: 7 - - - Solving: Stress period: 8 Time step: 8 - - - Solving: Stress period: 8 Time step: 9 - - - Solving: Stress period: 8 Time step: 10 - - - Solving: Stress period: 8 Time step: 11 - - - Solving: Stress period: 8 Time step: 12 - - - Solving: Stress period: 8 Time step: 13 - - - Solving: Stress period: 8 Time step: 14 - - - Solving: Stress period: 9 Time step: 1 - -1 - STRESS PERIOD NO. 9, LENGTH = 0.1314900E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 10 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 9 Time step: 2 - - - Solving: Stress period: 9 Time step: 3 - - - Solving: Stress period: 9 Time step: 4 - - - Solving: Stress period: 9 Time step: 5 - - - Solving: Stress period: 9 Time step: 6 - - - Solving: Stress period: 9 Time step: 7 - - - Solving: Stress period: 9 Time step: 8 - - - Solving: Stress period: 9 Time step: 9 - - - Solving: Stress period: 9 Time step: 10 - - - Solving: Stress period: 10 Time step: 1 - -1 - STRESS PERIOD NO. 10, LENGTH = 0.1840860E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 14 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 10 Time step: 2 - - - Solving: Stress period: 10 Time step: 3 - - - Solving: Stress period: 10 Time step: 4 - - - Solving: Stress period: 10 Time step: 5 - - - Solving: Stress period: 10 Time step: 6 - - - Solving: Stress period: 10 Time step: 7 - - - Solving: Stress period: 10 Time step: 8 - - - Solving: Stress period: 10 Time step: 9 - - - Solving: Stress period: 10 Time step: 10 - - - Solving: Stress period: 10 Time step: 11 - - - Solving: Stress period: 10 Time step: 12 - - - Solving: Stress period: 10 Time step: 13 - - - Solving: Stress period: 10 Time step: 14 - - - Solving: Stress period: 11 Time step: 1 - -1 - STRESS PERIOD NO. 11, LENGTH = 0.1314900E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 10 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 11 Time step: 2 - - - Solving: Stress period: 11 Time step: 3 - - - Solving: Stress period: 11 Time step: 4 - - - Solving: Stress period: 11 Time step: 5 - - - Solving: Stress period: 11 Time step: 6 - - - Solving: Stress period: 11 Time step: 7 - - - Solving: Stress period: 11 Time step: 8 - - - Solving: Stress period: 11 Time step: 9 - - - Solving: Stress period: 11 Time step: 10 - - - Solving: Stress period: 12 Time step: 1 - -1 - STRESS PERIOD NO. 12, LENGTH = 0.1840860E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 14 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 12 Time step: 2 - - - Solving: Stress period: 12 Time step: 3 - - - Solving: Stress period: 12 Time step: 4 - - - Solving: Stress period: 12 Time step: 5 - - - Solving: Stress period: 12 Time step: 6 - - - Solving: Stress period: 12 Time step: 7 - - - Solving: Stress period: 12 Time step: 8 - - - Solving: Stress period: 12 Time step: 9 - - - Solving: Stress period: 12 Time step: 10 - - - Solving: Stress period: 12 Time step: 11 - - - Solving: Stress period: 12 Time step: 12 - - - Solving: Stress period: 12 Time step: 13 - - - Solving: Stress period: 12 Time step: 14 - - - Solving: Stress period: 13 Time step: 1 - -1 - STRESS PERIOD NO. 13, LENGTH = 0.1314900E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 10 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 13 Time step: 2 - - - Solving: Stress period: 13 Time step: 3 - - - Solving: Stress period: 13 Time step: 4 - - - Solving: Stress period: 13 Time step: 5 - - - Solving: Stress period: 13 Time step: 6 - - - Solving: Stress period: 13 Time step: 7 - - - Solving: Stress period: 13 Time step: 8 - - - Solving: Stress period: 13 Time step: 9 - - - Solving: Stress period: 13 Time step: 10 - - - Solving: Stress period: 14 Time step: 1 - -1 - STRESS PERIOD NO. 14, LENGTH = 0.1840860E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 14 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 14 Time step: 2 - - - Solving: Stress period: 14 Time step: 3 - - - Solving: Stress period: 14 Time step: 4 - - - Solving: Stress period: 14 Time step: 5 - - - Solving: Stress period: 14 Time step: 6 - - - Solving: Stress period: 14 Time step: 7 - - - Solving: Stress period: 14 Time step: 8 - - - Solving: Stress period: 14 Time step: 9 - - - Solving: Stress period: 14 Time step: 10 - - - Solving: Stress period: 14 Time step: 11 - - - Solving: Stress period: 14 Time step: 12 - - - Solving: Stress period: 14 Time step: 13 - - - Solving: Stress period: 14 Time step: 14 - - - Solving: Stress period: 15 Time step: 1 - -1 - STRESS PERIOD NO. 15, LENGTH = 0.1314900E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 10 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 15 Time step: 2 - - - Solving: Stress period: 15 Time step: 3 - - - Solving: Stress period: 15 Time step: 4 - - - Solving: Stress period: 15 Time step: 5 - - - Solving: Stress period: 15 Time step: 6 - - - Solving: Stress period: 15 Time step: 7 - - - Solving: Stress period: 15 Time step: 8 - - - Solving: Stress period: 15 Time step: 9 - - - Solving: Stress period: 15 Time step: 10 - - - Solving: Stress period: 16 Time step: 1 - -1 - STRESS PERIOD NO. 16, LENGTH = 0.1840860E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 14 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 16 Time step: 2 - - - Solving: Stress period: 16 Time step: 3 - - - Solving: Stress period: 16 Time step: 4 - - - Solving: Stress period: 16 Time step: 5 - - - Solving: Stress period: 16 Time step: 6 - - - Solving: Stress period: 16 Time step: 7 - - - Solving: Stress period: 16 Time step: 8 - - - Solving: Stress period: 16 Time step: 9 - - - Solving: Stress period: 16 Time step: 10 - - - Solving: Stress period: 16 Time step: 11 - - - Solving: Stress period: 16 Time step: 12 - - - Solving: Stress period: 16 Time step: 13 - - - Solving: Stress period: 16 Time step: 14 - - - Solving: Stress period: 17 Time step: 1 - -1 - STRESS PERIOD NO. 17, LENGTH = 0.1314900E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 10 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 17 Time step: 2 - - - Solving: Stress period: 17 Time step: 3 - - - Solving: Stress period: 17 Time step: 4 - - - Solving: Stress period: 17 Time step: 5 - - - Solving: Stress period: 17 Time step: 6 - - - Solving: Stress period: 17 Time step: 7 - - - Solving: Stress period: 17 Time step: 8 - - - Solving: Stress period: 17 Time step: 9 - - - Solving: Stress period: 17 Time step: 10 - - - Solving: Stress period: 18 Time step: 1 - -1 - STRESS PERIOD NO. 18, LENGTH = 0.1840860E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 14 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 18 Time step: 2 - - - Solving: Stress period: 18 Time step: 3 - - - Solving: Stress period: 18 Time step: 4 - - - Solving: Stress period: 18 Time step: 5 - - - Solving: Stress period: 18 Time step: 6 - - - Solving: Stress period: 18 Time step: 7 - - - Solving: Stress period: 18 Time step: 8 - - - Solving: Stress period: 18 Time step: 9 - - - Solving: Stress period: 18 Time step: 10 - - - Solving: Stress period: 18 Time step: 11 - - - Solving: Stress period: 18 Time step: 12 - - - Solving: Stress period: 18 Time step: 13 - - - Solving: Stress period: 18 Time step: 14 - - - Solving: Stress period: 19 Time step: 1 - -1 - STRESS PERIOD NO. 19, LENGTH = 0.1314900E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 10 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 19 Time step: 2 - - - Solving: Stress period: 19 Time step: 3 - - - Solving: Stress period: 19 Time step: 4 - - - Solving: Stress period: 19 Time step: 5 - - - Solving: Stress period: 19 Time step: 6 - - - Solving: Stress period: 19 Time step: 7 - - - Solving: Stress period: 19 Time step: 8 - - - Solving: Stress period: 19 Time step: 9 - - - Solving: Stress period: 19 Time step: 10 - - - Solving: Stress period: 20 Time step: 1 - -1 - STRESS PERIOD NO. 20, LENGTH = 0.1840860E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 14 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 20 Time step: 2 - - - Solving: Stress period: 20 Time step: 3 - - - Solving: Stress period: 20 Time step: 4 - - - Solving: Stress period: 20 Time step: 5 - - - Solving: Stress period: 20 Time step: 6 - - - Solving: Stress period: 20 Time step: 7 - - - Solving: Stress period: 20 Time step: 8 - - - Solving: Stress period: 20 Time step: 9 - - - Solving: Stress period: 20 Time step: 10 - - - Solving: Stress period: 20 Time step: 11 - - - Solving: Stress period: 20 Time step: 12 - - - Solving: Stress period: 20 Time step: 13 - - - Solving: Stress period: 20 Time step: 14 - - - Solving: Stress period: 21 Time step: 1 - -1 - STRESS PERIOD NO. 21, LENGTH = 0.1314900E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 10 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 21 Time step: 2 - - - Solving: Stress period: 21 Time step: 3 - - - Solving: Stress period: 21 Time step: 4 - - - Solving: Stress period: 21 Time step: 5 - - - Solving: Stress period: 21 Time step: 6 - - - Solving: Stress period: 21 Time step: 7 - - - Solving: Stress period: 21 Time step: 8 - - - Solving: Stress period: 21 Time step: 9 - - - Solving: Stress period: 21 Time step: 10 - - - Solving: Stress period: 22 Time step: 1 - -1 - STRESS PERIOD NO. 22, LENGTH = 0.1840860E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 14 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 22 Time step: 2 - - - Solving: Stress period: 22 Time step: 3 - - - Solving: Stress period: 22 Time step: 4 - - - Solving: Stress period: 22 Time step: 5 - - - Solving: Stress period: 22 Time step: 6 - - - Solving: Stress period: 22 Time step: 7 - - - Solving: Stress period: 22 Time step: 8 - - - Solving: Stress period: 22 Time step: 9 - - - Solving: Stress period: 22 Time step: 10 - - - Solving: Stress period: 22 Time step: 11 - - - Solving: Stress period: 22 Time step: 12 - - - Solving: Stress period: 22 Time step: 13 - - - Solving: Stress period: 22 Time step: 14 - - - Solving: Stress period: 23 Time step: 1 - -1 - STRESS PERIOD NO. 23, LENGTH = 0.1314900E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 10 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 23 Time step: 2 - - - Solving: Stress period: 23 Time step: 3 - - - Solving: Stress period: 23 Time step: 4 - - - Solving: Stress period: 23 Time step: 5 - - - Solving: Stress period: 23 Time step: 6 - - - Solving: Stress period: 23 Time step: 7 - - - Solving: Stress period: 23 Time step: 8 - - - Solving: Stress period: 23 Time step: 9 - - - Solving: Stress period: 23 Time step: 10 - - - Solving: Stress period: 24 Time step: 1 - -1 - STRESS PERIOD NO. 24, LENGTH = 0.1840860E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 14 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 24 Time step: 2 - - - Solving: Stress period: 24 Time step: 3 - - - Solving: Stress period: 24 Time step: 4 - - - Solving: Stress period: 24 Time step: 5 - - - Solving: Stress period: 24 Time step: 6 - - - Solving: Stress period: 24 Time step: 7 - - - Solving: Stress period: 24 Time step: 8 - - - Solving: Stress period: 24 Time step: 9 - - - Solving: Stress period: 24 Time step: 10 - - - Solving: Stress period: 24 Time step: 11 - - - Solving: Stress period: 24 Time step: 12 - - - Solving: Stress period: 24 Time step: 13 - - - Solving: Stress period: 24 Time step: 14 - - - Solving: Stress period: 25 Time step: 1 - -1 - STRESS PERIOD NO. 25, LENGTH = 0.1314900E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 10 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 25 Time step: 2 - - - Solving: Stress period: 25 Time step: 3 - - - Solving: Stress period: 25 Time step: 4 - - - Solving: Stress period: 25 Time step: 5 - - - Solving: Stress period: 25 Time step: 6 - - - Solving: Stress period: 25 Time step: 7 - - - Solving: Stress period: 25 Time step: 8 - - - Solving: Stress period: 25 Time step: 9 - - - Solving: Stress period: 25 Time step: 10 - - - Solving: Stress period: 26 Time step: 1 - -1 - STRESS PERIOD NO. 26, LENGTH = 0.1840860E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 14 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 26 Time step: 2 - - - Solving: Stress period: 26 Time step: 3 - - - Solving: Stress period: 26 Time step: 4 - - - Solving: Stress period: 26 Time step: 5 - - - Solving: Stress period: 26 Time step: 6 - - - Solving: Stress period: 26 Time step: 7 - - - Solving: Stress period: 26 Time step: 8 - - - Solving: Stress period: 26 Time step: 9 - - - Solving: Stress period: 26 Time step: 10 - - - Solving: Stress period: 26 Time step: 11 - - - Solving: Stress period: 26 Time step: 12 - - - Solving: Stress period: 26 Time step: 13 - - - Solving: Stress period: 26 Time step: 14 - - - Solving: Stress period: 27 Time step: 1 - -1 - STRESS PERIOD NO. 27, LENGTH = 0.1314900E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 10 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 27 Time step: 2 - - - Solving: Stress period: 27 Time step: 3 - - - Solving: Stress period: 27 Time step: 4 - - - Solving: Stress period: 27 Time step: 5 - - - Solving: Stress period: 27 Time step: 6 - - - Solving: Stress period: 27 Time step: 7 - - - Solving: Stress period: 27 Time step: 8 - - - Solving: Stress period: 27 Time step: 9 - - - Solving: Stress period: 27 Time step: 10 - - - Solving: Stress period: 28 Time step: 1 - -1 - STRESS PERIOD NO. 28, LENGTH = 0.1840860E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 14 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 28 Time step: 2 - - - Solving: Stress period: 28 Time step: 3 - - - Solving: Stress period: 28 Time step: 4 - - - Solving: Stress period: 28 Time step: 5 - - - Solving: Stress period: 28 Time step: 6 - - - Solving: Stress period: 28 Time step: 7 - - - Solving: Stress period: 28 Time step: 8 - - - Solving: Stress period: 28 Time step: 9 - - - Solving: Stress period: 28 Time step: 10 - - - Solving: Stress period: 28 Time step: 11 - - - Solving: Stress period: 28 Time step: 12 - - - Solving: Stress period: 28 Time step: 13 - - - Solving: Stress period: 28 Time step: 14 - - - Solving: Stress period: 29 Time step: 1 - -1 - STRESS PERIOD NO. 29, LENGTH = 0.1314900E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 10 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 29 Time step: 2 - - - Solving: Stress period: 29 Time step: 3 - - - Solving: Stress period: 29 Time step: 4 - - - Solving: Stress period: 29 Time step: 5 - - - Solving: Stress period: 29 Time step: 6 - - - Solving: Stress period: 29 Time step: 7 - - - Solving: Stress period: 29 Time step: 8 - - - Solving: Stress period: 29 Time step: 9 - - - Solving: Stress period: 29 Time step: 10 - - - Solving: Stress period: 30 Time step: 1 - -1 - STRESS PERIOD NO. 30, LENGTH = 0.1840860E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 14 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 30 Time step: 2 - - - Solving: Stress period: 30 Time step: 3 - - - Solving: Stress period: 30 Time step: 4 - - - Solving: Stress period: 30 Time step: 5 - - - Solving: Stress period: 30 Time step: 6 - - - Solving: Stress period: 30 Time step: 7 - - - Solving: Stress period: 30 Time step: 8 - - - Solving: Stress period: 30 Time step: 9 - - - Solving: Stress period: 30 Time step: 10 - - - Solving: Stress period: 30 Time step: 11 - - - Solving: Stress period: 30 Time step: 12 - - - Solving: Stress period: 30 Time step: 13 - - - Solving: Stress period: 30 Time step: 14 - - - Solving: Stress period: 31 Time step: 1 - -1 - STRESS PERIOD NO. 31, LENGTH = 0.1314900E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 10 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 31 Time step: 2 - - - Solving: Stress period: 31 Time step: 3 - - - Solving: Stress period: 31 Time step: 4 - - - Solving: Stress period: 31 Time step: 5 - - - Solving: Stress period: 31 Time step: 6 - - - Solving: Stress period: 31 Time step: 7 - - - Solving: Stress period: 31 Time step: 8 - - - Solving: Stress period: 31 Time step: 9 - - - Solving: Stress period: 31 Time step: 10 - - - Solving: Stress period: 32 Time step: 1 - -1 - STRESS PERIOD NO. 32, LENGTH = 0.1840860E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 14 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 32 Time step: 2 - - - Solving: Stress period: 32 Time step: 3 - - - Solving: Stress period: 32 Time step: 4 - - - Solving: Stress period: 32 Time step: 5 - - - Solving: Stress period: 32 Time step: 6 - - - Solving: Stress period: 32 Time step: 7 - - - Solving: Stress period: 32 Time step: 8 - - - Solving: Stress period: 32 Time step: 9 - - - Solving: Stress period: 32 Time step: 10 - - - Solving: Stress period: 32 Time step: 11 - - - Solving: Stress period: 32 Time step: 12 - - - Solving: Stress period: 32 Time step: 13 - - - Solving: Stress period: 32 Time step: 14 - - - Solving: Stress period: 33 Time step: 1 - -1 - STRESS PERIOD NO. 33, LENGTH = 0.1314900E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 10 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 33 Time step: 2 - - - Solving: Stress period: 33 Time step: 3 - - - Solving: Stress period: 33 Time step: 4 - - - Solving: Stress period: 33 Time step: 5 - - - Solving: Stress period: 33 Time step: 6 - - - Solving: Stress period: 33 Time step: 7 - - - Solving: Stress period: 33 Time step: 8 - - - Solving: Stress period: 33 Time step: 9 - - - Solving: Stress period: 33 Time step: 10 - - - Solving: Stress period: 34 Time step: 1 - -1 - STRESS PERIOD NO. 34, LENGTH = 0.1840860E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 14 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 34 Time step: 2 - - - Solving: Stress period: 34 Time step: 3 - - - Solving: Stress period: 34 Time step: 4 - - - Solving: Stress period: 34 Time step: 5 - - - Solving: Stress period: 34 Time step: 6 - - - Solving: Stress period: 34 Time step: 7 - - - Solving: Stress period: 34 Time step: 8 - - - Solving: Stress period: 34 Time step: 9 - - - Solving: Stress period: 34 Time step: 10 - - - Solving: Stress period: 34 Time step: 11 - - - Solving: Stress period: 34 Time step: 12 - - - Solving: Stress period: 34 Time step: 13 - - - Solving: Stress period: 34 Time step: 14 - - - Solving: Stress period: 35 Time step: 1 - -1 - STRESS PERIOD NO. 35, LENGTH = 0.1314900E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 10 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 35 Time step: 2 - - - Solving: Stress period: 35 Time step: 3 - - - Solving: Stress period: 35 Time step: 4 - - - Solving: Stress period: 35 Time step: 5 - - - Solving: Stress period: 35 Time step: 6 - - - Solving: Stress period: 35 Time step: 7 - - - Solving: Stress period: 35 Time step: 8 - - - Solving: Stress period: 35 Time step: 9 - - - Solving: Stress period: 35 Time step: 10 - - - Solving: Stress period: 36 Time step: 1 - -1 - STRESS PERIOD NO. 36, LENGTH = 0.1840860E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 14 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 36 Time step: 2 - - - Solving: Stress period: 36 Time step: 3 - - - Solving: Stress period: 36 Time step: 4 - - - Solving: Stress period: 36 Time step: 5 - - - Solving: Stress period: 36 Time step: 6 - - - Solving: Stress period: 36 Time step: 7 - - - Solving: Stress period: 36 Time step: 8 - - - Solving: Stress period: 36 Time step: 9 - - - Solving: Stress period: 36 Time step: 10 - - - Solving: Stress period: 36 Time step: 11 - - - Solving: Stress period: 36 Time step: 12 - - - Solving: Stress period: 36 Time step: 13 - - - Solving: Stress period: 36 Time step: 14 - - - Solving: Stress period: 37 Time step: 1 - -1 - STRESS PERIOD NO. 37, LENGTH = 0.1314900E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 10 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 37 Time step: 2 - - - Solving: Stress period: 37 Time step: 3 - - - Solving: Stress period: 37 Time step: 4 - - - Solving: Stress period: 37 Time step: 5 - - - Solving: Stress period: 37 Time step: 6 - - - Solving: Stress period: 37 Time step: 7 - - - Solving: Stress period: 37 Time step: 8 - - - Solving: Stress period: 37 Time step: 9 - - - Solving: Stress period: 37 Time step: 10 - - - Solving: Stress period: 38 Time step: 1 - -1 - STRESS PERIOD NO. 38, LENGTH = 0.1840860E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 14 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 38 Time step: 2 - - - Solving: Stress period: 38 Time step: 3 - - - Solving: Stress period: 38 Time step: 4 - - - Solving: Stress period: 38 Time step: 5 - - - Solving: Stress period: 38 Time step: 6 - - - Solving: Stress period: 38 Time step: 7 - - - Solving: Stress period: 38 Time step: 8 - - - Solving: Stress period: 38 Time step: 9 - - - Solving: Stress period: 38 Time step: 10 - - - Solving: Stress period: 38 Time step: 11 - - - Solving: Stress period: 38 Time step: 12 - - - Solving: Stress period: 38 Time step: 13 - - - Solving: Stress period: 38 Time step: 14 - - - Solving: Stress period: 39 Time step: 1 - -1 - STRESS PERIOD NO. 39, LENGTH = 0.1314900E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 10 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 39 Time step: 2 - - - Solving: Stress period: 39 Time step: 3 - - - Solving: Stress period: 39 Time step: 4 - - - Solving: Stress period: 39 Time step: 5 - - - Solving: Stress period: 39 Time step: 6 - - - Solving: Stress period: 39 Time step: 7 - - - Solving: Stress period: 39 Time step: 8 - - - Solving: Stress period: 39 Time step: 9 - - - Solving: Stress period: 39 Time step: 10 - - - Solving: Stress period: 40 Time step: 1 - -1 - STRESS PERIOD NO. 40, LENGTH = 0.1840860E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 14 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 40 Time step: 2 - - - Solving: Stress period: 40 Time step: 3 - - - Solving: Stress period: 40 Time step: 4 - - - Solving: Stress period: 40 Time step: 5 - - - Solving: Stress period: 40 Time step: 6 - - - Solving: Stress period: 40 Time step: 7 - - - Solving: Stress period: 40 Time step: 8 - - - Solving: Stress period: 40 Time step: 9 - - - Solving: Stress period: 40 Time step: 10 - - - Solving: Stress period: 40 Time step: 11 - - - Solving: Stress period: 40 Time step: 12 - - - Solving: Stress period: 40 Time step: 13 - - - Solving: Stress period: 40 Time step: 14 - - - Solving: Stress period: 41 Time step: 1 - -1 - STRESS PERIOD NO. 41, LENGTH = 0.1314900E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 10 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 41 Time step: 2 - - - Solving: Stress period: 41 Time step: 3 - - - Solving: Stress period: 41 Time step: 4 - - - Solving: Stress period: 41 Time step: 5 - - - Solving: Stress period: 41 Time step: 6 - - - Solving: Stress period: 41 Time step: 7 - - - Solving: Stress period: 41 Time step: 8 - - - Solving: Stress period: 41 Time step: 9 - - - Solving: Stress period: 41 Time step: 10 - - - Solving: Stress period: 42 Time step: 1 - -1 - STRESS PERIOD NO. 42, LENGTH = 0.1840860E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 14 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 42 Time step: 2 - - - Solving: Stress period: 42 Time step: 3 - - - Solving: Stress period: 42 Time step: 4 - - - Solving: Stress period: 42 Time step: 5 - - - Solving: Stress period: 42 Time step: 6 - - - Solving: Stress period: 42 Time step: 7 - - - Solving: Stress period: 42 Time step: 8 - - - Solving: Stress period: 42 Time step: 9 - - - Solving: Stress period: 42 Time step: 10 - - - Solving: Stress period: 42 Time step: 11 - - - Solving: Stress period: 42 Time step: 12 - - - Solving: Stress period: 42 Time step: 13 - - - Solving: Stress period: 42 Time step: 14 - - - Solving: Stress period: 43 Time step: 1 - -1 - STRESS PERIOD NO. 43, LENGTH = 0.1314900E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 10 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 43 Time step: 2 - - - Solving: Stress period: 43 Time step: 3 - - - Solving: Stress period: 43 Time step: 4 - - - Solving: Stress period: 43 Time step: 5 - - - Solving: Stress period: 43 Time step: 6 - - - Solving: Stress period: 43 Time step: 7 - - - Solving: Stress period: 43 Time step: 8 - - - Solving: Stress period: 43 Time step: 9 - - - Solving: Stress period: 43 Time step: 10 - - - Solving: Stress period: 44 Time step: 1 - -1 - STRESS PERIOD NO. 44, LENGTH = 0.1840860E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 14 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 44 Time step: 2 - - - Solving: Stress period: 44 Time step: 3 - - - Solving: Stress period: 44 Time step: 4 - - - Solving: Stress period: 44 Time step: 5 - - - Solving: Stress period: 44 Time step: 6 - - - Solving: Stress period: 44 Time step: 7 - - - Solving: Stress period: 44 Time step: 8 - - - Solving: Stress period: 44 Time step: 9 - - - Solving: Stress period: 44 Time step: 10 - - - Solving: Stress period: 44 Time step: 11 - - - Solving: Stress period: 44 Time step: 12 - - - Solving: Stress period: 44 Time step: 13 - - - Solving: Stress period: 44 Time step: 14 - - - Solving: Stress period: 45 Time step: 1 - -1 - STRESS PERIOD NO. 45, LENGTH = 0.1314900E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 10 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 45 Time step: 2 - - - Solving: Stress period: 45 Time step: 3 - - - Solving: Stress period: 45 Time step: 4 - - - Solving: Stress period: 45 Time step: 5 - - - Solving: Stress period: 45 Time step: 6 - - - Solving: Stress period: 45 Time step: 7 - - - Solving: Stress period: 45 Time step: 8 - - - Solving: Stress period: 45 Time step: 9 - - - Solving: Stress period: 45 Time step: 10 - - - Solving: Stress period: 46 Time step: 1 - -1 - STRESS PERIOD NO. 46, LENGTH = 0.1840860E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 14 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 46 Time step: 2 - - - Solving: Stress period: 46 Time step: 3 - - - Solving: Stress period: 46 Time step: 4 - - - Solving: Stress period: 46 Time step: 5 - - - Solving: Stress period: 46 Time step: 6 - - - Solving: Stress period: 46 Time step: 7 - - - Solving: Stress period: 46 Time step: 8 - - - Solving: Stress period: 46 Time step: 9 - - - Solving: Stress period: 46 Time step: 10 - - - Solving: Stress period: 46 Time step: 11 - - - Solving: Stress period: 46 Time step: 12 - - - Solving: Stress period: 46 Time step: 13 - - - Solving: Stress period: 46 Time step: 14 - - - Solving: Stress period: 47 Time step: 1 - -1 - STRESS PERIOD NO. 47, LENGTH = 0.1314900E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 10 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 47 Time step: 2 - - - Solving: Stress period: 47 Time step: 3 - - - Solving: Stress period: 47 Time step: 4 - - - Solving: Stress period: 47 Time step: 5 - - - Solving: Stress period: 47 Time step: 6 - - - Solving: Stress period: 47 Time step: 7 - - - Solving: Stress period: 47 Time step: 8 - - - Solving: Stress period: 47 Time step: 9 - - - Solving: Stress period: 47 Time step: 10 - - - Solving: Stress period: 48 Time step: 1 - -1 - STRESS PERIOD NO. 48, LENGTH = 0.1840860E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 14 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 48 Time step: 2 - - - Solving: Stress period: 48 Time step: 3 - - - Solving: Stress period: 48 Time step: 4 - - - Solving: Stress period: 48 Time step: 5 - - - Solving: Stress period: 48 Time step: 6 - - - Solving: Stress period: 48 Time step: 7 - - - Solving: Stress period: 48 Time step: 8 - - - Solving: Stress period: 48 Time step: 9 - - - Solving: Stress period: 48 Time step: 10 - - - Solving: Stress period: 48 Time step: 11 - - - Solving: Stress period: 48 Time step: 12 - - - Solving: Stress period: 48 Time step: 13 - - - Solving: Stress period: 48 Time step: 14 - - - Solving: Stress period: 49 Time step: 1 - -1 - STRESS PERIOD NO. 49, LENGTH = 0.1314900E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 10 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 49 Time step: 2 - - - Solving: Stress period: 49 Time step: 3 - - - Solving: Stress period: 49 Time step: 4 - - - Solving: Stress period: 49 Time step: 5 - - - Solving: Stress period: 49 Time step: 6 - - - Solving: Stress period: 49 Time step: 7 - - - Solving: Stress period: 49 Time step: 8 - - - Solving: Stress period: 49 Time step: 9 - - - Solving: Stress period: 49 Time step: 10 - - - Solving: Stress period: 50 Time step: 1 - -1 - STRESS PERIOD NO. 50, LENGTH = 0.1840860E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 14 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 50 Time step: 2 - - - Solving: Stress period: 50 Time step: 3 - - - Solving: Stress period: 50 Time step: 4 - - - Solving: Stress period: 50 Time step: 5 - - - Solving: Stress period: 50 Time step: 6 - - - Solving: Stress period: 50 Time step: 7 - - - Solving: Stress period: 50 Time step: 8 - - - Solving: Stress period: 50 Time step: 9 - - - Solving: Stress period: 50 Time step: 10 - - - Solving: Stress period: 50 Time step: 11 - - - Solving: Stress period: 50 Time step: 12 - - - Solving: Stress period: 50 Time step: 13 - - - Solving: Stress period: 50 Time step: 14 - - - Solving: Stress period: 51 Time step: 1 - -1 - STRESS PERIOD NO. 51, LENGTH = 0.1314900E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 10 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 51 Time step: 2 - - - Solving: Stress period: 51 Time step: 3 - - - Solving: Stress period: 51 Time step: 4 - - - Solving: Stress period: 51 Time step: 5 - - - Solving: Stress period: 51 Time step: 6 - - - Solving: Stress period: 51 Time step: 7 - - - Solving: Stress period: 51 Time step: 8 - - - Solving: Stress period: 51 Time step: 9 - - - Solving: Stress period: 51 Time step: 10 - - - Solving: Stress period: 52 Time step: 1 - -1 - STRESS PERIOD NO. 52, LENGTH = 0.1840860E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 14 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 52 Time step: 2 - - - Solving: Stress period: 52 Time step: 3 - - - Solving: Stress period: 52 Time step: 4 - - - Solving: Stress period: 52 Time step: 5 - - - Solving: Stress period: 52 Time step: 6 - - - Solving: Stress period: 52 Time step: 7 - - - Solving: Stress period: 52 Time step: 8 - - - Solving: Stress period: 52 Time step: 9 - - - Solving: Stress period: 52 Time step: 10 - - - Solving: Stress period: 52 Time step: 11 - - - Solving: Stress period: 52 Time step: 12 - - - Solving: Stress period: 52 Time step: 13 - - - Solving: Stress period: 52 Time step: 14 - - - Solving: Stress period: 53 Time step: 1 - -1 - STRESS PERIOD NO. 53, LENGTH = 0.1314900E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 10 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 53 Time step: 2 - - - Solving: Stress period: 53 Time step: 3 - - - Solving: Stress period: 53 Time step: 4 - - - Solving: Stress period: 53 Time step: 5 - - - Solving: Stress period: 53 Time step: 6 - - - Solving: Stress period: 53 Time step: 7 - - - Solving: Stress period: 53 Time step: 8 - - - Solving: Stress period: 53 Time step: 9 - - - Solving: Stress period: 53 Time step: 10 - - - Solving: Stress period: 54 Time step: 1 - -1 - STRESS PERIOD NO. 54, LENGTH = 0.1840860E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 14 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 54 Time step: 2 - - - Solving: Stress period: 54 Time step: 3 - - - Solving: Stress period: 54 Time step: 4 - - - Solving: Stress period: 54 Time step: 5 - - - Solving: Stress period: 54 Time step: 6 - - - Solving: Stress period: 54 Time step: 7 - - - Solving: Stress period: 54 Time step: 8 - - - Solving: Stress period: 54 Time step: 9 - - - Solving: Stress period: 54 Time step: 10 - - - Solving: Stress period: 54 Time step: 11 - - - Solving: Stress period: 54 Time step: 12 - - - Solving: Stress period: 54 Time step: 13 - - - Solving: Stress period: 54 Time step: 14 - - - Solving: Stress period: 55 Time step: 1 - -1 - STRESS PERIOD NO. 55, LENGTH = 0.1314900E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 10 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 55 Time step: 2 - - - Solving: Stress period: 55 Time step: 3 - - - Solving: Stress period: 55 Time step: 4 - - - Solving: Stress period: 55 Time step: 5 - - - Solving: Stress period: 55 Time step: 6 - - - Solving: Stress period: 55 Time step: 7 - - - Solving: Stress period: 55 Time step: 8 - - - Solving: Stress period: 55 Time step: 9 - - - Solving: Stress period: 55 Time step: 10 - - - Solving: Stress period: 56 Time step: 1 - -1 - STRESS PERIOD NO. 56, LENGTH = 0.1840860E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 14 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 56 Time step: 2 - - - Solving: Stress period: 56 Time step: 3 - - - Solving: Stress period: 56 Time step: 4 - - - Solving: Stress period: 56 Time step: 5 - - - Solving: Stress period: 56 Time step: 6 - - - Solving: Stress period: 56 Time step: 7 - - - Solving: Stress period: 56 Time step: 8 - - - Solving: Stress period: 56 Time step: 9 - - - Solving: Stress period: 56 Time step: 10 - - - Solving: Stress period: 56 Time step: 11 - - - Solving: Stress period: 56 Time step: 12 - - - Solving: Stress period: 56 Time step: 13 - - - Solving: Stress period: 56 Time step: 14 - - - Solving: Stress period: 57 Time step: 1 - -1 - STRESS PERIOD NO. 57, LENGTH = 0.1314900E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 10 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 57 Time step: 2 - - - Solving: Stress period: 57 Time step: 3 - - - Solving: Stress period: 57 Time step: 4 - - - Solving: Stress period: 57 Time step: 5 - - - Solving: Stress period: 57 Time step: 6 - - - Solving: Stress period: 57 Time step: 7 - - - Solving: Stress period: 57 Time step: 8 - - - Solving: Stress period: 57 Time step: 9 - - - Solving: Stress period: 57 Time step: 10 - - - Solving: Stress period: 58 Time step: 1 - -1 - STRESS PERIOD NO. 58, LENGTH = 0.1840860E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 14 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 58 Time step: 2 - - - Solving: Stress period: 58 Time step: 3 - - - Solving: Stress period: 58 Time step: 4 - - - Solving: Stress period: 58 Time step: 5 - - - Solving: Stress period: 58 Time step: 6 - - - Solving: Stress period: 58 Time step: 7 - - - Solving: Stress period: 58 Time step: 8 - - - Solving: Stress period: 58 Time step: 9 - - - Solving: Stress period: 58 Time step: 10 - - - Solving: Stress period: 58 Time step: 11 - - - Solving: Stress period: 58 Time step: 12 - - - Solving: Stress period: 58 Time step: 13 - - - Solving: Stress period: 58 Time step: 14 - - - Solving: Stress period: 59 Time step: 1 - -1 - STRESS PERIOD NO. 59, LENGTH = 0.1314900E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 10 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 59 Time step: 2 - - - Solving: Stress period: 59 Time step: 3 - - - Solving: Stress period: 59 Time step: 4 - - - Solving: Stress period: 59 Time step: 5 - - - Solving: Stress period: 59 Time step: 6 - - - Solving: Stress period: 59 Time step: 7 - - - Solving: Stress period: 59 Time step: 8 - - - Solving: Stress period: 59 Time step: 9 - - - Solving: Stress period: 59 Time step: 10 - - - Solving: Stress period: 60 Time step: 1 - -1 - STRESS PERIOD NO. 60, LENGTH = 0.1840860E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 14 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 60 Time step: 2 - - - Solving: Stress period: 60 Time step: 3 - - - Solving: Stress period: 60 Time step: 4 - - - Solving: Stress period: 60 Time step: 5 - - - Solving: Stress period: 60 Time step: 6 - - - Solving: Stress period: 60 Time step: 7 - - - Solving: Stress period: 60 Time step: 8 - - - Solving: Stress period: 60 Time step: 9 - - - Solving: Stress period: 60 Time step: 10 - - - Solving: Stress period: 60 Time step: 11 - - - Solving: Stress period: 60 Time step: 12 - - - Solving: Stress period: 60 Time step: 13 - - - Solving: Stress period: 60 Time step: 14 - - - Solving: Stress period: 61 Time step: 1 - -1 - STRESS PERIOD NO. 61, LENGTH = 0.1314900E+08 - ------------------------------------------ - NUMBER OF TIME STEPS = 10 - MULTIPLIER FOR DELT = 1.000 - INITIAL TIME STEP SIZE = 1314900. - - Solving: Stress period: 61 Time step: 2 - - - Solving: Stress period: 61 Time step: 3 - - - Solving: Stress period: 61 Time step: 4 - - - Solving: Stress period: 61 Time step: 5 - - - Solving: Stress period: 61 Time step: 6 - - - Solving: Stress period: 61 Time step: 7 - - - Solving: Stress period: 61 Time step: 8 - - - Solving: Stress period: 61 Time step: 9 - - - Solving: Stress period: 61 Time step: 10 - - - - Solution SLN_1 summary - ---------------------------------------------------------------------- - Total formulate time: 1.303031000000611 seconds - Total solution time: .7496689999999060 seconds - - - - Solution SLN_2 summary - ---------------------------------------------------------------------- - Total formulate time: 53.76477000000007 seconds - Total solution time: 129.3818160000000 seconds - - - MEMORY MANAGER TOTAL STORAGE BY DATA TYPE, IN MEGABYTES - ------------------------------- - ALLOCATED - DATA TYPE MEMORY - ------------------------------- - Character 1.71450000E-02 - Logical 6.80000000E-05 - Integer 13.460984 - Real 24.887384 - ------------------------------- - Total 38.365581 - Virtual 0.0000000 - ------------------------------- - - - - Run end date and time (yyyy/mm/dd hh:mm:ss): 2025/11/13 20:34:57 - Elapsed run time: 4 Minutes, 43.903 Seconds - Normal termination of simulation.