Rock facies identification plays a key role in the exploration and development of O&G. It is important for reservoir modelling as different rocks have different permeability and ﬂuid saturation for a given porosity. Knowing the rock type distribution throughout the reservoir remains the source of uncertainty in such reservoir modelling.
Geologists traditionally identified rock facies by looking at core samples. There is a high cost to it. Rock facies classification is also done by indirect measurements such as data from wireline logs. Experts use proprietary and expensive software. Such traditional analytical methods are tedious, expensive, and error-prone when human interpretation is involved.
COULD THERE BE A BETTER WAY?
Techniques such as machine learning can now be used to draw patterns from known data, learn from it, and reliably predict unknowns. In this case, data from the wireline log was used. Some rocks had the facies classes assigned. The algorithm was trained to understand patterns between measurements called input features, with the facies class being the target output. Once trained, the algorithm was used to assign facies classes to rocks that had not been classified thereby predicting the rock type distribution throughout the reservoir.
The advantage was a higher accuracy with continuously increasing data. The AI was able to identify patterns and relationships which might be invisible to the human eye. We were able to extract valuable information in just a few clicks.
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ABOUT THE AUTHOR
Dr. Subrata Biswas is a data scientist and a geologist skilled in sedimentation and tectonics, mapping, scientific computing, quantitative modelling, and database and software engineering.
With a Ph.D. from the University of Vienna and 20 years' experience, he is well versed in ML/AI, software engineering, GIS technologies, remote sensing, image processing, and high-performance computing.
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