{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 3. Slug Test - Multi-well in confined aquifer\n", "\n", "**This test is taken from examples of AQTESOLV.**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 1. Import required libraries" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "\n", "import ttim as ttm\n", "\n", "plt.rcParams[\"figure.figsize\"] = [5, 3]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction and Conceptual Model\n", "\n", "In this notebook, we reproduce the work of Yang (2020) to check the TTim performance in analysing slug-test. We later compare the solution in TTim with the KGS analytical model (Hyder et al. 1994) implemented in AQTESOLV (Duffield, 2007) and to the MLU model (Carlson & Randall, 2012).\n", "\n", "This Slug Test was reported in Butler (1998). A well (Ln-2) fully penetrates a sandy confined aquifer, with 6.1 m thickness. Additionally, a fully penetrating observation well (Ln-3) is placed 6.45 m away from the test well.\n", "\n", "The slug displacement is 2.798 m. Head change was recorded at the slug well and the observation well. The well and casing radii of the slug well are 0.102 and 0.051 m, respectively. For the observation well, they are 0.051 and 0.025 m, respectively.\n", "\n", "The conceptual model can be seen in the figure below.\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "jupyter": { "source_hidden": true }, "tags": [ "hide-input" ] }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "##Now printing the conceptual model figure:\n", "\n", "fig = plt.figure()\n", "ax = fig.add_subplot(1, 1, 1)\n", "# sky\n", "sky = plt.Rectangle((-5, 1), width=15, height=3, fc=\"b\", zorder=0, alpha=0.1)\n", "ax.add_patch(sky)\n", "\n", "# Aquifer:\n", "ground = plt.Rectangle(\n", " (-5, -6.1),\n", " width=15,\n", " height=6.1,\n", " fc=np.array([209, 179, 127]) / 255,\n", " zorder=0,\n", " alpha=0.9,\n", ")\n", "ax.add_patch(ground)\n", "\n", "well = plt.Rectangle(\n", " (-0.5, -(6.1)), width=1, height=(7.1), fc=np.array([200, 200, 200]) / 255, zorder=1\n", ")\n", "ax.add_patch(well)\n", "\n", "# Confining Unit\n", "conf = plt.Rectangle(\n", " (-5, 0), width=15, height=1, fc=np.array([100, 100, 100]) / 255, zorder=0, alpha=0.9\n", ")\n", "ax.add_patch(conf)\n", "\n", "# Wellhead\n", "wellhead = plt.Rectangle(\n", " (-0.6, 1),\n", " width=1.2,\n", " height=1.5,\n", " fc=np.array([200, 200, 200]) / 255,\n", " zorder=2,\n", " ec=\"k\",\n", ")\n", "ax.add_patch(wellhead)\n", "\n", "# Screen for the well:\n", "screen = plt.Rectangle(\n", " (-0.5, -(6.1)),\n", " width=1,\n", " height=6.1,\n", " fc=np.array([200, 200, 200]) / 255,\n", " alpha=1,\n", " zorder=2,\n", " ec=\"k\",\n", " ls=\"--\",\n", ")\n", "screen.set_linewidth(2)\n", "ax.add_patch(screen)\n", "pumping_arrow = plt.Arrow(x=1, y=1.5, dx=0, dy=1, color=\"#00035b\")\n", "ax.add_patch(pumping_arrow)\n", "ax.text(x=1.2, y=1.5, s=r\"$ D = 2.798$ m\", fontsize=\"large\")\n", "\n", "# Piezometer\n", "piez = plt.Rectangle(\n", " (6.20, -(6.1)),\n", " width=0.5,\n", " height=(7.1),\n", " fc=np.array([200, 200, 200]) / 255,\n", " zorder=1,\n", ")\n", "ax.add_patch(piez)\n", "screen_piez = plt.Rectangle(\n", " (6.2, -(6.1)),\n", " width=0.5,\n", " height=6.1,\n", " fc=np.array([200, 200, 200]) / 255,\n", " alpha=1,\n", " zorder=2,\n", " ec=\"k\",\n", " ls=\"--\",\n", ")\n", "screen_piez.set_linewidth(2)\n", "ax.add_patch(screen_piez)\n", "\n", "# last line\n", "line = plt.Line2D(xdata=[-200, 1200], ydata=[1, 1], color=\"k\")\n", "ax.add_line(line)\n", "\n", "# Water table\n", "# wt = plt.Line2D(xdata= [-200,1200], ydata = [0,0], color = \"b\")\n", "# ax.add_line(wt)\n", "\n", "ax.text(0.6, -0.5, s=\"Ln-2\", fontsize=\"large\")\n", "ax.text(6.9, -0.5, \"Ln-3\", fontsize=\"large\")\n", "ax.set_xlim([-5, 10])\n", "ax.set_ylim([-6.1, 3])\n", "ax.set_xlabel(\"Distance [m]\")\n", "ax.set_ylabel(\"Relative height [m]\")\n", "ax.set_title(\"Conceptual Model - Multi-Well Example\");" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "## Step 2. Set basic parameters" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "H0 = 2.798 # initial displacement, m\n", "b = -6.1 # aquifer thickness, m\n", "rw1 = 0.102 # well radius of Ln-2 Well, m\n", "rw2 = 0.071 # well radius of observation Ln-3 Well, m\n", "rc1 = 0.051 # casing radius of Ln-2 Well, m\n", "rc2 = 0.025 # casing radius of Ln-3 Well, m\n", "r = 6.45 # distance from observation well to test well, m" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 3. Converting slug displacement to volume" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Slug: 0.02286 m^3\n" ] } ], "source": [ "Q = np.pi * rc1**2 * H0\n", "print(\"Slug:\", round(Q, 5), \"m^3\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 4. Load data" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "data1 = np.loadtxt(\"data/ln-2.txt\")\n", "t1 = data1[:, 0] / 60 / 60 / 24 # convert time from seconds to days\n", "h1 = data1[:, 1]\n", "data2 = np.loadtxt(\"data/ln-3.txt\")\n", "t2 = data2[:, 0] / 60 / 60 / 24\n", "h2 = data2[:, 1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 5. Create First Model - single layer\n", "\n", "We begin with a single layer model built in ```ModelMaq```.\n", "Details on setting up the model can be seen in: [Confined 1 - Oude Korendijk](confined1_oude_korendijk.ipynb).\n", "\n", "The slug well is set accordingly. Details on setting up the ```Well``` object can be seen in: [Slug 1 - Pratt County](slug1_pratt_county.ipynb)." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "self.neq 1\n", "solution complete\n" ] } ], "source": [ "ml_0 = ttm.ModelMaq(kaq=10, z=[0, b], Saq=1e-4, tmin=1e-5, tmax=0.01)\n", "w_0 = ttm.Well(\n", " ml_0, xw=0, yw=0, rw=rw1, rc=rc1, tsandQ=[(0, -Q)], layers=0, wbstype=\"slug\"\n", ")\n", "ml_0.solve()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 6. Model calibration both simultaneous wells\n", "\n", "\n", "The procedures for calibration can be seen in [Unconfined 1 - Vennebulten](unconfined1_vennebulten.ipynb)\n", "\n", "We calibrate hydraulic conductivity and specific storage, as in the KGS model (Hyder et al. 1994)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "....................................\n", "Fit succeeded.\n" ] } ], "source": [ "# unknown parameters: kaq, Saq\n", "ca_0 = ttm.Calibrate(ml_0)\n", "ca_0.set_parameter(name=\"kaq0\", initial=10)\n", "ca_0.set_parameter(name=\"Saq0\", initial=1e-4)\n", "ca_0.seriesinwell(name=\"Ln-2\", element=w_0, t=t1, h=h1)\n", "ca_0.series(name=\"Ln-3\", x=r, y=0, layer=0, t=t2, h=h2)\n", "ca_0.fit()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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optimalstdperc_stdpminpmaxinitialparray
kaq01.1661122.924550e-030.250795-infinf10.0000[1.1661123343530744]
Saq00.0000091.158463e-071.234779-infinf0.0001[9.381941955415476e-06]
\n", "
" ], "text/plain": [ " optimal std perc_std pmin pmax initial \\\n", "kaq0 1.166112 2.924550e-03 0.250795 -inf inf 10.0000 \n", "Saq0 0.000009 1.158463e-07 1.234779 -inf inf 0.0001 \n", "\n", " parray \n", "kaq0 [1.1661123343530744] \n", "Saq0 [9.381941955415476e-06] " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "RMSE: 0.010236292742416775\n" ] } ], "source": [ "display(ca_0.parameters)\n", "print(\"RMSE:\", ca_0.rmse())" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hm1_0 = w_0.headinside(t1)\n", "hm2_0 = ml_0.head(r, 0, t2, layers=0)\n", "plt.semilogx(t1, h1 / H0, \".\", label=\"obs ln-2\")\n", "plt.semilogx(t1, hm1_0[0] / H0, label=\"ttim ln-2\")\n", "plt.semilogx(t2, h2 / H0, \".\", label=\"obs ln-3\")\n", "plt.semilogx(t2, hm2_0[0] / H0, label=\"ttim ln-3\")\n", "plt.xlabel(\"time [d]\")\n", "plt.ylabel(\"Normalized Head: h/H0\")\n", "plt.title(\"Model Results - Single layer model\")\n", "plt.legend()\n", "plt.grid()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In general, the single-layer model seems to be performing well, with a good visual fit between observations and the model." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 8. Calibration with well skin resistance\n", "\n", "Now we test if the skin resistance of the well has an impact on model calibration. Therefore, we add the ```res``` parameter in the calibration settings. We use the one-layer model." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ".....................................................\n", "Fit succeeded.\n", "[[Fit Statistics]]\n", " # fitting method = leastsq\n", " # function evals = 50\n", " # data points = 162\n", " # variables = 3\n", " chi-square = 0.01256033\n", " reduced chi-square = 7.8996e-05\n", " Akaike info crit = -1527.29887\n", " Bayesian info crit = -1518.03608\n", "[[Variables]]\n", " kaq0: 1.24189423 +/- 0.01095307 (0.88%) (init = 10)\n", " Saq0: 9.0457e-06 +/- 1.0985e-07 (1.21%) (init = 0.0001)\n", " res: 0.02258043 +/- 0.00297322 (13.17%) (init = 0)\n", "[[Correlations]] (unreported correlations are < 0.100)\n", " C(kaq0, res) = +0.9661\n", " C(kaq0, Saq0) = -0.5091\n", " C(Saq0, res) = -0.4049\n" ] } ], "source": [ "# unknown parameters: kaq, Saq, res\n", "ca_1 = ttm.Calibrate(ml_0)\n", "ca_1.set_parameter(name=\"kaq0\", initial=10)\n", "ca_1.set_parameter(name=\"Saq0\", initial=1e-4)\n", "ca_1.set_parameter_by_reference(name=\"res\", parameter=w_0.res, initial=0)\n", "ca_1.seriesinwell(name=\"Ln-2\", element=w_0, t=t1, h=h1)\n", "ca_1.series(name=\"Ln-3\", x=r, y=0, layer=0, t=t2, h=h2)\n", "ca_1.fit(report=True)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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optimalstdperc_stdpminpmaxinitialparray
kaq01.2418941.095307e-020.881965-infinf10.0000[1.241894225226266]
Saq00.0000091.098451e-071.214331-infinf0.0001[9.045727748956226e-06]
res0.0225802.973222e-0313.167253-infinf0.0000[0.02258042554072732]
\n", "
" ], "text/plain": [ " optimal std perc_std pmin pmax initial \\\n", "kaq0 1.241894 1.095307e-02 0.881965 -inf inf 10.0000 \n", "Saq0 0.000009 1.098451e-07 1.214331 -inf inf 0.0001 \n", "res 0.022580 2.973222e-03 13.167253 -inf inf 0.0000 \n", "\n", " parray \n", "kaq0 [1.241894225226266] \n", "Saq0 [9.045727748956226e-06] \n", "res [0.02258042554072732] " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "RMSE: 0.008805278496246407\n" ] } ], "source": [ "display(ca_1.parameters)\n", "print(\"RMSE:\", ca_1.rmse())" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hm1_1 = w_0.headinside(t1)\n", "hm2_1 = ml_0.head(r, 0, t2, layers=0)\n", "plt.semilogx(t1, h1 / H0, \".\", label=\"obs ln-2\")\n", "plt.semilogx(t1, hm1_1[0] / H0, label=\"ttim ln-2\")\n", "plt.semilogx(t2, h2 / H0, \".\", label=\"obs ln-3\")\n", "plt.semilogx(t2, hm2_1[0] / H0, label=\"ttim ln-3\")\n", "plt.xlabel(\"time [d]\")\n", "plt.ylabel(\"Normalized Head: h/H0\")\n", "plt.title(\"Model Results - Single layer model with res\")\n", "plt.legend()\n", "plt.grid()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Adding well screen resistance does not improve the performance significantly, while the AIC value increases. Thus, it is recommended to leave it out of the model." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 11. Analysis and comparison of simulated values\n", "\n", "We now compare the values in TTim and also add the results of the modelling done in AQTESOLV and MLU by Yang (2020)." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Comparison of parameter values and error under different models
 k [m/d]Ss [1/m]RMSE [m]
MLU1.3110000.0000080.010373
AQTESOLV1.1660000.0000090.009151
ttim1.1661120.0000090.022986
ttim_skin1.1661120.0000090.008805
\n" ], "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ta = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\"],\n", " index=[\"MLU\", \"AQTESOLV\", \"ttim\", \"ttim_skin\"],\n", ")\n", "ta.loc[\"AQTESOLV\"] = [1.166, 9.368e-06]\n", "ta.loc[\"MLU\"] = [1.311, 8.197e-06]\n", "ta.loc[\"ttim\"] = ca_0.parameters[\"optimal\"].values\n", "ta.loc[\"ttim_skin\"] = ca_0.parameters[\"optimal\"].values[:2]\n", "ta[\"RMSE [m]\"] = [0.010373, 0.009151, ca_0.rmse(), ca_1.rmse()]\n", "ta.style.set_caption(\"Comparison of parameter values and error under different models\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The parameters in every model closely match each other. The error was also very similar." ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "## References\n", "\n", "* Butler, J.J., Jr., 1998. The Design, Performance, and Analysis of Slug Tests, Lewis Publishers, Boca Raton, Florida, 252p.\n", "* Hyder, Z., Butler Jr, J.J., McElwee, C.D., Liu, W., 1994. Slug tests in partially penetrating wells. Water Resources Research 30, 2945–2957.\n", "* Duffield, G.M., 2007. AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", "* Yang, Xinzhu (2020) Application and comparison of different methodsfor aquifer test analysis using TTim. Master Thesis, Delft University of Technology (TUDelft), Delft, The Netherlands." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.4" } }, "nbformat": 4, "nbformat_minor": 4 }