Well report

This is an auto-generated report showing some summary plots followed by a standard set of plots for every well.

#papermill_description=Initialise_Report
import numpy as np
import pandas as pd
import cegalprizm.investigator as investigator
from cegalprizm.investigator import InvestigatorConnection
from cegalprizm.investigator.views import *

from cegalprizm.investigator import DimensionPropertyNameTuple

required_dimensions = []
required_dimensions.append(DimensionPropertyNameTuple(name="Porosity", property_name="porosity"))
required_dimensions.append(DimensionPropertyNameTuple(name="Permeability", property_name="perm"))
required_dimensions.append(DimensionPropertyNameTuple(name="Gamma ray", property_name="gamma"))
required_dimensions.append(DimensionPropertyNameTuple(name="Z", property_name="depth"))
required_dimensions.append(DimensionPropertyNameTuple(name="Facies", property_name="facies"))

#papermill_description=Load_Investigation
inv_conn = InvestigatorConnection(use_licensed_features=True)
inv = inv_conn.load_investigation(investigation_id='fc8e6397-6311-4d57-8082-b242c17cb9ce')

if inv is None:
  sys.exit("Investigation cannot be accessed")

continuous_dimension_names = [ ]
discrete_dimension_names = [ ]

for required_dimension in required_dimensions:
  for dimension in inv.continuous_dimension_property_names:
    settings = inv.get_continuous_settings(dimension.name)
    name = dimension.name
    found = False
    if required_dimension.property_name in dimension.property_name.lower():
      name = required_dimension.name
      found = True

    if found:
      continuous_dimension_names.append(name)
      settings.set_name(name)

  for dimension in inv.discrete_dimension_property_names:
    if dimension.name is not investigator.DATASET_DIMENSION_NAME:
      settings = inv.get_discrete_settings(dimension.name)
      name = dimension.name
      found = False
      if required_dimension.property_name in dimension.property_name.lower():
        name = required_dimension.name
        found = True

      if found:
        discrete_dimension_names.append(name)
        settings.set_name(name)

inv.update()

#papermill_description=Verify_Investigation
import sys

#if len(continuous_dimension_names) + len(discrete_dimension_names) != len(required_dimensions):
#  sys.exit("The required dimensions are not available in the investigation")

raw_wells = [x for x in inv.dataset_names if x.startswith("Wells")]
well_names = [x.replace("Wells/", "") for x in raw_wells]

settings = inv.get_continuous_settings("Z")
settings.set_bin_size(10)
inv.update()

continuous_dimension_names = [x for x in continuous_dimension_names if x != "Z"]

for well in raw_wells:
    settings = inv.get_dataset_settings(well)
    settings.set_shape("circlefill", 6)
inv.update()

Overview map

A map showing the well head locations for each well in this report.

#papermill_description=Write_Overview
view = MapView(inv)
view.show_boreholes(True, True, True)
investigator.plot(view)

A matrix plot of the continuous data for all wells.

view = MatrixView(inv)
view.set_datasets_visible(raw_wells)
view.set_dimensions(continuous_dimension_names)
view.set_count_axis("percentage")
view.show_histograms(True)
view.show_legend(True)
investigator.plot(view)

logtracks_view = LogTracksView(inv)
logtracks_view.group_tracks_by("property")
logtracks_view.show_continuous_fill(True)

matrix_view = MatrixView(inv)
matrix_view.set_dimensions(continuous_dimension_names)
matrix_view.set_color_by(investigator.DATASET_DIMENSION_NAME)
matrix_view.set_count_axis("percentage")
matrix_view.show_histograms(True)
matrix_view.show_histogram_as("bars")
matrix_view.show_legend(True)

multi_histo_view = HistogramView(inv)
multi_histo_view.show_multi_histograms(True)
multi_histo_view.set_dimensions(continuous_dimension_names)
multi_histo_view.set_count_axis("percentage")
multi_histo_view.set_draw_statistics(True)
multi_histo_view.show_histogram_as("bars")
multi_histo_view.show_legend(True)

facies_proportion_view = HistogramView(inv)
facies_proportion_view.set_dimension("Z")
facies_proportion_view.set_count_axis("proportional")
facies_proportion_view.set_color_by("Facies")
facies_proportion_view.show_histogram_as("bars")
facies_proportion_view.swap_axes()
facies_proportion_view.show_legend(True)

#papermill_description=Write_Well_QC_B2

B2 QC

The data from the B2 well are plotted in different ways to allow QC of the individual well and comparison between this wells and the other wells in the investigation.

A well section showing log data.

view = logtracks_view.copy()
view.set_boreholes(['B2'])
investigator.plot(view, width=400)

A matrix plot of the continuous properties.

view = matrix_view.copy()
view.set_datasets_visible(['Wells/B2'])
investigator.plot(view)

A multi-dimension histogram showing the continuous data for each log plotted alongside the same data from all the other wells in the investigation.

view = multi_histo_view.copy()
view.set_highlight('B2')
investigator.plot(view)

A histogram showing the proportion of facies in 10m TVD intervals for the well.

view = facies_proportion_view.copy()
view.set_datasets_visible(['Wells/B2'])
investigator.plot(view)

#papermill_description=Write_Well_QC_B4

B4 QC

The data from the B4 well are plotted in different ways to allow QC of the individual well and comparison between this wells and the other wells in the investigation.

A well section showing log data.

view = logtracks_view.copy()
view.set_boreholes(['B4'])
investigator.plot(view, width=400)

A matrix plot of the continuous properties.

view = matrix_view.copy()
view.set_datasets_visible(['Wells/B4'])
investigator.plot(view)

A multi-dimension histogram showing the continuous data for each log plotted alongside the same data from all the other wells in the investigation.

view = multi_histo_view.copy()
view.set_highlight('B4')
investigator.plot(view)

A histogram showing the proportion of facies in 10m TVD intervals for the well.

view = facies_proportion_view.copy()
view.set_datasets_visible(['Wells/B4'])
investigator.plot(view)

#papermill_description=Write_Well_QC_B8

B8 QC

The data from the B8 well are plotted in different ways to allow QC of the individual well and comparison between this wells and the other wells in the investigation.

A well section showing log data.

view = logtracks_view.copy()
view.set_boreholes(['B8'])
investigator.plot(view, width=400)

A matrix plot of the continuous properties.

view = matrix_view.copy()
view.set_datasets_visible(['Wells/B8'])
investigator.plot(view)

A multi-dimension histogram showing the continuous data for each log plotted alongside the same data from all the other wells in the investigation.

view = multi_histo_view.copy()
view.set_highlight('B8')
investigator.plot(view)

A histogram showing the proportion of facies in 10m TVD intervals for the well.

view = facies_proportion_view.copy()
view.set_datasets_visible(['Wells/B8'])
investigator.plot(view)

#papermill_description=Write_Well_QC_B9

B9 QC

The data from the B9 well are plotted in different ways to allow QC of the individual well and comparison between this wells and the other wells in the investigation.

A well section showing log data.

view = logtracks_view.copy()
view.set_boreholes(['B9'])
investigator.plot(view, width=400)

A matrix plot of the continuous properties.

view = matrix_view.copy()
view.set_datasets_visible(['Wells/B9'])
investigator.plot(view)

A multi-dimension histogram showing the continuous data for each log plotted alongside the same data from all the other wells in the investigation.

view = multi_histo_view.copy()
view.set_highlight('B9')
investigator.plot(view)

A histogram showing the proportion of facies in 10m TVD intervals for the well.

view = facies_proportion_view.copy()
view.set_datasets_visible(['Wells/B9'])
investigator.plot(view)