Wolfcamp Example - Bulk Process las files

This example shows the full petrophysical workflow avaiable in PetroPy for a folder of wolfcamp las file courtesy of University Lands Texas.

The workflow progresses in 3 iterations to edit data, calculate the petrophysical model, and to calculate statistics and save. To better understand the worflow first read through the single well example.

The First Iteration

1. Read las file and create a petropy.Log object
2. Create a petropy.LogViewer show in edit_mode to fix data
3. Writes updated data to new folder via petropy.Log.write()

The Second Iteration

1. Read las file and create a petropy.Log object
2. Load tops from a csv file using petropy.Log.tops_from_csv()

3. Calculate fluid properties by

   1. Loading parameters via petropy.Log.fluid_properties_parameters_from_csv()
   2. Calculating over formations via petropy.Log.formation_fluid_properties()

4. Calculate mulitmineral properties by

   1. Loading parameters via petropy.Log.multimineral_parameters_from_csv()
   2. Calculating over formations via petropy.Log.formation_multimineral_model()

5. Curve summations via petropy.Log.summations()
6. Adding pay flags via petropy.Log.add_pay_flag()

After the second iteration, the list processed_logs is used to perform electrofacies calculations via petropy.electrofacies()

The Third Iteration

1. Exporting log statistics via petropy.Log.statistics()
2. Saving LogViewer to png and Log to las

import petropy as ptr

# use glob to get file paths
import glob

# import pyplot to add logo to figure
import matplotlib.pyplot as plt


# define parameters                           #

f = ['WFMPA', 'WFMPB', 'WFMPC']
sum_curves = ['OIP', 'BVH', 'PHIE']
stats_curves = ['OIP', 'BVH', 'PHIE', 'SW', 'VCLAY', 'TOC']

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)]

# get paths for all las files in folder       #
# wfmp_raw_las                                #

raw_las_folder = r'wfmp_raw_las/'
las_files = glob.glob(raw_las_folder + r'*.las')


### First Iteration ###

for las_file in las_files:


    ### 1. read raw las file ###

    # create a Log object by reading a file path  #

    log = ptr.Log(las_file)

    ### 2. graphically edit raw 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

    # 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(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('.', '')

    # save changes to edited folder in case   #
    # reprocessing after editing is needed    #

    edited_log_file_name = 'wfmp_edited_las/%s_edited.las' % well_name
    # log.write(edited_log_file_name)

### Second Iteration ###

edited_file_folder = r'wfmp_edited_las/'
edited_las_files = glob.glob(edited_file_folder + r'*.las')

# create list to hold processed log objects   #

processed_logs = []

for las_file in edited_las_files:

    ### 1. Read las file ###

    log = ptr.Log(las_file)

    ### 2. load tops ###

    log.tops_from_csv('wfmp_raw_las/wfmp_tops.csv')

    ### 3. 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')

    ### 4. 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')

    ### 5. summations ###

    log.summations(f, curves = sum_curves)

    ### 6. pay flags ###

    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)

    # add log to processed_logs list              #

    processed_logs.append(log)


### 9. electrofacies ###

# define curves to use in electofaceis module #

electro_logs = ['GR_N', 'RESDEEP_N', 'NPHI_N', 'RHOB_N', 'PE_N']

# specifiy to use logarithmically scaled RESDEEP_N #

scaled = ['RESDEEP_N']

# calculate electrofacies for the processed logs #
final_logs = ptr.electrofacies(processed_logs, f,
                               electro_logs, 6, log_scale = scaled)


### Third Iteration ###

for log in final_logs:

    ### 1. export statistics ###

    log.statistics_to_csv('wfmp_processed_las/wfmp_statistics.csv',
                          replace = True, formations = f,
                          curves = stats_curves)

    ### 2. 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'wfmp_processed_las/%s_processed.png' % well_name
    las_file_name = r'wfmp_processed_las/%s_processed.las' % well_name

    viewer.fig.savefig(viewer_file_name)
    viewer.log.write(las_file_name)