Schlumberger Technology Corporation

Joseph Matthew Chalupsky

Emilien Dupont

Suhas Suresha

Vishakh Hegde

John Z Pang

A method, apparatus, and program product utilize a super resolution machine learning model to reconstruct high resolution seismic data from low resolution seismic data in connection with generating seismic visualizations, e.g., to reduce storage and/or communication costs associated with generating seismic visualizations.

Super resolution machine learning model for seismic visualization generation

Various computer-implemented methods for utilizing a modified upper confidence bound (UCB) in an agent-simulator environment in well placement planning for oil fields are disclosed herein. A set of well placement sequences for placing well in a geographical region may be received, where each well placement sequent defines a sequence of multiple oil wells to be placed within the geographical region. A computer-implemented simulation may be executed on each of the well placement sequences to determine, for each of the well placement sequences, a reward based upon a calculated hydrocarbon recovery and a cost of the calculated hydrocarbon recovery. The well placement sequences may be iteratively selected for the computer-implemented simulations using the modified UCB algorithm and based upon the rewards determined for each of the plurality of well placement sequences.

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Houston, TX, United States of America

Joseph Matthew Chalupsky