Explainable Machine Learning for Hydrocarbon Prospect Risking

TL;DR

This paper explores how LIME, an explanation technique, can improve trust and debugging in machine learning models used for hydrocarbon prospect risking by revealing their decision processes and aligning with domain knowledge.

Contribution

It demonstrates the application of LIME to hydrocarbon prospect risk models, enhancing interpretability, trust, and debugging capabilities in geophysical data analysis.

Findings

LIME provides interpretable explanations aligned with domain knowledge.

LIME helps identify mispredictions caused by data anomalies.

The approach increases trust in AI-based prospect risking models.

Abstract

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.