Landmark Graphics Corporation

Phil Norlund

Fan Jiang

Youli Mao

Meng Meng

Zhili Wei

This disclosure presents a fault prediction system using a deep learning neural network, such as a convolutional neural network. The fault prediction system utilizes as input seismic data, and then derives various seismic attributes from the seismic data. In various aspects, the seismic attributes can be normalized and have importance coefficients determined. A sub-set of seismic attributes can be selected to reduce computing resources and processing time. The deep learning neural network can utilize the seismic data and seismic attributes to determine parameterized results representing fault probabilities. The fault prediction system can utilize the fault probabilities to determine fault predictions which can be represented as a predicted new seismic data, such as using a three-dimensional image.

Deep learning seismic attribute fault predictions

A system can receive fault likelihood data about a subterranean environment and apply a binary mask filter using a tuning parameter to convert the fault likelihood data to binary distribution data having a plurality of pixels arranged in a plurality of profiles in at least two directions. The system can perform, for each profile of the plurality of profiles, fault skeletonization on the binary distribution data to form fault skeletonization data with pixels connected that represent part of a fracture. The system can convert the fault skeletonization data to seismic volume data and combine and filter the seismic volume data in the at least two directions to form combined seismic volume data. The system can output the combined seismic volume data as an image for use in detecting objects to plan a wellbore operation.

Fault skeletonization for fault identification in a subterranean environment

Hydrocarbon exploration and extraction can be facilitated using machine-learning models. For example, a system described herein can receive seismic data indicating locations of geological bodies in a target area of a subterranean formation. The system can provide the seismic data as input to a trained machine-learning model for determining whether the target area of the subterranean formation includes one or more types of geological bodies. The system can receive an output from the trained machine-learning model indicating whether or not the target area of the subterranean formation includes the one or more types of geological bodies. The system can then execute one or more processing operations for facilitating hydrocarbon exploration or extraction based on the seismic data and the output from the trained machine-learning model.

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Spring, TX, United States of America

Phil Norlund