{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# H2O-CO2 EOS calculations\n", "- DiadFit contains the EOS of Duan and Zhang (2006)\n", "- This code was converted from the C code from Yoshimura et al. (2023) - we thank them for making such a clear description of the mistakes in the original Duan and Zhang paper!\n", "https://doi.org/10.2465/jmps.221224a\n", "- There are two main types of calculations you might want to do with a mixed EOS.\n", "\n", "Example 1) If you work on fluid inclusions, its nice to assume they are a pure CO$_2$ fluid. But in reality, you probably have some H$_2$O in the fluid phase. You can calculate a pressure for a given XH2O value, assuming the H has been lost .\n", "\n", "Example 2) If you are an experimentalist, you probably know P and T, and you have some idea of XH2O. You might want to calculate molar volumes, densities, compressabilities, activities and fugacities. This is also useful for people doing EOS calculations for melt inclusion vapour bubbles etc. \n", "\n", "The two base functions in DiadFit are:\n", "\n", "a) pf.calculate_entrapment_P_XH2O - Calculates pressure for a known density, temperature and XH2O\n", "\n", "b) pf.calc_prop_knownP_EOS_DZ2006 - Calculates properties (density, fugacity, compressability, molar volumes) for a H2O-CO2 mixed fluid for a known pressure, temperature and XH2O \n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# If you havent already - install DiadFit.\n", "#!pip install DiadFit --upgrade " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- If you havent installed CoolProp, you will need that package too\n", "If you use conda, in your command line run:\n", "conda install -c conda-forge coolprop" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'0.0.91'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import DiadFit as pf\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import math\n", "pf.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Correcting fluid inclusions for H2O\n", "- The theory for this is layed out in Hansteen and Klugel (2008). In all but the most anhydrous, deep systems, the exsolved fluid phase will contain some H2O. It is generally assumed that this is lost from the fluid inclusion, so you are left with CO2 you have measured by Raman spectroscopy\n", "- First, you have to calculate the bulk density of the original CO2+H2O mix. Then, using this density, you can use the mixed EOS to calculate pressure. Here, we compare this to pure CO2 EOS. \n", " - You can make 2 different end member assumptions - either all H is lost or the H exists as a film. The math is layed out in this notebook incase of interest! \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1a: Correcting a single FI composition\n", "- Say you measured a CO2 density of 0.3, and you think XH2O=0.1 (perhaps based on melt inclusion data - you can get XH2O from VESical)\n", "- Lets compare the calculations" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " P_kbar_pureCO2_SW96 P_kbar_pureCO2_SP94 P_kbar_pureCO2_DZ06 \\\n", "0 1.055698 1.070557 1.052613 \n", "\n", " P_kbar_mixCO2_DZ06_Hloss P_kbar_mixCO2_DZ06_no_Hloss \\\n", "0 1.052613 1.052613 \n", "\n", " P Mix_Hloss/P Pure DZ06 P Mix_no_Hloss/P Pure DZ06 rho_mix_calc_Hloss \\\n", "0 1.0 1.0 0.3 \n", "\n", " rho_mix_calc_noHloss CO2_dens_gcm3 T_K XH2O \n", "0 0.3 0.3 1473.15 0 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Diff_EOS=pf.calculate_entrapment_P_XH2O(XH2O=0, CO2_dens_gcm3=0.3, T_K=1200+273.15, T_K_ambient=37+273.15 )\n", "Diff_EOS" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- We can see that the mixed fluid pressure is ~13% higher than doing the pure calculation if H is lost, and 12% higher if H2O is in a film" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1b - Visualizing changes across a wider space\n", "- You dont have to feed a single value into this function, you could do an entire spreadsheet of values. \n", "- Here, lets do calculations for different densities for a range of XH2O values" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "XH2O=np.linspace(0.0001, 0.2, 50) # 50 evenly spaced XH2O values\n", "\n", "df_01=pf.calculate_entrapment_P_XH2O(XH2O=XH2O, CO2_dens_gcm3=0.1, T_K=1200+273.15)\n", "df_02=pf.calculate_entrapment_P_XH2O(XH2O=XH2O, CO2_dens_gcm3=0.2, T_K=1200+273.15)\n", "df_03=pf.calculate_entrapment_P_XH2O(XH2O=XH2O, CO2_dens_gcm3=0.3, T_K=1200+273.15)\n", "df_05=pf.calculate_entrapment_P_XH2O(XH2O=XH2O, CO2_dens_gcm3=0.5, T_K=1200+273.15)\n", "df_07=pf.calculate_entrapment_P_XH2O(XH2O=XH2O, CO2_dens_gcm3=0.7, T_K=1200+273.15)\n", "df_09=pf.calculate_entrapment_P_XH2O(XH2O=XH2O, CO2_dens_gcm3=0.9, T_K=1200+273.15)\n", "df_11=pf.calculate_entrapment_P_XH2O(XH2O=XH2O, CO2_dens_gcm3=1.1, T_K=1200+273.15)\n", "\n", "# Create a dictionary to hold your density values and corresponding dataframe columns directly\n", "densities = {\n", " 0.1: df_01['P Mix_Hloss/P Pure DZ06'],\n", " 0.2: df_02['P Mix_Hloss/P Pure DZ06'],\n", " 0.3: df_03['P Mix_Hloss/P Pure DZ06'],\n", " 0.5: df_05['P Mix_Hloss/P Pure DZ06'],\n", " 0.7: df_07['P Mix_Hloss/P Pure DZ06'],\n", " 0.9: df_09['P Mix_Hloss/P Pure DZ06'],\n", " 1.1: df_11['P Mix_Hloss/P Pure DZ06'],\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "of course, you can write this in a loop if you want!" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Define CO2 densities to loop over\n", "CO2_densities = [0.1, 0.2, 0.3, 0.5, 0.7, 0.9, 1.1]\n", "\n", "# Initialize a dictionary to hold the dataframes\n", "dfs = {}\n", "\n", "# Loop over CO2 densities\n", "for density in CO2_densities:\n", " # Calculate entrapment_P_XH2O for each CO2 density\n", " df = pf.calculate_entrapment_P_XH2O(XH2O=XH2O, CO2_dens_gcm3=density, T_K=1200 + 273.15)\n", " # Store the dataframe in the dictionary with a key indicating the CO2 density\n", " densities[density] = df['P Mix_Hloss/P Pure DZ06']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Lets make a nice plot showing this" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.1\n", "0.2\n", "0.3\n", "0.5\n", "0.7\n", "0.9\n", "1.1\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib.colors as mcolors\n", "\n", "# Create figure and axes\n", "fig, ax1 = plt.subplots(1, 1, figsize=(5, 4))\n", "\n", "# Choose a colormap\n", "cmap = plt.cm.plasma_r\n", "\n", "# Create a Normalize object for your densities\n", "norm = mcolors.Normalize(vmin=min(densities.keys()), vmax=max(densities.keys()))\n", "\n", "# Create a ScalarMappable object with the normalization and colormap\n", "sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)\n", "sm.set_array([])\n", "\n", "# Plot each line on ax1 using the density as the key to retrieve the corresponding dataframe column\n", "for density, data in densities.items():\n", " print(density)\n", " ax1.plot(XH2O, data, label=f'{density} g/cm$^3$', color=cmap(norm(density)))\n", "\n", "ax1.legend()\n", "ax1.set_xlabel('X$_{H_{2}O}^{molar}$')\n", "ax1.set_ylabel('P Mix EOS/P Pure EOS')\n", "#fig.colorbar(sm, ax=ax1, label='Density (g/cm$^3$)')\n", "ax1.set_xticks([0, 0.05, 0.1, 0.15, 0.2])\n", "ax1.set_xlim([0, 0.2])\n", "ax1.set_ylim([1, 1.5])\n", "plt.show()\n", "fig.savefig('H2OCO2.png', dpi=200, bbox_inches='tight')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1c: Calculations on an entire spreadsheet\n", "- Lets say to start with you want to apply a correction for a constant XH2O to all fluid inclusion densities" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SampleDensity_g_cm3Temp in C
0FI10.11100
1FI20.41200
2FI30.71150
3FI41.11200
4FI51.51300
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" ], "text/plain": [ " Sample Density_g_cm3 Temp in C\n", "0 FI1 0.1 1100\n", "1 FI2 0.4 1200\n", "2 FI3 0.7 1150\n", "3 FI4 1.1 1200\n", "4 FI5 1.5 1300" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Load in the excel spreadsheet\n", "# Download it here - https://github.com/PennyWieser/DiadFit/blob/main/docs/Examples/EOS_calculations/FI_densities.xlsx\n", "df=pd.read_excel('FI_densities.xlsx')\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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P_kbar_pureCO2_SW96P_kbar_pureCO2_SP94P_kbar_pureCO2_DZ06P_kbar_mixCO2_DZ06_HlossP_kbar_mixCO2_DZ06_no_HlossP Mix_Hloss/P Pure DZ06P Mix_no_Hloss/P Pure DZ06rho_mix_calc_Hlossrho_mix_calc_noHlossCO2_dens_gcm3T_KXH2O
00.2187320.2173950.2187480.2422650.2411011.1075091.1021880.1045450.1040690.111000.1
11.2309501.2493531.2277821.3884401.3520691.1308521.1012290.4181820.4106680.412000.1
22.9653143.0038322.9814473.4645213.2539631.1620271.0914040.7318180.7091150.711500.1
38.8417328.8265109.10757811.2918809.6950141.2398331.0645001.1500001.0951271.112000.1
423.43650524.92488529.17748637.97286629.4350911.3014441.0088291.5681821.4706081.513000.1
\n", "
" ], "text/plain": [ " P_kbar_pureCO2_SW96 P_kbar_pureCO2_SP94 P_kbar_pureCO2_DZ06 \\\n", "0 0.218732 0.217395 0.218748 \n", "1 1.230950 1.249353 1.227782 \n", "2 2.965314 3.003832 2.981447 \n", "3 8.841732 8.826510 9.107578 \n", "4 23.436505 24.924885 29.177486 \n", "\n", " P_kbar_mixCO2_DZ06_Hloss P_kbar_mixCO2_DZ06_no_Hloss \\\n", "0 0.242265 0.241101 \n", "1 1.388440 1.352069 \n", "2 3.464521 3.253963 \n", "3 11.291880 9.695014 \n", "4 37.972866 29.435091 \n", "\n", " P Mix_Hloss/P Pure DZ06 P Mix_no_Hloss/P Pure DZ06 rho_mix_calc_Hloss \\\n", "0 1.107509 1.102188 0.104545 \n", "1 1.130852 1.101229 0.418182 \n", "2 1.162027 1.091404 0.731818 \n", "3 1.239833 1.064500 1.150000 \n", "4 1.301444 1.008829 1.568182 \n", "\n", " rho_mix_calc_noHloss CO2_dens_gcm3 T_K XH2O \n", "0 0.104069 0.1 1100 0.1 \n", "1 0.410668 0.4 1200 0.1 \n", "2 0.709115 0.7 1150 0.1 \n", "3 1.095127 1.1 1200 0.1 \n", "4 1.470608 1.5 1300 0.1 " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Lets perform a calculation for 10 mol% H2O (A mol fraction of 0.1)\n", "df_10perc=pf.calculate_entrapment_P_XH2O(XH2O=0.1, CO2_dens_gcm3=df['Density_g_cm3'], T_K=df['Temp in C'], T_K_ambient=37+273.15)\n", "df_10perc" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## But this isnt realistic - we know that XH2O will vary as a function of pressure, getting higher for the shallower pressure inclusions. \n", "- if you have melt inclusions for your system, you could use saturation pressures calculated in VESical, which returns XH2O, to determine how XH2O varies as a function of pressure\n", "- Here, we load in melt inclusion data from the 2018 eruption of Kilauea, where XH2O was calculated using MagmaSat" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Sat P (Bars)XCO2 (molar)
03100.959107
13600.963423
24300.968782
39700.986911
46000.978391
\n", "
" ], "text/plain": [ " Sat P (Bars) XCO2 (molar)\n", "0 310 0.959107\n", "1 360 0.963423\n", "2 430 0.968782\n", "3 970 0.986911\n", "4 600 0.978391" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Download excel file here! https://github.com/PennyWieser/DiadFit/blob/main/docs/Examples/EOS_calculations/FI_densities.xlsx\n", "df_MI=pd.read_excel('FI_densities.xlsx', sheet_name='MI_data')\n", "df_MI.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Lets see how XH2O varies with pressure" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x=df_MI['Sat P (Bars)']/10\n", "y=1-df_MI['XCO2 (molar)']\n", "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,5))\n", "ax1.plot(x, y, '.r', label='MI')\n", "# we only see FI with pressures of 20-100 MPa ish, \n", "filt=(x>10)& (x<100)\n", "Pf = np.poly1d(np.polyfit(x[filt], y[filt],\n", " 3))\n", "Px = np.linspace(10, 100, 101)\n", "ax2.plot(x[filt], y[filt], '.r')\n", "Py = Pf(Px)\n", "ax2.plot(Px, Py, '-r')\n", "ax2.set_ylim([0, 0.2])\n", "ax2.set_xlim([10, 105])\n", "ax1.set_ylabel('XH2O')\n", "ax1.set_xlabel('MI P (MPa)')\n", "ax2.set_ylabel('XH2O')\n", "ax2.set_xlabel('MI P (MPa)')\n", "ax1.legend()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Now we need to loop - so initially use the polynomial to guess a XH2O for each MI, then re-evaluate after we get a new pressure for the FI accounting for H2O" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Density_g_cm3
00.176605
10.180578
20.180858
30.147317
40.102414
\n", "
" ], "text/plain": [ " Density_g_cm3\n", "0 0.176605\n", "1 0.180578\n", "2 0.180858\n", "3 0.147317\n", "4 0.102414" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Load in FI data of DeVitre and Wieser (2024 - GPL) - https://github.com/PennyWieser/DiadFit/blob/main/docs/Examples/EOS_calculations/FI_densities.xlsx\n", "df_FI=pd.read_excel('FI_densities.xlsx', sheet_name='FI_data')\n", "df_FI.head()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# We start the iteration with a calculation at XH2O=0\n", "P_initial=pf.calculate_entrapment_P_XH2O(XH2O=0, CO2_dens_gcm3=df_FI['Density_g_cm3'], T_K=1200+273.15, T_K_ambient=37+273.15)\n", "P=P_initial['P_kbar_mixCO2_DZ06_Hloss']*100" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.02616126 0.02562257 0.02558634 0.031926 0.04871835 0.06122687\n", " 0.02328426 0.03806389 0.03871544 0.0275518 0.02906754 0.02620721\n", " 0.02759615 0.026134 0.02620046 0.025854 0.02618501 0.02637775\n", " 0.02890327 0.02612303 0.02944233 0.04910864 0.04902438 0.04347711\n", " 0.04346895 0.04301599 0.05048917 0.06524242 0.08810179 0.08907062\n", " 0.05684403 0.06110404 0.00952658 0.03477215 0.05515149 0.06786117\n", " 0.06472027 0.06472427 0.06215855 0.07779917 0.06735852 0.06625022\n", " 0.06652548 0.05867082 0.07294691 0.07104699 0.07350334 0.02173245\n", " 0.05160996 0.04294449 0.05336954 0.05315722 0.06499151 0.03809115\n", " 0.03076985 0.03732762 0.02468089 0.03068704 0.04238007 0.03711157\n", " 0.03314194 0.01761598 0.02266898 0.04853351 0.04925092 0.05023459\n", " 0.06987516 0.05483234 0.05707121 0.05062823 0.02252232 0.03709871\n", " 0.07250375 0.03585812 0.08084905 0.08065717 0.03585929 0.07264176\n", " 0.02499965 0.05609238 0.06596978 0.06112307 0.04234836 0.03814723\n", " 0.07123552 0.07877681 0.07340111 0.02225983 0.03828862 0.06231994\n", " 0.05520107 0.03846753 0.06337383 0.07552583 0.08978508 0.06171236\n", " 0.0328908 0.03684592 0.05771167 0.04135907 0.04889922 0.05924628\n", " 0.05839519 0.05342139 0.06106911 0.03691097 0.04167174 0.04217851\n", " 0.0236916 0.03990502 0.03841082 0.04062817 0.03704548 0.03532414\n", " 0.03878014 0.04318756 0.04230108 0.0417218 0.04535187 0.03569798\n", " 0.04024244 0.07309899 0.05973546]\n", "Iteration 1: XH2O_calc = 0.02616125591287602, Pressure_H_loss = 0.5718466673784245\n", "[0.02559573 0.02510724 0.02507439 0.03085334 0.04640834 0.05822758\n", " 0.02297478 0.03650191 0.03710402 0.02685868 0.02823934 0.02563742\n", " 0.02689902 0.025571 0.0256313 0.02531706 0.02561728 0.02579214\n", " 0.02808951 0.02556105 0.02858135 0.04677381 0.04669489 0.04151864\n", " 0.04151106 0.04109001 0.04806814 0.06207352 0.08456448 0.08554329\n", " 0.05406001 0.05811037 0.00847897 0.03346714 0.0524585 0.06459681\n", " 0.06157186 0.0615757 0.05911749 0.07429162 0.06411152 0.06304313\n", " 0.06330827 0.05579341 0.06953362 0.06768353 0.0700768 0.02144687\n", " 0.04912082 0.04102358 0.0507769 0.05057684 0.06183239 0.03652709\n", " 0.02979459 0.03582207 0.02425323 0.02971882 0.04049931 0.03562269\n", " 0.03196878 0.01683141 0.02239278 0.04623532 0.04690709 0.04782927\n", " 0.06654593 0.05215698 0.05427529 0.04819866 0.02225033 0.03561083\n", " 0.06910145 0.03446699 0.0773077 0.07711734 0.03446806 0.06923599\n", " 0.02454246 0.05334826 0.06277313 0.05812853 0.04046986 0.0365789\n", " 0.06786681 0.07525624 0.06997697 0.02199051 0.03670954 0.05927178\n", " 0.05250534 0.03687487 0.06028044 0.07205641 0.08626659 0.05869113\n", " 0.03173824 0.03537761 0.05488265 0.03955184 0.04657769 0.05634052\n", " 0.05553153 0.05082577 0.05807704 0.03543762 0.03984187 0.04031217\n", " 0.02335116 0.03820454 0.03682245 0.03887431 0.03556171 0.03397518\n", " 0.03716384 0.04124947 0.04042596 0.03988831 0.04326385 0.03431946\n", " 0.03851698 0.06968202 0.05680601]\n", "Iteration 2: XH2O_calc = 0.02559572820733616, Pressure_H_loss = 0.5715116449327785\n", "[0.02560767 0.02511735 0.02508438 0.03088906 0.04651978 0.05837955\n", " 0.02297882 0.03656614 0.0371713 0.02687578 0.02826257 0.02564952\n", " 0.02691629 0.02558285 0.02564337 0.02532795 0.0256293 0.02580485\n", " 0.02811206 0.02557286 0.02860616 0.04688682 0.04680756 0.04160776\n", " 0.04160014 0.04117707 0.04818654 0.06223356 0.08471682 0.08569333\n", " 0.05420023 0.05826205 0.00859845 0.03351594 0.05259343 0.06476065\n", " 0.061731 0.06173486 0.05927157 0.07445884 0.06427472 0.06320477\n", " 0.06347032 0.05593889 0.0697014 0.06785038 0.07024473 0.0214508\n", " 0.04924343 0.04111031 0.05090584 0.05070504 0.06199201 0.03659145\n", " 0.02982515 0.03588284 0.02426045 0.02974902 0.04058349 0.03568245\n", " 0.03201004 0.01686912 0.02239615 0.04634602 0.04702066 0.04794669\n", " 0.06671188 0.05229087 0.0544162 0.04831759 0.02225364 0.03567053\n", " 0.06926907 0.03452087 0.07747241 0.07728226 0.03452195 0.06940366\n", " 0.0245506 0.05348619 0.06293435 0.05828026 0.0405539 0.03664352\n", " 0.06803377 0.07542283 0.07014487 0.02199384 0.03677482 0.05942621\n", " 0.05264044 0.03694099 0.06043706 0.07222442 0.08641485 0.05884421\n", " 0.03177835 0.03543613 0.05502543 0.03963137 0.04668986 0.05648755\n", " 0.05567625 0.05095489 0.05822864 0.03549644 0.03992283 0.04039544\n", " 0.02335594 0.03827736 0.03688831 0.03895048 0.03562116 0.03402657\n", " 0.03723142 0.04133729 0.04050978 0.0399695 0.04336121 0.03437259\n", " 0.03859136 0.06984984 0.05695432]\n", "Iteration 3: XH2O_calc = 0.02560766818913658, Pressure_H_loss = 0.5715187138578254\n", "[0.02560742 0.02511715 0.02508418 0.03088787 0.04651441 0.05837187\n", " 0.02297877 0.03656349 0.03716849 0.02687536 0.02826192 0.02564926\n", " 0.02691586 0.0255826 0.02564312 0.02532773 0.02562905 0.02580457\n", " 0.02811143 0.02557261 0.02860544 0.04688135 0.04680211 0.0416037\n", " 0.04159609 0.04117313 0.04818076 0.06222549 0.08471028 0.08568697\n", " 0.05419318 0.05825438 0.00858488 0.03351412 0.05258668 0.06475244\n", " 0.06172297 0.06172683 0.05926377 0.07445089 0.06426654 0.06319664\n", " 0.06346218 0.05593154 0.06969317 0.06784212 0.07023652 0.02145075\n", " 0.0492374 0.04110639 0.05089943 0.05069868 0.06198396 0.0365888\n", " 0.02982419 0.03588039 0.02426033 0.02974808 0.04057972 0.03568005\n", " 0.03200859 0.01686732 0.02239611 0.04634069 0.04701516 0.04794097\n", " 0.06670363 0.05228418 0.05440911 0.04831177 0.0222536 0.03566814\n", " 0.06926083 0.03451878 0.07746477 0.0772746 0.03451987 0.06939543\n", " 0.02455046 0.05347926 0.06292624 0.05827259 0.04055014 0.03664086\n", " 0.06802552 0.07541497 0.07013666 0.02199379 0.03677212 0.0594184\n", " 0.05263368 0.03693824 0.06042915 0.0722163 0.08640862 0.05883647\n", " 0.03177695 0.0354338 0.05501824 0.03962787 0.04668444 0.05648012\n", " 0.05566895 0.05094847 0.05822097 0.03549409 0.03991925 0.04039173\n", " 0.02335587 0.03827424 0.03688558 0.03894717 0.03561878 0.03402461\n", " 0.03722859 0.04133331 0.04050604 0.03996591 0.04335667 0.03437054\n", " 0.03858816 0.06984162 0.05694682]\n", "Iteration 4: XH2O_calc = 0.025607415970367434, Pressure_H_loss = 0.5715185649873413\n", "[0.02560742 0.02511715 0.02508419 0.03088791 0.04651467 0.05837226\n", " 0.02297877 0.0365636 0.03716861 0.02687537 0.02826194 0.02564927\n", " 0.02691587 0.0255826 0.02564312 0.02532773 0.02562905 0.02580458\n", " 0.02811145 0.02557262 0.02860546 0.04688162 0.04680238 0.04160389\n", " 0.04159627 0.04117331 0.04818104 0.0622259 0.08471056 0.08568724\n", " 0.05419354 0.05825477 0.00858642 0.03351419 0.05258702 0.06475285\n", " 0.06172338 0.06172723 0.05926417 0.07445127 0.06426695 0.06319705\n", " 0.06346259 0.05593191 0.06969357 0.06784253 0.07023692 0.02145075\n", " 0.0492377 0.04110657 0.05089975 0.05069899 0.06198437 0.03658891\n", " 0.02982422 0.03588049 0.02426033 0.0297481 0.04057989 0.03568015\n", " 0.03200864 0.0168674 0.02239611 0.04634095 0.04701543 0.04794125\n", " 0.06670404 0.05228452 0.05440947 0.04831205 0.0222536 0.03566823\n", " 0.06926123 0.03451887 0.07746512 0.07727495 0.03451995 0.06939583\n", " 0.02455046 0.05347961 0.06292664 0.05827297 0.04055031 0.03664097\n", " 0.06802592 0.07541534 0.07013706 0.0219938 0.03677223 0.0594188\n", " 0.05263402 0.03693836 0.06042955 0.07221669 0.08640888 0.05883686\n", " 0.031777 0.03543389 0.0550186 0.03962803 0.0466847 0.05648049\n", " 0.05566931 0.05094879 0.05822136 0.03549419 0.03991941 0.04039189\n", " 0.02335588 0.03827437 0.03688569 0.03894731 0.03561887 0.03402468\n", " 0.03722871 0.04133349 0.0405062 0.03996607 0.04335688 0.03437062\n", " 0.03858829 0.06984202 0.0569472 ]\n", "Iteration 5: XH2O_calc = 0.025607421281928694, Pressure_H_loss = 0.5715185680676197\n", "[0.02560742 0.02511715 0.02508419 0.03088791 0.04651466 0.05837224\n", " 0.02297877 0.0365636 0.0371686 0.02687537 0.02826194 0.02564927\n", " 0.02691587 0.0255826 0.02564312 0.02532773 0.02562905 0.02580458\n", " 0.02811145 0.02557262 0.02860546 0.0468816 0.04680236 0.04160388\n", " 0.04159627 0.0411733 0.04818103 0.06222588 0.08471055 0.08568723\n", " 0.05419352 0.05825475 0.00858624 0.03351418 0.052587 0.06475283\n", " 0.06172336 0.06172721 0.05926415 0.07445125 0.06426693 0.06319703\n", " 0.06346256 0.05593189 0.06969355 0.06784251 0.0702369 0.02145075\n", " 0.04923768 0.04110656 0.05089973 0.05069898 0.06198435 0.0365889\n", " 0.02982422 0.03588049 0.02426033 0.0297481 0.04057988 0.03568014\n", " 0.03200864 0.0168674 0.02239611 0.04634094 0.04701542 0.04794123\n", " 0.06670402 0.0522845 0.05440945 0.04831204 0.0222536 0.03566823\n", " 0.06926121 0.03451886 0.07746511 0.07727494 0.03451994 0.06939581\n", " 0.02455046 0.05347959 0.06292662 0.05827295 0.0405503 0.03664097\n", " 0.0680259 0.07541532 0.07013704 0.0219938 0.03677222 0.05941878\n", " 0.052634 0.03693835 0.06042953 0.07221667 0.08640887 0.05883684\n", " 0.031777 0.03543389 0.05501858 0.03962802 0.04668469 0.05648048\n", " 0.0556693 0.05094878 0.05822134 0.03549418 0.0399194 0.04039189\n", " 0.02335588 0.03827437 0.03688569 0.03894731 0.03561887 0.03402468\n", " 0.0372287 0.04133348 0.0405062 0.03996606 0.04335687 0.03437062\n", " 0.03858829 0.069842 0.05694718]\n", "Iteration 6: XH2O_calc = 0.025607421172027175, Pressure_H_loss = 0.5715185679542237\n", "[0.02560742 0.02511715 0.02508419 0.03088791 0.04651466 0.05837224\n", " 0.02297877 0.0365636 0.0371686 0.02687537 0.02826194 0.02564927\n", " 0.02691587 0.0255826 0.02564312 0.02532773 0.02562905 0.02580458\n", " 0.02811145 0.02557262 0.02860546 0.0468816 0.04680236 0.04160388\n", " 0.04159627 0.0411733 0.04818103 0.06222588 0.08471055 0.08568723\n", " 0.05419352 0.05825475 0.00858626 0.03351418 0.052587 0.06475284\n", " 0.06172336 0.06172721 0.05926415 0.07445125 0.06426693 0.06319703\n", " 0.06346257 0.05593189 0.06969355 0.06784251 0.0702369 0.02145075\n", " 0.04923768 0.04110656 0.05089973 0.05069898 0.06198435 0.0365889\n", " 0.02982422 0.03588049 0.02426033 0.0297481 0.04057988 0.03568014\n", " 0.03200864 0.0168674 0.02239611 0.04634094 0.04701542 0.04794123\n", " 0.06670402 0.0522845 0.05440945 0.04831204 0.0222536 0.03566823\n", " 0.06926121 0.03451886 0.07746511 0.07727494 0.03451994 0.06939581\n", " 0.02455046 0.05347959 0.06292662 0.05827295 0.0405503 0.03664097\n", " 0.0680259 0.07541532 0.07013704 0.0219938 0.03677223 0.05941878\n", " 0.052634 0.03693835 0.06042953 0.07221668 0.08640887 0.05883684\n", " 0.031777 0.03543389 0.05501858 0.03962802 0.04668469 0.05648048\n", " 0.0556693 0.05094878 0.05822134 0.03549418 0.0399194 0.04039189\n", " 0.02335588 0.03827437 0.03688569 0.03894731 0.03561887 0.03402468\n", " 0.0372287 0.04133348 0.0405062 0.03996606 0.04335688 0.03437062\n", " 0.03858829 0.069842 0.05694718]\n", "Iteration 7: XH2O_calc = 0.025607421176073022, Pressure_H_loss = 0.5715185684384301\n", "[0.02560742 0.02511715 0.02508419 0.03088791 0.04651466 0.05837224\n", " 0.02297877 0.0365636 0.0371686 0.02687537 0.02826194 0.02564927\n", " 0.02691587 0.0255826 0.02564312 0.02532773 0.02562905 0.02580458\n", " 0.02811145 0.02557262 0.02860546 0.0468816 0.04680236 0.04160388\n", " 0.04159627 0.0411733 0.04818103 0.06222588 0.08471055 0.08568723\n", " 0.05419352 0.05825475 0.00858626 0.03351418 0.052587 0.06475284\n", " 0.06172336 0.06172721 0.05926415 0.07445125 0.06426693 0.06319703\n", " 0.06346257 0.0559319 0.06969355 0.06784251 0.0702369 0.02145075\n", " 0.04923768 0.04110656 0.05089973 0.05069898 0.06198435 0.0365889\n", " 0.02982422 0.03588049 0.02426033 0.0297481 0.04057988 0.03568014\n", " 0.03200864 0.0168674 0.02239611 0.04634094 0.04701542 0.04794123\n", " 0.06670402 0.0522845 0.05440945 0.04831204 0.0222536 0.03566823\n", " 0.06926121 0.03451886 0.07746511 0.07727494 0.03451994 0.06939581\n", " 0.02455046 0.05347959 0.06292662 0.05827295 0.0405503 0.03664097\n", " 0.06802591 0.07541532 0.07013704 0.0219938 0.03677223 0.05941878\n", " 0.052634 0.03693835 0.06042953 0.07221668 0.08640887 0.05883684\n", " 0.031777 0.03543389 0.05501858 0.03962802 0.04668469 0.05648048\n", " 0.0556693 0.05094878 0.05822134 0.03549418 0.0399194 0.04039189\n", " 0.02335588 0.03827437 0.03688569 0.03894731 0.03561887 0.03402468\n", " 0.0372287 0.04133348 0.0405062 0.03996606 0.04335688 0.03437062\n", " 0.03858829 0.069842 0.05694718]\n", "Iteration 8: XH2O_calc = 0.025607421158796995, Pressure_H_loss = 0.5715185674605416\n", "[0.02560742 0.02511715 0.02508419 0.03088791 0.04651466 0.05837224\n", " 0.02297877 0.0365636 0.0371686 0.02687537 0.02826194 0.02564927\n", " 0.02691587 0.0255826 0.02564312 0.02532773 0.02562905 0.02580458\n", " 0.02811145 0.02557262 0.02860546 0.0468816 0.04680236 0.04160388\n", " 0.04159627 0.0411733 0.04818103 0.06222588 0.08471055 0.08568723\n", " 0.05419352 0.05825475 0.00858626 0.03351418 0.052587 0.06475284\n", " 0.06172336 0.06172721 0.05926415 0.07445125 0.06426693 0.06319703\n", " 0.06346257 0.0559319 0.06969355 0.06784251 0.0702369 0.02145075\n", " 0.04923768 0.04110656 0.05089973 0.05069898 0.06198435 0.0365889\n", " 0.02982422 0.03588049 0.02426033 0.0297481 0.04057988 0.03568014\n", " 0.03200864 0.0168674 0.02239611 0.04634094 0.04701542 0.04794123\n", " 0.06670402 0.0522845 0.05440945 0.04831204 0.0222536 0.03566823\n", " 0.06926121 0.03451886 0.07746511 0.07727494 0.03451994 0.06939581\n", " 0.02455046 0.05347959 0.06292662 0.05827295 0.0405503 0.03664097\n", " 0.0680259 0.07541532 0.07013704 0.0219938 0.03677222 0.05941878\n", " 0.052634 0.03693835 0.06042953 0.07221668 0.08640887 0.05883684\n", " 0.031777 0.03543389 0.05501858 0.03962802 0.04668469 0.05648048\n", " 0.0556693 0.05094878 0.05822134 0.03549418 0.0399194 0.04039189\n", " 0.02335588 0.03827437 0.03688569 0.03894731 0.03561887 0.03402468\n", " 0.0372287 0.04133348 0.0405062 0.03996606 0.04335688 0.03437062\n", " 0.03858829 0.069842 0.05694718]\n", "Iteration 9: XH2O_calc = 0.025607421193687155, Pressure_H_loss = 0.5715185674965405\n", "[0.02560742 0.02511715 0.02508419 0.03088791 0.04651466 0.05837224\n", " 0.02297877 0.0365636 0.0371686 0.02687537 0.02826194 0.02564927\n", " 0.02691587 0.0255826 0.02564312 0.02532773 0.02562905 0.02580458\n", " 0.02811145 0.02557262 0.02860546 0.0468816 0.04680236 0.04160388\n", " 0.04159627 0.0411733 0.04818103 0.06222588 0.08471055 0.08568723\n", " 0.05419352 0.05825475 0.00858626 0.03351418 0.052587 0.06475284\n", " 0.06172336 0.06172721 0.05926415 0.07445125 0.06426693 0.06319703\n", " 0.06346257 0.0559319 0.06969355 0.06784251 0.0702369 0.02145075\n", " 0.04923768 0.04110656 0.05089973 0.05069898 0.06198435 0.0365889\n", " 0.02982422 0.03588049 0.02426033 0.0297481 0.04057988 0.03568014\n", " 0.03200864 0.0168674 0.02239611 0.04634094 0.04701542 0.04794123\n", " 0.06670402 0.0522845 0.05440945 0.04831204 0.0222536 0.03566823\n", " 0.06926121 0.03451886 0.07746511 0.07727494 0.03451994 0.06939581\n", " 0.02455046 0.05347959 0.06292662 0.05827296 0.0405503 0.03664097\n", " 0.0680259 0.07541532 0.07013704 0.0219938 0.03677222 0.05941878\n", " 0.052634 0.03693835 0.06042953 0.07221668 0.08640887 0.05883684\n", " 0.031777 0.03543389 0.05501858 0.03962802 0.04668469 0.05648048\n", " 0.0556693 0.05094878 0.05822134 0.03549418 0.0399194 0.04039189\n", " 0.02335588 0.03827437 0.03688569 0.03894731 0.03561887 0.03402468\n", " 0.0372287 0.04133348 0.0405062 0.03996606 0.04335688 0.03437062\n", " 0.03858829 0.069842 0.05694718]\n", "Iteration 10: XH2O_calc = 0.025607421192402738, Pressure_H_loss = 0.5715185684552788\n" ] } ], "source": [ "\n", "# Loop 10 times\n", "for _ in range(10):\n", " XH2O_calc = Pf(P)\n", " print(XH2O_calc)\n", " Pressure_H_loss = pf.calculate_entrapment_P_XH2O(XH2O=XH2O_calc, CO2_dens_gcm3=df_FI['Density_g_cm3'], \n", " T_K=1200 + 273.15, T_K_ambient=37 + 273.15).P_kbar_mixCO2_DZ06_Hloss\n", " P = Pressure_H_loss * 100 # Update P for the next iteration\n", " # Optional: Print values to track changes in each iteration\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'XH2O')" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "## Lets compare results as a function of pressure\n", "fig, (ax2) = plt.subplots(1, 1, figsize=(10,5))\n", "\n", "\n", "ax2.plot( XH2O_calc, P_initial['P_kbar_pureCO2_DZ06']*100/P, '.r')\n", "ax2.set_ylabel('P (Pure CO2/Mixed EOS)')\n", "ax2.set_xlabel('XH2O')\n", "#ax1.plot([0, 100], [0, 100], '-k')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Calculating properties of mixed fluid. \n", "- Say you are an experimentalist, or working on another problem where you know P, T, XH2O, but want to calculate the properties of the mixed H2O-CO2 fluid (density, compressability, fugacity, molar volumes etc)\n", "- Download an example spreadsheet of the known conditions (e.g., experimental conditions here)\n", "https://github.com/PennyWieser/DiadFit/blob/main/docs/Examples/EOS_calculations/Exp_Cond.xlsx" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ExperimentP_kbarXH2OT_K
0test11.00.11200
1test22.00.21400
2test34.00.0800
3test41.00.01500
4test50.51.01200
\n", "
" ], "text/plain": [ " Experiment P_kbar XH2O T_K\n", "0 test1 1.0 0.1 1200\n", "1 test2 2.0 0.2 1400\n", "2 test3 4.0 0.0 800\n", "3 test4 1.0 0.0 1500\n", "4 test5 0.5 1.0 1200" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## Lets load in a spreadsheet of experimental conditions - Get from here: https://github.com/PennyWieser/DiadFit/blob/main/docs/Examples/EOS_calculations/Exp_Cond.xlsx\n", "df_exp=pd.read_excel('Exp_Cond.xlsx')\n", "df_exp.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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P_kbarT_KXH2OXCO2Molar Volume (cm3/mol)Density (g/cm3)Compressability_factorfugacity_H2O (kbar)fugacity_CO2 (kbar)activity_H2Oactivity_CO2
01.012000.10.9125.3335000.3303191.2561790.0929451.1632750.1009540.900294
12.014000.20.886.9921170.4460171.4946770.4249962.6683230.2134290.804635
24.08000.01.045.1532350.9744602.7153420.00000017.1982130.0000001.000000
31.015000.01.0155.0859050.2837141.2435020.0000001.2642440.0000001.000000
40.512001.00.0191.1788320.0941530.9580630.4770240.0000001.0000000.000000
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" ], "text/plain": [ " P_kbar T_K XH2O XCO2 Molar Volume (cm3/mol) Density (g/cm3) \\\n", "0 1.0 1200 0.1 0.9 125.333500 0.330319 \n", "1 2.0 1400 0.2 0.8 86.992117 0.446017 \n", "2 4.0 800 0.0 1.0 45.153235 0.974460 \n", "3 1.0 1500 0.0 1.0 155.085905 0.283714 \n", "4 0.5 1200 1.0 0.0 191.178832 0.094153 \n", "\n", " Compressability_factor fugacity_H2O (kbar) fugacity_CO2 (kbar) \\\n", "0 1.256179 0.092945 1.163275 \n", "1 1.494677 0.424996 2.668323 \n", "2 2.715342 0.000000 17.198213 \n", "3 1.243502 0.000000 1.264244 \n", "4 0.958063 0.477024 0.000000 \n", "\n", " activity_H2O activity_CO2 \n", "0 0.100954 0.900294 \n", "1 0.213429 0.804635 \n", "2 0.000000 1.000000 \n", "3 0.000000 1.000000 \n", "4 1.000000 0.000000 " ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_calc=pf.calc_prop_knownP_EOS_DZ2006(P_kbar=df_exp['P_kbar'], T_K=df_exp['T_K'], XH2O=df_exp['XH2O'])\n", "df_calc.head()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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P_kbarT_KXH2OXCO2Molar Volume (cm3/mol)Density (g/cm3)Compressability_factorfugacity_H2O (kbar)fugacity_CO2 (kbar)activity_H2Oactivity_CO2
0314000.20.867.0072140.5790421.7269520.6957615.2831010.2235670.806015
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" ], "text/plain": [ " P_kbar T_K XH2O XCO2 Molar Volume (cm3/mol) Density (g/cm3) \\\n", "0 3 1400 0.2 0.8 67.007214 0.579042 \n", "\n", " Compressability_factor fugacity_H2O (kbar) fugacity_CO2 (kbar) \\\n", "0 1.726952 0.695761 5.283101 \n", "\n", " activity_H2O activity_CO2 \n", "0 0.223567 0.806015 " ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# You can also do the calc for a single condition, say a melt inclusion where you know pressure, temp and XH2O from a solubility model\n", "df_calc2=pf.calc_prop_knownP_EOS_DZ2006(P_kbar=3, T_K=1400, XH2O=0.2)\n", "df_calc2" ] } ], "metadata": { "kernelspec": { "display_name": "base", "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": "3.11.5" } }, "nbformat": 4, "nbformat_minor": 2 }