Exxonmobil Upstream Research Company

Exxonmobil Technology and Engineering Company

Diego A Hernandez

Cody J Macdonald

Wei D Liu

Huseyin Denli

Niranjan A Subrahmanya

Kuang-Hung Liu

Dimitar Vasilev Trenev

Michael H Kovalski

Victoria M Som De Cerff

D Braden Fitz-Gerald

Dudley Braden Fitz-Gerald

A method and apparatus for seismic analysis include obtaining an initial geophysical model and seismic data for a subsurface region; producing a subsurface image of the subsurface region with the seismic data and the geophysical model; generating a map of one or more geologic features of the subsurface region by automatically interpreting the subsurface image; and iteratively updating the geophysical model, subsurface image, and map of geologic features by: building an updated geophysical model based on the geophysical model of a prior iteration constrained by one or more geologic features from the prior iteration; imaging the seismic data with the updated geophysical model to produce an updated subsurface image; and automatically interpreting the updated subsurface image to generate an updated map of geologic features. The method and apparatus may also include post-stack migration, pre-stack time migration, pre-stack depth migration, reverse-time migration, gradient-based tomography, and/or gradient-based inversion methods.

Automated seismic interpretation-guided inversion

A method for subsurface fault extraction using undirected graphs is provided. Extracting faults in the subsurface may assist in various stages of geophysical prospecting. To that end, an undirected graph may be used in order to identify distinctive fault branches in the subsurface. Fault probability data, from seismic data, may be used to establish connections in the undirected graph. Thereafter, some of the connections in the undirected graph may be removed based on analyzing one or more attributes, such as dip, azimuth, or context, associated with the connections or nodes associated with the connections. After which, the undirected graph may be analyzed in order to extract the faults in the subsurface.

Subsurface fault extraction using undirected graphs

A method and apparatus for automated seismic interpretation (ASI), including: obtaining trained models comprising a geologic scenario from a model repository, wherein the trained models comprise executable code; obtaining test data comprising geophysical data for a subsurface region; and performing an inference on the test data with the trained models to generate a feature probability map representative of subsurface features. A method and apparatus for machine learning, including: an ASI model; a training dataset comprising seismic images and a plurality of data portions; a plurality of memory locations, each comprising a replication of the ASI model and a different data portion of the training dataset; a plurality of data augmentation modules, each identified with one of the plurality of memory locations; a training module configured to receive output from the plurality of data augmentation modules; and a model repository configured to receive updated models from the training module.

Automated seismic interpretation systems and methods for continual learning and inference of geological features

A method to automatically interpret a subsurface feature within geophysical data, the method including: storing, in a computer memory, geophysical data obtained from a survey of a subsurface region; and extracting, with a computer, a feature probability volume by processing the geophysical data with one or more fully convolutional neural networks, which are trained to relate the geophysical data to at least one subsurface feature, wherein the extracting includes fusing together outputs of the one or more fully convolutional neural networks.

Automated seismic interpretation using fully convolutional neural networks

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Tomball, TX, United States of America

Diego A Hernandez