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  • Physics Embedded Machine Learning for Modeling and Optimization of Oil and Gas Assets

    By: Ferdinando Marfella Ferdinando Marfella , 4 years ago
    PHYSICS EMBEDDED MACHINE LEARNING FOR MODELING AND OPTIMIZATION OF OIL AND GAS ASSETS by Dr. Pallav Sarma

    Data Physics is the amalgamation of the state-of-the-art in machine learning and the same underlying physics present in reservoir simulators. These models can be created as efficiently as machine learning models, integrate all kinds of data, and can be evaluated orders of magnitude faster than full scale simulation models, and since they include similar underlying physics as simulators, they have good long term predictive capacity. We present applications of Data Physics models to a complex waterflood field in Argentina, wherein, the injectant is redistributed to maximize/minimize multiple objectives. A significant increase in actual incremental oil production and reduction in operational cost is demonstrated. Additonal applications to infill drilling optimization and subsurface back allocation are also discussed.

    BIO OF THE SPEAKER: Dr. Pallav Sarma is Co-Founder and Chief Scientist at Tachyus responsible for the modeling and optimization technologies underlying the Tachyus platform. He is a renowned expert in closed-loop reservoir management, with multiple patents and papers on various topics including simulation, optimization, data assimilation and machine learning. He has over 12 years of experience working for Chevron and Schlumberger prior to Tachyus. He holds a Ph.D. in Petroleum Engg. and a Ph.D. Minor in Operations Research from Stanford University and a B.Tech from Indian School of Mines.