Schlumberger Technology Corporation

Services Petroliers Schlumberger

Geoquest Systems B.v.

Suhas Suresha

Jose Celaya Galvan

Crispin Chatar

Sergey Doronichev

Emilien Dupont

Carlos Boneti

Soumya Gupta

Vishakh Hegde

Joseph Matthew Chalupsky

Anthony Lichnewsky

Indranil Roychoudhury

Laetitia Shao

A subsurface structure identification system includes one or more processors and a memory coupled to the one or more processors. The memory is encoded with instructions that when executed by the one or more processors cause the one or more processors to provide a convolutional neural network trained to identify a subsurface structure in an input migrated seismic volume, and to partition the input migrated seismic volume into multi-dimensional sub-volumes of seismic data. The instructions also cause the one or more processors to process each of the multi-dimensional sub-volumes of seismic data in the convolutional neural network, and identify the subsurface structure in the input migrated seismic volume based on a probability map of the input migrated seismic volume generated by the convolutional neural network.

System and method for identifying subsurface structures

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 method includes receiving training images representing a portion of a drilling rig over a first period of time, associating individual training images of the training images with times at which the individual training images were captured, determining a rig state at each of the times, classifying the individual training images based on the rig state at each of the times, training a machine learning model to identify rig state based on the classified training images, receiving additional images representing the portion of the drilling rig over a second period of time, and determining one or more rig states of the drilling rig during the second period of time using the machine learning model based on the additional images.

Rig state detection using video data

Growing community of inventors

Menlo Park, CA, United States of America

Suhas Suresha