Artificial Intelligence/Machine Learning in Support of Subsurface Exploration & Production
Dr. Bruno de Ribet, an expert in the oil and gas E&P industry, explains how machine learning and Industrial AI have led to deeper insights into relationships in geological data, helping to understand the risks and prospectivity of reservoirs.
Through mitigating risk, the ML techniques offered by AspenTech help enhance user confidence when characterizing a reservoir or planning new well placement.
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A customer in Eastern Europe needed an accurate assessment of effective oil-saturated thicknesses in a major development project. This case study explains how machine learning-based algorithms were used to analyze complex borehole and seismic studies employing a set of unique classification approaches embedded in Aspen SeisEarth™ and Aspen Geolog Facimage™.
Data Sheet:
Neural Network Inversion (NNI) in Aspen SeisEarth™
Article:
Using a Self-growing Neural Network Approach to CCS Monitoring
Brochure:
Aspen SeisEarth™