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app.py
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from cmath import nan
import lasio
import pathlib
from numpy.core.fromnumeric import mean
import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
from fpdf import FPDF
from tempfile import NamedTemporaryFile
import tempfile
import streamlit.components.v1 as components
import striplog
from striplog import Legend, Lexicon, Interval, Component, Decor
import missingno as ms
litho='b'
limestone_strip = Decor({'component': Component({'hatch':litho}), 'hatch': litho, 'colour': '#eeeeee'}).plot(fmt="{hatch}")
# st.pyplot(limestone_strip)
sns.set(style='ticks')
st.set_option('deprecation.showfileUploaderEncoding', False)
st.title('Welcome to Plot Petrophysics!')
st.text('Plot your LAS 2.0 file into a triple combo and/or formation evaluation plots.\n(c) 2021, Aditya Arie Wijaya\n=============================')
st.write('Find the source code in [**my Github repo**] (https://github.com/ariewjy/triple_combo_web_plotter) and reach me out in [**LinkedIn**] (www.linkedin.com/in/adityaariewijaya89)')
st.markdown('Support this web application by [donating as low as 1 USD] (https://ko-fi.com/plotpetrophysics)')
# st.write('Please reload when stucked. Enjoy!')
st.title('LAS File Data')
mode = st.radio(
"Select an option:",
('Upload File', 'Use Preloaded File')
)
if mode == 'Upload File':
file = st.file_uploader('Upload the LAS file')
if file is not None:
tfile = tempfile.NamedTemporaryFile(delete=False)
tfile.write(file.read())
las_file = lasio.read(tfile.name)
las_df=las_file.df()
if mode == 'Use Preloaded File':
file = '42303347740000.las'
las_file = lasio.read(file)
las_df=las_file.df()
if file:
las_df.insert(0, 'DEPTH', las_df.index)
las_df.reset_index(drop=True, inplace=True)
try:
well_name = las_file.header['Well'].WELL.value
start_depth = las_df['DEPTH'].min()
stop_depth = las_df['DEPTH'].max()
step = abs(las_file.header['Well'].STEP.value)
company_name = las_file.header['Well'].COMP.value
date = las_file.header['Well'].DATE.value
curvename = las_file.curves
except:
well_name = 'unknown'
start_depth = 0.00
stop_depth = 10000.00
step = abs(las_df['DEPTH'][1]-las_df['DEPTH'][0])
company_name = 'unknown'
date = 'unknown'
curvename = las_file.curves
st.subheader('Well Information')
st.text(f'================================================\nWell Name : {well_name}')
st.text(f'Start Depth : {start_depth}')
st.text(f'Stop Depth : {stop_depth}')
st.text(f'Step : {step}')
st.text(f'Company : {company_name}')
st.text(f'Logging Date : {date}')
# st.subheader('Curve Information')
# st.text(f'================================================\n{curvename}')
# st.subheader('Curve Data Overview')
# st.markdown(f'The value on the left figure is number of rows. White space in each column of curve is a missing value rows/data. Expand to see more details')
# st.pyplot(ms.matrix(las_df, sparkline=False, labels=100).figure)
# for item in las_file.well:
# st.text(f"{item.descr} ({item.mnemonic} {item.unit}): {item.value}")
st.title('Selecting Curves')
curves = las_df.columns.values
if 'GR' in curves:
gr_col = las_df.columns.get_loc('GR')
else:
gr_col = 0
if 'ILD' in curves:
res_col = las_df.columns.get_loc('ILD')
else:
res_col = 0
if 'RHOB' in curves:
den_col = las_df.columns.get_loc('RHOB')
else:
den_col = 0
if 'NPHI' in curves:
neu_col = las_df.columns.get_loc('NPHI')
else:
neu_col = 0
gr_curve = st.selectbox('select the GAMMA RAY curve', curves, index=gr_col)
res_curve = st.selectbox('select the RESISTIVITY curve', curves, index=res_col)
den_curve = st.selectbox('select the BULK DENSITY curve', curves, index=den_col)
neu_curve = st.selectbox('select the NEUTRON POROSITY curve', curves, index=neu_col)
curve_list = [gr_curve, res_curve, den_curve, neu_curve]
#==========================
st.sidebar.title('Plot Setting')
well_name = st.sidebar.text_input('Well Name',value =(well_name))
well_df = las_df
curve_names = curve_list
top_depth = st.sidebar.number_input('Top Depth', min_value=0.00, value=(start_depth), step=100.00)
bot_depth = st.sidebar.number_input('Bottom Depth', min_value=0.00, value=(stop_depth), step=100.00)
plot_h = 17
plot_w = 12
title_size = 12
title_height = 1.0
line_width = 1
st.sidebar.title('Gamma Ray Logs')
gr_color = 'green'
gr_trackname = f'Gamma Ray ({gr_curve})'
gr_left = st.sidebar.slider('Gamma Ray Left Scale', min_value=0, value=0, step=10)
gr_right = st.sidebar.slider('Gamma Ray Right Scale', min_value=0, value=200, max_value=300, step=10)
gr_cutoff = st.sidebar.slider('Gamma Ray Cutoff', min_value=0, value=60)
gr_base = st.sidebar.slider('Gamma Ray Base', min_value=0, value=0)
gr_sand = st.sidebar.radio('Reservoir Colour',['gold','yellow', 'none'])
gr_shale = st.sidebar.radio('Non-Reservoir Colour',['lime','gray','none'])
if gr_right == 150:
gr_div = 6
else:
gr_div=5
st.sidebar.title('Resistivity Logs')
res_color = 'purple'
res_trackname = f'Resistivity ({res_curve})'
res_left = st.sidebar.number_input('Resistivity Left Scale', min_value=0.0001, max_value=1000000.0000, value=0.2)
res_right = st.sidebar.number_input('Resistivity Right Scale', min_value=0.0001, max_value=1000000.0000, value = 20000.0000)
res_cutoff = st.sidebar.number_input('Resistivity Cutoff', min_value=0.01, max_value=1000.00, value=100.00)
res_shading = st.sidebar.radio('Resistivity Shading',['none','lightcoral', 'lightgreen'])
st.sidebar.title('Density Logs')
den_color = 'red'
den_trackname = f'Density ({den_curve})'
den_left = st.sidebar.number_input('Density Left Scale', min_value=0.00, value=1.95, step=0.05)
den_right = st.sidebar.number_input('Density Right Scale', max_value=3.00, value=2.95, step=0.05)
st.sidebar.title('Neutron Logs')
neu_color = 'blue'
neu_trackname = f'Neutron ({neu_curve})'
neu_mean = np.nanmean(las_df[str(neu_curve)])
if neu_mean < 1 :
neu_left = st.sidebar.number_input('Neutron Left Scale', min_value=-50.00, value=0.45)
neu_right = st.sidebar.number_input('Neutron Right Scale', min_value=-50.00, value=-0.15)
if neu_mean > 1:
neu_left = st.sidebar.number_input('Neutron Left Scale', min_value=-50.00, value=45.00)
neu_right = st.sidebar.number_input('Neutron Right Scale', min_value=-50.00, value=-15.00)
den_neu_div = st.sidebar.radio('Number of Division:',[4,5])
dn_xover = st.sidebar.radio('D-N Colour',['yellow','gold','none'])
dn_sep = st.sidebar.radio('N-D Colour',['lightgray','green', 'none'])
#=================
st.title('Triple Combo Plot')
fig, ax = plt.subplots(figsize=(plot_w,plot_h))
fig.suptitle(f"Triple Combo Plot\n===================\nWell: {well_name}\n(Interval: {top_depth} - {bot_depth})\n===================\n ---(c) Aditya Arie Wijaya,2021---\nhttps://github.com/ariewjy\n===================",
size=title_size, y=title_height)
gr_log=las_df[curve_list[0]]
res_log=las_df[curve_list[1]]
den_log=las_df[curve_list[2]]
neu_log=las_df[curve_list[3]]
#Set up the plot axes
ax1 = plt.subplot2grid((1,3), (0,0), rowspan=1, colspan = 1)
ax2 = plt.subplot2grid((1,3), (0,1), rowspan=1, colspan = 1)
ax3 = plt.subplot2grid((1,3), (0,2), rowspan=1, colspan = 1)
ax4 = ax3.twiny() #Twins the y-axis for the density track with the neutron track
#adding top border
ax7 = ax1.twiny()
ax7.xaxis.set_visible(False)
ax8 = ax2.twiny()
ax8.xaxis.set_visible(False)
ax9 = ax3.twiny()
ax9.xaxis.set_visible(False)
# Gamma Ray track
ax1.plot(gr_log, "DEPTH", data = well_df, color = gr_color, lw=line_width)
ax1.set_xlabel(gr_trackname)
ax1.minorticks_on()
ax1.set_xlim(gr_left, gr_right)
ax1.set_ylim(bot_depth, top_depth)
ax1.xaxis.label.set_color(gr_color)
ax1.tick_params(axis='x', colors=gr_color)
ax1.spines["top"].set_edgecolor(gr_color)
ax1.spines["top"].set_position(("axes", 1.02))
ax1.set_xticks(list(np.linspace(gr_left, gr_right, num = gr_div)))
ax1.grid(which='major', color='silver', linestyle='-')
ax1.grid(which='minor', color='lightgrey', linestyle=':', axis='y')
ax1.xaxis.set_ticks_position("top")
ax1.xaxis.set_label_position("top")
##area-fill sand and shale from gr
ax1.fill_betweenx(well_df['DEPTH'], gr_base, gr_log, where=(gr_cutoff >= gr_log), interpolate=True, color = gr_sand, linewidth=0, alpha=0.8)
ax1.fill_betweenx(well_df['DEPTH'], gr_base, gr_log, where=(gr_cutoff <= gr_log), interpolate=True, color = gr_shale, linewidth=0, alpha=0.8)
# RES track
ax2.plot(res_log, "DEPTH", data = well_df, color = res_color, lw=line_width)
ax2.set_xlabel(res_trackname)
ax2.set_xlim(res_left, res_right)
ax2.set_ylim(bot_depth, top_depth)
ax2.semilogx()
ax2.minorticks_on()
ax2.xaxis.grid(which='minor', linestyle=':', linewidth='0.5', color='gray')
ax2.xaxis.label.set_color(res_color)
ax2.tick_params(axis='x', colors=res_color)
ax2.spines["top"].set_edgecolor(res_color)
ax2.spines["top"].set_position(("axes", 1.02))
ax2.grid(which='major', color='silver', linestyle='-')
ax2.grid(which='minor', color='lightgrey', linestyle=':', axis='y')
ax2.xaxis.set_ticks_position("top")
ax2.xaxis.set_label_position("top")
ax2.fill_betweenx(well_df['DEPTH'], res_cutoff, res_log, where=(res_log >= res_cutoff), interpolate=True, color = res_shading, linewidth=0)
# Density track
ax3.plot(den_log, "DEPTH", data = well_df, color = den_color, lw=line_width)
ax3.set_xlabel(den_trackname)
# ax3.minorticks_on()
ax3.set_xlim(den_left, den_right)
ax3.set_ylim(bot_depth, top_depth)
ax3.xaxis.label.set_color(den_color)
ax3.tick_params(axis='x', colors=den_color)
ax3.spines["top"].set_edgecolor(den_color)
ax3.spines["top"].set_position(("axes", 1.02))
ax3.set_xticks(list(np.linspace(den_left, den_right, num = (den_neu_div+1))))
ax3.grid(which='major', color='silver', linestyle='-')
ax3.grid(which='minor', color='lightgrey', linestyle=':', axis='y')
ax3.xaxis.set_ticks_position("top")
ax3.xaxis.set_label_position("top")
# Neutron trak placed ontop of density track
ax4.plot(neu_log, "DEPTH", data = well_df, color = neu_color, lw=line_width)
ax4.set_xlabel(neu_trackname)
ax4.minorticks_on()
ax4.xaxis.label.set_color(neu_color)
ax4.set_xlim(neu_left, neu_right)
ax4.set_ylim(bot_depth, top_depth)
ax4.tick_params(axis='x', colors=neu_color)
ax4.spines["top"].set_position(("axes", 1.08))
ax4.spines["top"].set_visible(True)
ax4.spines["top"].set_edgecolor(neu_color)
ax4.set_xticks(list(np.linspace(neu_left, neu_right, num = (den_neu_div+1))))
#shading between density and neutron
x1=den_log
x2=neu_log
x = np.array(ax3.get_xlim())
z = np.array(ax4.get_xlim())
nz=((x2-np.max(z))/(np.min(z)-np.max(z)))*(np.max(x)-np.min(x))+np.min(x)
ax3.fill_betweenx(well_df['DEPTH'], x1, nz, where=x1>=nz, interpolate=True, color=dn_sep, linewidth=0, alpha=0.8)
ax3.fill_betweenx(well_df['DEPTH'], x1, nz, where=x1<=nz, interpolate=True, color=dn_xover, linewidth=0, alpha=0.8)
plt.tight_layout()
plt.show()
st.pyplot(fig)
#download feature
#exporting as pdf
pdf = FPDF()
pdf.add_page()
with NamedTemporaryFile(delete=False, suffix=".png") as tmpfile:
fig.savefig(tmpfile.name)
pdf.image(tmpfile.name, 10, 10, (plot_w*16), (plot_h*16))
st.download_button(
"Download Triple Combo Plot as PDF",
data=pdf.output(dest='S').encode('latin-1'),
file_name=f"{well_name}_triple_combo.pdf",
)
st.title(' ')
####### VSH-------------...
if file:
formevalmode = st.checkbox("Formation Evaluation Module")
if formevalmode:
st.title('Formation Evaluation')
#sidebar-vsh
st.sidebar.title('Volume of Shale')
mode = st.sidebar.radio(
"Calculate VSH from:",
('Gamma-Ray', 'Density-Neutron')
)
if mode == 'Gamma-Ray':
gr_min = st.sidebar.number_input('GR at 0% Shale', min_value=0, value=10, step=10)
gr_max = st.sidebar.number_input('GR at 100% Shale', min_value=0, value=150, max_value=300, step=10)
# st.write(gr_log)
vsh_log = (gr_log - gr_min)/(gr_max-gr_min) * 100
vsh_log = np.clip(vsh_log, 0, 100)
vsh_color = 'black'
well_df['VSH'] = vsh_log
if neu_mean > 1 :
neu_log = neu_log/100
else:
neu_log=neu_log
if mode == 'Density-Neutron':
denmat = st.sidebar.number_input('Matrix-Density', min_value=1.0, value=2.65, step=0.1)
denfl = st.sidebar.number_input('Fluid-Density', min_value=0.0, value=1.0, max_value=1.5, step=0.1)
dphi = (denmat - den_log)/(denmat-denfl)
den_shale = st.sidebar.number_input('Density at 100% Shale', min_value=1.0, value=2.7, step=0.1)
dphi_shale = (den_shale - den_log)/(den_shale-denfl)
dphi_shale = np.clip(dphi_shale, 0, 1)
neu_shale = st.sidebar.number_input('Neutron at 100% Shale', min_value=0.0, value=0.35, step=0.1)
neu_mean = neu_log.mean()
vsh_log = (neu_log - dphi)/(neu_shale-dphi_shale) *100
vsh_log = np.clip(vsh_log, 0, 100)
vsh_color = 'black'
well_df['VSH'] = vsh_log
shale_shading = st.sidebar.radio('Shale Shading',['green','gray'])
sand_shading = st.sidebar.radio('Sand/Carbonate Shading',['Sandstone','Carbonate'])
vsh_trackname = f'Vshale {mode} (%)\n'
st.sidebar.title('Coal Flag')
coal_flag = st.sidebar.checkbox('Coal Flag?')
if coal_flag:
neu_coal = st.sidebar.number_input('Neutron Coal', value = 0.25, step = 0.05)
den_coal = st.sidebar.number_input('Density Coal', value = 2.00, step = 0.05)
coal_index = np.where((neu_log>=neu_coal) & (den_log<=den_coal), 1, 0)
else:
coal_index = np.nan
well_df['COAL'] = coal_index
#sidebar-porosity
st.sidebar.title('Porosity')
mode = st.sidebar.radio(
"Choose the Porosity Method",
('Density-Neutron', 'Density')
)
if mode == 'Density':
density_mat = st.sidebar.number_input('Matrix Density', min_value=1.0, value=2.65, step=0.1)
density_fluid = st.sidebar.number_input('Fluid Density', min_value=0.0, value=1.0, max_value=1.5, step=0.1)
dphi_log = (density_mat - den_log)/(density_mat-density_fluid)
tpor_log = dphi_log
tpor_log = np.clip(tpor_log, 0.001, 1)
tpor_log = np.where((coal_index ==1), 0.001, tpor_log)
epor_log = tpor_log*(1-vsh_log/100)
epor_log = np.clip(epor_log, 0.001, 1)
epor_log = np.where((coal_index ==1), 0.001, epor_log)
if mode == 'Density-Neutron':
density_mat = st.sidebar.number_input('Matrix Density', min_value=1.0, value=2.65, step=0.1)
density_fluid = st.sidebar.number_input('Fluid Density', min_value=0.0, value=1.0, max_value=1.5, step=0.1)
dphi_log = (density_mat - den_log)/(density_mat-density_fluid)
dnphi_log = ((dphi_log**2 + neu_log**2)/2)**0.5
tpor_log = dnphi_log
tpor_log = np.clip(tpor_log, 0.001, 1)
tpor_log = np.where((coal_index ==1), 0.001, tpor_log)
epor_log = tpor_log*(1-vsh_log/100)
epor_log = np.clip(epor_log, 0.001, 1)
epor_log = np.where((coal_index ==1), 0.001, epor_log)
mode = st.sidebar.radio(
"Porosity to Display",
('Effective Porosity', 'Total Porosity')
)
if mode == 'Total Porosity':
por_log = tpor_log*100
if mode == 'Effective Porosity':
por_log = epor_log*100
well_df['TPOR'] = tpor_log
well_df['EPOR'] = epor_log
por_left = st.sidebar.number_input('Left Scale', min_value=0, max_value=100, value=35, step=10)
por_right = st.sidebar.number_input('Right Scale', min_value=0, max_value=100, value=0, step=10)
por_grid = st.sidebar.number_input('Number of Grids', min_value = 0, value=8, step =1)
por_color = 'black'
por_shading = st.sidebar.radio('Total Porosity Shading',['aqua','none'])
# sand_shading = st.sidebar.radio('Sand Shading',['gold','yellow'])
por_trackname = f'Porosity (p.u.)\n'
#sidebar-Sw
st.sidebar.title('Water Saturation')
rw_input = st.sidebar.checkbox("Input Rw")
if rw_input:
water_sal = st.sidebar.number_input ('Input Salinity in NaCl ppm', min_value=0, value =25000)
fm_temp = st.sidebar.number_input('Formation Temperature in Fahrenheit', min_value=10, value = 75)
rw_calc = (400000 / fm_temp / water_sal) ** 0.88
st.sidebar.subheader(f'The Calculated Rw = {round(rw_calc,3)} ohm-m')
rw = st.sidebar.number_input('Formation Water Resistivity (Rw)', min_value=0.0, value=0.05, step=0.01)
a_value = st.sidebar.number_input('Turtuosity Factor (a)', min_value=0.0, value=1.0, max_value=10.0, step=0.1)
m_value = st.sidebar.number_input('Porosity Exponent (m)', min_value=0.0, value=2.0, max_value=10.0, step=0.1)
n_value = st.sidebar.number_input('Saturation Exponent (n)', min_value=0.0, value=2.0, max_value=10.0, step=0.1)
por_input = por_log/100
sw_log = (rw/(res_log*por_input**m_value))**(a_value/n_value)*100
sw_log = np.clip(sw_log, 0, 100)
sw_color = 'black'
hc_shading = st.sidebar.radio('Hydrocarbon Shading',['lime','coral'])
well_df['SW'] = sw_log
st.sidebar.title('Pay Flag')
pay_flag = st.sidebar.checkbox('Pay Flag')
if pay_flag:
# vsh_cutoff = st.sidebar.number_input('VSH Cutoff', value = 0.75, step = 0.05)
epor_cutoff = st.sidebar.number_input('EPOR Cutoff', value = 0.1, step = 0.05)
sw_cutoff = st.sidebar.number_input('SW Cutoff', value = 80, step = 5)
pay_index = np.where((epor_log>epor_cutoff) & (sw_log<sw_cutoff), 1, 0)
else:
pay_index = np.nan
well_df['PAY'] = pay_index
# shale_shading = st.sidebar.radio('Shale Shading',['green','gray'])
# sand_shading = st.sidebar.radio('Sand Shading',['gold','yellow'])
sw_left = 100
sw_right = 0
sw_trackname = f'Water Saturation (%)\n'
fig, ax = plt.subplots(figsize=(plot_w,plot_h))
fig.suptitle(f"Formation Evaluation Plot\n===================\nWell: {well_name}\n(Interval: {top_depth} - {bot_depth})\n===================\n ---(c) Aditya Arie W,2021---\nhttps://github.com/ariewjy\n===================",
size=title_size, y=title_height)
#Set up the plot axes
ax1 = plt.subplot2grid((1,3), (0,0), rowspan=1, colspan = 1)
ax2 = plt.subplot2grid((1,3), (0,1), rowspan=1, colspan = 1)
ax3 = plt.subplot2grid((1,3), (0,2), rowspan=1, colspan = 1)
if coal_flag:
ax4 = ax1.twiny() #Twins the y-axis for the density track with the neutron track
if pay_flag:
ax5 = ax3.twiny()
#adding top border
ax7 = ax1.twiny()
ax7.xaxis.set_visible(False)
ax8 = ax2.twiny()
ax8.xaxis.set_visible(False)
ax9 = ax3.twiny()
ax9.xaxis.set_visible(False)
# Vsh track
ax1.plot(vsh_log, "DEPTH", data = well_df, color = vsh_color, lw=line_width)
ax1.set_xlabel(vsh_trackname)
ax1.minorticks_on()
ax1.set_xlim(0, 100)
ax1.set_ylim(bot_depth, top_depth)
ax1.xaxis.label.set_color(vsh_color)
ax1.tick_params(axis='x', colors=vsh_color)
ax1.spines["top"].set_edgecolor(vsh_color)
ax1.spines["top"].set_position(("axes", 1.02))
ax1.set_xticks(list(np.linspace(0, 100, num = 5)))
ax1.grid(which='major', color='grey', linestyle='--')
ax1.grid(which='minor', color='lightgrey', linestyle='-.', axis='y')
ax1.xaxis.set_ticks_position("top")
ax1.xaxis.set_label_position("top")
##area-fill sand and shale for VSH
ax1.fill_betweenx(well_df['DEPTH'], 0, vsh_log, interpolate=False, color = shale_shading, linewidth=0, alpha=0.5, hatch = '=-')
if sand_shading == 'Carbonate':
ax1.fill_betweenx(well_df['DEPTH'], vsh_log, 100, interpolate=False, color = 'cornflowerblue', linewidth=0, alpha=0.5, hatch = 'b')
else:
ax1.fill_betweenx(well_df['DEPTH'], vsh_log, 100, interpolate=False, color = 'gold', linewidth=0, alpha=0.5, hatch = 'o')
# Porosity track
ax2.plot(por_log, "DEPTH", data = well_df, color = por_color, lw=line_width)
ax2.set_xlabel(por_trackname)
ax2.minorticks_on()
ax2.set_xlim(por_left, por_right)
ax2.set_ylim(bot_depth, top_depth)
ax2.xaxis.label.set_color(por_color)
ax2.tick_params(axis='x', colors=por_color)
ax2.spines["top"].set_edgecolor(por_color)
ax2.spines["top"].set_position(("axes", 1.02))
ax2.set_xticks(list(np.linspace(por_left, por_right, num = int(por_grid))))
ax2.grid(which='major', color='grey', linestyle='--')
ax2.grid(which='minor', color='lightgrey', linestyle='-.', axis='y')
ax2.xaxis.set_ticks_position("top")
ax2.xaxis.set_label_position("top")
##area-fill tpor and epor
ax2.fill_betweenx(well_df['DEPTH'], por_log, 0, interpolate=True, color = por_shading, linewidth=0, alpha=0.5)
# ax2.fill_betweenx(well_df['DEPTH'], vsh_log, 100, interpolate=True, color = sand_shading, linewidth=0)
#coal track
if coal_flag:
ax4.plot(coal_index, "DEPTH", data = well_df, color = 'black', lw=2)
ax4.set_xlabel('COAL')
ax4.minorticks_on()
ax4.xaxis.label.set_color('black')
ax4.set_xlim(1, 0)
ax4.set_ylim(bot_depth, top_depth)
ax4.tick_params(axis='x', colors='black')
ax4.spines["top"].set_position(("axes", 1.08))
ax4.spines["top"].set_visible(True)
ax4.spines["top"].set_edgecolor('black')
ax4.set_xticks(list(np.linspace(1, 0, num = 2)))
ax4.fill_betweenx(well_df['DEPTH'], coal_index, 0, interpolate=True, color = 'black', linewidth=0.0, alpha = 0.7)
# Sw track
ax3.plot(sw_log, "DEPTH", data = well_df, color = sw_color, lw=line_width)
ax3.set_xlabel(sw_trackname)
ax3.minorticks_on()
ax3.set_xlim(sw_left, sw_right)
ax3.set_ylim(bot_depth, top_depth)
ax3.xaxis.label.set_color(sw_color)
ax3.tick_params(axis='x', colors=sw_color)
ax3.spines["top"].set_edgecolor(sw_color)
ax3.spines["top"].set_position(("axes", 1.02))
ax3.set_xticks(list(np.linspace(sw_left, sw_right, num = 6)))
ax3.grid(which='major', color='grey', linestyle='--')
ax3.grid(which='minor', color='lightgrey', linestyle='-.', axis='y')
ax3.xaxis.set_ticks_position("top")
ax3.xaxis.set_label_position("top")
##area-fill sw
ax3.fill_betweenx(well_df['DEPTH'], 100, sw_log, interpolate=True, color = hc_shading, linewidth=0, alpha=0.5)
ax3.fill_betweenx(well_df['DEPTH'], sw_log, 0, interpolate=True, color = 'lightblue', linewidth=0, alpha=0.5)
if pay_flag:
ax5.plot(pay_index, "DEPTH", data = well_df, color = 'red', lw=2)
ax5.set_xlabel('PAY FLAG')
ax5.minorticks_on()
ax5.xaxis.label.set_color('red')
ax5.set_xlim(10, 0)
ax5.set_ylim(bot_depth, top_depth)
ax5.tick_params(axis='x', colors='black')
ax5.spines["top"].set_position(("axes", 1.08))
ax5.spines["top"].set_visible(True)
ax5.spines["top"].set_edgecolor('black')
ax5.set_xticks(list(np.linspace(10, 0, num = 2)))
ax5.fill_betweenx(well_df['DEPTH'], pay_index, 0, interpolate=True, color = 'red', linewidth=0.0, alpha = 0.7)
plt.tight_layout()
plt.show()
st.pyplot(fig)
#exporting as pdf
pdf = FPDF()
pdf.add_page()
with NamedTemporaryFile(delete=False, suffix=".png") as tmpfile:
fig.savefig(tmpfile.name)
pdf.image(tmpfile.name, 10, 10, (plot_w*16), (plot_h*16))
st.download_button(
"Download Formation Evaluation Plot as PDF",
data=pdf.output(dest='S').encode('latin-1'),
file_name=f"{well_name}_formation_eval.pdf",
)
well_df= well_df.query(f"`DEPTH` >= {top_depth} and `DEPTH` <= {bot_depth}")
st.markdown('**Final Result, Expand to See Full Data.**')
st.text('VSH, TPOR, EPOR, and SW are in the Last Right 4 Columns')
st.write (well_df)
st.title('Downloading Final Result as CSV')
st.markdown('**REMARKS**: _The CSV file will include input LAS data_ **AND** _Formation Evaluation Result: Volume of Shale (%), Porosity (dec), and Water Saturation (%) at the above depth interval_')
#exporting as CSV
@st.cache
def convert_df(df):
return df.to_csv().encode('utf-8')
csv = convert_df(well_df)
st.download_button(
"Download the Formation Evaluation CSV file",
csv,
f"{well_name}_formation_eval.csv",
"text/csv",
key='download-csv'
)
# Histogram
st.title('Histogram')
st.sidebar.title('Histogram')
well_df = well_df.drop('DEPTH', axis=1, inplace=False)
curve_hist = st.selectbox('select the curve for histogram', well_df.columns)
scale_hist_left = st.sidebar.number_input ('Left Scale',value= well_df[curve_hist].min())
scale_hist_right = st.sidebar.number_input ('Right Scale',value= well_df[curve_hist].max())
agree = st.sidebar.checkbox('Logarithmic Scale')
if agree:
log_value_hist = True
else:
log_value_hist = False
fig = px.histogram(well_df, x=curve_hist, log_x = log_value_hist, range_x=[scale_hist_left, scale_hist_right])
st.plotly_chart(fig)
# @st.cache
pdf = FPDF()
pdf.add_page()
with NamedTemporaryFile(delete=False, suffix=".png") as tmpfile:
fig.write_image(tmpfile.name)
pdf.image(tmpfile.name, 10, 10, (plot_w*16), (plot_h*8))
st.download_button(
"Download Histogram as PDF",
data=pdf.output(dest='S').encode('latin-1'),
file_name=f"{well_name}_histogram_{curve_hist}.pdf",
)
# Scatter Plot
st.title('Scatter Plot')
st.sidebar.title('Scatter Plot')
x_curve = st.selectbox('select the curve for X-axis', well_df.columns)
scale_x_left = st.sidebar.number_input ('Left Scale X-axis', value= well_df[x_curve].min())
scale_x_right = st.sidebar.number_input ('Right Scale X-axis', value = well_df[x_curve].max())
agreex = st.sidebar.checkbox('Logarithmic Scale on X')
if agreex:
log_valuex = True
else:
log_valuex=False
y_curve = st.selectbox('select the curve for Y-axis', well_df.columns)
scale_y_upper = st.sidebar.number_input ('Upper Scale Y-axis', value= well_df[y_curve].min())
scale_y_bottom = st.sidebar.number_input ('Bottom Scale Y-axis', value = well_df[y_curve].max())
agreey = st.sidebar.checkbox('Logarithmic Scale on Y')
if agreey:
log_valuey = True
else:
log_valuey=False
z_curve = st.selectbox('select the curve for Z-axis', well_df.columns)
scale_z_left = st.sidebar.number_input ('Bottom Scale Z-axis', value= 0)
scale_z_right = st.sidebar.number_input ('Upper Scale Z-axis', value = 100)
fig=px.scatter(well_df, x=x_curve, y=y_curve,log_y=log_valuey,log_x = log_valuex,
color = z_curve, range_x=[scale_x_left, scale_x_right], range_y = [scale_y_bottom, scale_y_upper],
color_continuous_scale=px.colors.sequential.Aggrnyl_r)
st.plotly_chart(fig)
# @st.cache
pdf = FPDF()
pdf.add_page()
with NamedTemporaryFile(delete=False, suffix=".png") as tmpfile:
fig.write_image(tmpfile.name)
pdf.image(tmpfile.name, 10, 10, (plot_w*16), (plot_h*8))
st.download_button(
"Download Scatter Plot as PDF",
data=pdf.output(dest='S').encode('latin-1'),
file_name=f"{well_name}_Crossplot_{x_curve}_{y_curve}_{z_curve}.pdf",
)