Making the black-box brighter: interpreting machine learning algorithm for forecasting drilling accidents
This work addresses the need for interpretability in drilling accident forecasting to increase trust among drilling engineers, though it is incremental as it builds on existing explanation methods.
The authors tackled the problem of interpreting black-box accident forecasting models in oil and gas drilling by developing an explanatory model using Shapley additive explanations and Bag-of-features representation, achieving 15% precision at 70% recall and outperforming a random baseline and multi-head attention neural network.
We present an approach for interpreting a black-box alarming system for forecasting accidents and anomalies during the drilling of oil and gas wells. The interpretation methodology aims to explain the local behavior of the accident predictive model to drilling engineers. The explanatory model uses Shapley additive explanations analysis of features, obtained through Bag-of-features representation of telemetry logs used during the drilling accident forecasting phase. Validation shows that the explanatory model has 15% precision at 70% recall, and overcomes the metric values of a random baseline and multi-head attention neural network. These results justify that the developed explanatory model is better aligned with explanations of drilling engineers, than the state-of-the-art method. The joint performance of explanatory and Bag-of-features models allows drilling engineers to understand the logic behind the system decisions at the particular moment, pay attention to highlighted telemetry regions, and correspondingly, increase the trust level in the accident forecasting alarms.