mlsanityData to 7 Oct 2026

Explainable Machine Learning for Hydrocarbon Prospect Risking

arXiv 2212.07563 ↗First posted 15 Dec 2022cs.LGcited by 1

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Abstract (arXiv)

Hydrocarbon prospect risking is a critical application in geophysics predicting well outcomes from a variety of data including geological, geophysical, and other information modalities. Traditional routines require interpreters to go through a long process to arrive at the probability of success of specific outcomes. AI has the capability to automate the process but its adoption has been limited thus far owing to a lack of transparency in the way complicated, black box models generate decisions. We demonstrate how LIME -- a model-agnostic explanation technique -- can be used to inject trust in model decisions by uncovering the model's reasoning process for individual predictions. It generates these explanations by fitting interpretable models in the local neighborhood of specific datapoints being queried. On a dataset of well outcomes and corresponding geophysical attribute data, we show how LIME can induce trust in model's decisions by revealing the decision-making process to be aligned to domain knowledge. Further, it has the potential to debug mispredictions made due to anomalous patterns in the data or faulty training datasets.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 29 of its 66 references that are in the map. First posted on arXiv 15 Dec 2022.

Builds on

29 of 66 references.

References · 29

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17 more
  1. 2022Volumetric Supervised Contrastive Learning for Seismic Semantic Segmentationcited by 12
  2. 2019Semi-supervised Sequence Modeling for Elastic Impedance Inversioncited by 10
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Citing papers per year: 2025: 1.

1 paper.

  1. 2025A Large-scale Benchmark on Geological Fault Delineation Models: Domain Shift, Training Dynamics, Generalizability, Evaluation and Inferential Behaviorcited by 4

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