Cegal Prizm Python Tool Pro - Sequence identifier#

Identify fining up, coarsening up or blocky sequences within reservoir sands#

In this tutorial we will show you how to: 1. Connect to a Petrel project
2. Access well data from Petrel and turn it into a Dataframe
3. Use a simple linear regression based algorithm to identify sequences in a gamma ray log
4. Plot and QC the new sequence log (discrete log) using cegal.welltools package
5. Write the new sequence log back to Petrel

The purpose of this tutorial is to show how simple it is to pick up data from a Petrel project, define a simple logic or algorithm based workflow of your own design that is unavailable in Petrel and write the output back to your project.

Import modules#

[ ]:

from cegalprizm.pythontool import PetrelConnection
from cegalprizm.pythontool.welllog import WellLog, DiscreteWellLog
from cegal.welltools.plotting import CegalWellPlotter as cwp


import pandas as pd
import numpy as np
from scipy.stats import linregress
import plotly.express as px

Connect to Petrel project#

[7]:

petrel = PetrelConnection()

print('Connected to {}'.format(petrel.get_current_project_name()))

Connected to Gullfaks_dataset2.pet

Access well data#

  • we can use the petrel connection “Petrel” python object and the wells method to find which wells are available in the project and turn this to a python list

  • then we can access the 3rd well in this list using list indexing [2]

  • finally we can do some list comprehension to find the gamma ray log

[8]:

list_of_well_paths = list(petrel.wells.keys())
print(list_of_well_paths)

['Input/Wells/Injectors/C3', 'Input/Wells/Injectors/C2', 'Input/Wells/Producers/B8', 'Input/Wells/Producers/A10', 'Input/Wells/Producers/A15', 'Input/Wells/Injectors/C6', 'Input/Wells/Injectors/C4', 'Input/Wells/Producers/A16', 'Input/Wells/Injectors/C5', 'Input/Wells/Producers/B9']

[10]:

well = petrel.wells[list_of_well_paths[5]]
print(well)

Well(petrel_name="C6")

[11]:

gamma = [log for log in well.logs if type(log) is WellLog and 'Gamma' in str(log)][0]
gamma

[11]:

WellLog(petrel_name="Gamma")

Creating a dataframe of our well log#

  • this is simple and uses the logs_dataframe method on our well objectusing the welllog object called gamma

  • the dataframe includes the log (in this case GR), MD, TWT and TVD as columns

[12]:

logs_df = well.logs_dataframe(gamma)
logs_df

[12]:
Gamma MD TWT TVD
0 81.027946 1877.2223 1779.021825 1876.858053
1 85.427429 1877.7223 1779.495470 1877.357739
2 88.121590 1878.2223 1779.969411 1877.857736
3 88.684662 1878.7223 1780.443351 1878.357733
4 88.200562 1879.2223 1780.917291 1878.857729
... ... ... ... ...
1185 85.349678 2469.7223 2161.390165 2422.068200
1186 85.691147 2470.2223 2161.670309 2422.485010
1187 84.015839 2470.7223 2161.950453 2422.901820
1188 80.894783 2471.2223 2162.230598 2423.318630
1189 75.967026 2471.7223 2162.510742 2423.735440

1190 rows × 4 columns

Quick visualization#

  • we can quickly visualise the data using plotly and a histogram (note you can zoom in and navigate around the plot interactively)

[13]:

fig = px.histogram(logs_df, x=gamma.petrel_name)
fig.show()