Explainable Machine Learning for Hydrocarbon Prospect Risking
This addresses the need for explainable AI in geophysics to improve trust and adoption among domain experts, but it is incremental as it applies an existing method to a new domain.
The paper tackled the problem of limited adoption of AI in hydrocarbon prospect risking due to lack of transparency in black-box models, by applying LIME to generate explanations for individual predictions, showing it can induce trust by aligning decisions with domain knowledge and debug mispredictions.
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.