Abstract:
Machine learning has excellent potential to predict rock types and depositional
trends at a sub-centimeter scale using borehole data in oil and gas wells. The use of
machine learning calculations, especially core plug imagery summarized by the RGB
log, improves the accuracy and speed while predicting lithology and facies at the sub centimeter level, thus offering a more detailed view of the relationship of depositional
trends within oil and gas wells. The required dataset includes core plug extracted from
the wells and well-log data acquired through different tools in run in boreholes from six
wells in the lower Indus basin. Core plugs are the only subsurface data that is true to
geologic scale and inherent heterogeneity. The research employs a rock-type driven
labeling scheme and a rock depositional process focused classification scheme to
interpret the training data from core plugs at a sub-centimeter scale. To generate
predictions for lithology and facies, an “RGB log” (RGBL) is developed to summarize
the core plug image at each depth step. The use of RGBL data has generated even more
accurate results and requires far less computing power than core image data. On the
other hand, it is anticipated that well-log data will continue to be inadequate in
predicting rock types or depositional trends at the sub-centimeter level due to logging
speed and step interval. To overcome this challenge, multiple curves are used as inputs
for activation functions to predict rock types from well-log data with signatures of
encountered rock types. The study demonstrates the potential to transform large
quantities of photographed core into a normalized digital format for geologic insights.
The methodology involves a machine learning workflow developed in python;
employed for the analysis of core image data in a scalable and reproducible manner.
This approach can be extended to other geologic basins with similar clastic depositional
trends, provided there is an abundance of photographed core plugs. RandomForest and
GradientBoosting were used to estimate the facies using well log data; RandomForest
was slightly higher in accuracy at 87.1% compared to GradientBoosting's 85.7%. Using
RGB log data, MLP-SVM predicted facies with an overall accuracy of 92.31%, with
metrics for precision, recall, and F1 score of 0.96, 0.92, and 0.93, respectively. This
research sets the stage for future research and analysis of borehole and core data for
geologically consistent facies prediction.
