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%matplotlib inline
This example shows the full petrophysical workflow avaiable in PetroPy for a single wolfcamp las file courtesy of University Lands Texas.
The workflow progresses in these 11 steps
petropy.Log objectpetropy.Log.tops_from_csvpetropy.LogViewer show in edit_mode to fix datapetropy.Log.fluid_properties_parameters_from_csvpetropy.Log.formation_fluid_propertiespetropy.Log.multimineral_parameters_from_csvpetropy.Log.formation_multimineral_modelpetropy.Log.summationspetropy.Log.add_pay_flagpetropy.electrofaciespetropy.Log.statisticsTo bulk process a folder of las files at once, use the bulk example_ .
Downloading the script at the bottom of this webpage will not download the required las
file or PetroPy logo. To download all files, view the examples folder_ on GitHub.
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import petropy as ptr
# import pyplot to add logo to figure
import matplotlib.pyplot as plt
### 1. Read las file
# create a Log object by reading a file path #
las_file_path = '42303347740000.las'
log = ptr.Log(las_file_path)
### 2. load tops ###
tops_file_path = 'tops.csv'
log.tops_from_csv(tops_file_path)
### 3. graphically edit log ###
# use manual mode for fixing borehole washout #
# and other changes requiring redrawing data #
# use bulk shift mode to linearly adjust all #
# curve data #
# close both windows to continue program #
viewer = ptr.LogViewer(log, top = 6950, height = 100)
viewer.show(edit_mode = True)
# overwrite log variable with updated log #
# from LogViewer edits #
log = viewer.log
### 4. define formations ###
f = ['WFMPA', 'WFMPB', 'WFMPC']
### 5. fluid properties ###
# load fluid properties from a csv file #
# since path is not specified, load default #
# csv file included with petropy #
log.fluid_properties_parameters_from_csv()
# calculate fluid properties over defined #
# formations with parameter WFMP from #
# previously loaded csv #
log.formation_fluid_properties(f, parameter = 'WFMP')
### 6. multimineral model ###
# load multimineral parameters from csv file #
# since path is not specified, load default #
# csv file included with petropy #
log.multimineral_parameters_from_csv()
# calculate multiminearl model over defined #
# formations with parameter WFMP from #
# previously loaded csv #
log.formation_multimineral_model(f, parameter = 'WFMP')
### 7. summations ###
# define curves to calculate cumulative values #
c = ['OIP', 'BVH', 'PHIE']
# calculate cumulative values over formations #
log.summations(f, curves = c)
### 8. pay flags ###
# define pay flogs as list of tuples for #
# (curve, value) #
flag_1_gtoe = [('PHIE', 0.03)]
flag_2_gtoe = [('PAY_FLAG_1', 1), ('BVH', 0.02)]
flag_3_gtoe = [('PAY_FLAG_2', 1)]
flag_3_ltoe = [('SW', 0.2)]
# add pay flags over defined formations #
log.add_pay_flag(f, greater_than_or_equal = flag_1_gtoe)
log.add_pay_flag(f, greater_than_or_equal = flag_2_gtoe)
log.add_pay_flag(f, greater_than_or_equal = flag_3_gtoe,
less_than_or_equal = flag_3_ltoe)
### 9. electrofacies ###
# define curves to use in electofaceis module #
electro_logs = ['GR_N', 'RESDEEP_N', 'NPHI_N', 'RHOB_N', 'PE_N']
# make a list of Log objects as input #
logs = [log]
# calculate electrofacies for the defined logs#
# over the specified formations #
# finding 6 clusters of electrofacies #
# with RESDEEP_N logarithmically scaled #
logs = ptr.electrofacies(logs, f, electro_logs, 6,
log_scale = ['RESDEEP_N'])
# unpack log object from returned list #
log = logs[0]
### 10. statistics ###
# define list of curves to find statistics #
stats_curves = ['OIP', 'BVH', 'PHIE', 'SW', 'VCLAY', 'TOC']
# calculate stats over specified formation and#
# save to csv file wfmp_statistics.csv #
# update the line if the well, formation is #
# already included in the csv file #
log.statistics_to_csv('wfmp_statistics.csv', replace = True,
formations = f, curves = stats_curves)
### 11. export data ###
# find way to name well, looking for well name#
# or UWI or API #
if len(log.well['WELL'].value) > 0:
well_name = log.well['WELL'].value
elif len(str(log.well['UWI'].value)) > 0:
well_name = str(log.well['UWI'].value)
elif len(log.well['API'].value) > 0:
well_name = str(log.well['API'].value)
else:
well_name = 'UNKNOWN'
well_name = well_name.replace('.', '')
# scale height of viewer to top and bottom #
# of calculated values #
wfmpa_top = log.tops['WFMPA']
wfmpc_base = log.next_formation_depth('WFMPC')
top = wfmpa_top
height = wfmpc_base - wfmpa_top
# create LogViewer with the default full_oil #
# template included in petropy #
viewer = ptr.LogViewer(log, top = top, height = height,
template_defaults = 'full_oil')
# set viewer to 17x11 inches size for use in #
# PowerPoint or printing to larger paper #
viewer.fig.set_size_inches(17, 11)
# add well_name to title of LogViewer #
viewer.fig.suptitle(well_name, fontweight = 'bold', fontsize = 30)
# add logo to top left corner #
logo_im = plt.imread('company_logo.png')
logo_ax = viewer.fig.add_axes([0, 0.85, 0.2, 0.2])
logo_ax.imshow(logo_im)
logo_ax.axis('off')
# add text to top right corner #
if len(str(log.well['UWI'].value)) > 0:
label = 'UWI: ' + str(log.well['UWI'].value) + '\n'
elif len(log.well['API'].value) > 0:
label = 'API: ' + str(log.well['API'].value) + '\n'
else:
label = ''
label += 'County: Reagan\nCreated By: Todd Heitmann\n'
label += 'Creation Date: October 23, 2017'
viewer.axes[0].annotate(label, xy = (0.99,0.99),
xycoords = 'figure fraction',
horizontalalignment = 'right',
verticalalignment = 'top',
fontsize = 14)
# save figure and log #
viewer_file_name=r'%s_processed.png' % well_name
las_file_name = r'%s_processed.las' % well_name
viewer.fig.savefig(viewer_file_name)
viewer.log.write(las_file_name)