AI Powered Fault Detection

Use Case

Detecting Faults and Structural Discontinuities in 3D Seismic Data

Business Challenge

Accurate fault interpretation is vital for structural understanding, but traditional methods are slow, manual, and depend heavily on interpreter expertise, especially challenging with large 3D datasets and tight timelines.

Subjective interpretations, especially in low-quality seismic zones, result in inconsistent fault frameworks.
Manual horizon picking and fault drawing slow down exploration workflows and delay model updates.
Scaling interpretation across large volumes is impractical without automation, impacting multi-field or regional studies.
Inefficient updates make fault modeling unsuitable for iterative decision-making in fast-paced projects.

The AI Approach

To overcome manual interpretation bottlenecks, the client used an AI-driven fault detection pipeline to identify structural breaks in large 3D seismic volumes, accelerating workflows, improving consistency, and enabling faster decisions.

Convolutional Neural Network modeling detected fault-related discontinuities and amplitude curvature across 3D seismic volumes with high accuracy.
Fault probability cube merging combined outputs from multiple runs with confidence scoring to improve robustness and reduce noise.
Automated fault skeletonization algorithms converted probabilistic fault volumes into continuous surfaces and structural polygons.
All outputs were optimized for seamless integration into interpretation platforms like Petrel, enabling rapid QC and further analysis.

Project Deployment Overview

Input Data Used

3D seismic volumes from multiple basins, totaling over 250 GB of labeled training and inference data.

Final Output Generated

Fault probability cubes, confidence maps, and skeletonized fault polygons ready for integration in interpretation platforms.

Deployment Platform

Executed via GPU-accelerated Python pipeline, supporting inline, crossline, and time-slice visualizations.

Processing Scope

Handled pre-stack and post-stack seismic inputs; outputs integrated directly into Petrel for interpreter QC.

Business Outcomes & Value Unlocked

The AI-driven fault detection pipeline rapidly accelerated interpretation by automating seismic discontinuity detection, improving consistency, reducing manual dependency, and enabling faster, informed insights for exploration teams using large 3D datasets.