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Artificial Intelligence Improves Seismic-Image Reconstruction

Seismic imaging provides vital tools for the exploration of potential hydrocarbon reserves and subsequent production-planning activities. The acquisition of high-resolution, regularly sampled seismic data may be hindered by physical or financial constraints, which lead to undersampled, sparse seismic data. However, if seismic data are available at a higher resolution and sampled evenly throughout the region of interest, the generated 3D models of petrophysical properties could be improved. Such improvements would show potential benefits through the successive steps of reservoir modeling and production planning.

Traditional Approaches

Traditional methods used to overcome the previously mentioned data-quality issues can be divided broadly into three categories.

  • Wave-equation-based methods. These methods use physics-based wave-propagation equations, using velocity models to reconstruct missing seismic traces.
  • Domain-transform methods. These are data-driven methods that involve transformation of data between different domains, such as time and frequency.
  • Prediction-error filters. These methods use a filter that learns from the known seismic data and constructs missing seismic data.
  • Given recent advances in the field of artificial intelligence (AI), it is worth examining whether AI methods also can be useful in the task of seismic-data reconstruction.
This article, written by JPT Technology Editor Chris Carpenter, contains highlights of the open-submission paper “Artificial Intelligence for Seismic-Image Reconstruction,” by Yogendra Narayan Pandey, SPE, and Govind Chada, Prabuddha, and Tejas Karmarkar, Oracle Cloud Infrastructure. The paper was not presented at an SPE conference and has not been peer-reviewed.
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Artificial Intelligence Improves Seismic-Image Reconstruction

01 October 2019

Volume: 71 | Issue: 10

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