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April 21, 2026Open Access

The Energy Paradox: Artificial Intelligence for Seismic Computational Load Reduction in Oil and Gas — A Technical Survey and Position Analysis.

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Authors

RRRubens Rudio

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Overview

A technical survey analyzes AI techniques reducing seismic workloads in the oil and gas sector, indicating strategic implications.

Key Points

  • This study aims to investigate how AI techniques can alleviate computational burdens in seismic imaging for the oil and gas industry.
  • Survey of principal AI and ML techniques including DNNs and physics-informed networks.
  • Review of institutional deployments and quantified performance outcomes.
  • Analysis of strategic implications related to the Energy Paradox.
  • DNN surrogate models significantly reduce computation time for Full Waveform Inversion tasks.
  • AI-driven approaches enhance computational efficiency of seismic processing workflows.
  • The relationship between energy consumption and AI adoption poses strategic and regulatory challenges.

Cite This Study

Rubens Rudio (2026) studied this question.

synapsesocial.com/papers/69e71467cb99343efc98dba6https://doi.org/10.5281/zenodo.19652036
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