Schlumberger Technology Corporation

Sensia LLC

Emilien Dupont

Velizar Vesselinov

Jose Celaya Galvan

Andrey Sergeevich Konchenko

Sergey Doronichev

Carlos Boneti

Erik Burton

Valerian Guillot

Suhas Suresha

Joseph Matthew Chalupsky

Jose Ramon Celaya Galvan

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

A system can include a processor; memory operatively coupled to the processor; and processor-executable instructions stored in the memory to instruct the system to: receive a marker on a well log for a well in a geographic region; and iteratively propagate the marker automatically to a plurality of well logs for other wells in the geographic region.

Well log correlation and propagation system

A method for rapid region wide production forecasting includes identifying base data of a well in a plurality of wells of a region; selecting, using the base data and from a set of a models comprising a rich machine learning model, a location based machine learning model, and a decline curve model, a well model; and generating, based on the selecting, a forecasted production of the well using the base data and the well model. The method further includes aggregating a plurality of forecasted productions of the plurality of wells, the plurality of forecasted productions including the forecasted production, to generate a region forecast using the rich machine learning model, the location based machine learning model, and the decline curve model; and presenting the region forecast.

Rapid region wide production forecasting

A method, apparatus, and program product utilize a disentangled factor learning framework to analyze petro-technical image data such as seismic image data to infer properties of a subsurface volume and/or to generate image data for use in training machine learning algorithms for use in petro-technical applications.

Disentanglement for inference on seismic data and generation of seismic data

A method includes receiving data where the data include data for a plurality of factors associated with a plurality of wells; training a regression model based at least in part on the data and the plurality of factors; outputting a trained regression model; and predicting production of a well via the trained regression model.

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Los Altos, CA, United States of America

Emilien Dupont