Enhancing Seismic Facies Classification Using Interpretable Feature Selection and Time Series Ensemble Learning Model With Uncertainty Assessment

Seismic facies classification is crucial in reservoir evaluation and guiding oil and gas exploration and development. While machine learning and deep learning models have shown promising results in this field, they often lack interpretability, hindering the understanding of their decision-making process. Additionally, the nonuniqueness of classification results and the need for uncertainty evaluation pose challenges. This study proposes an interpretable and high-precision workflow by combining the time series ensemble learning algorithm Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) 2.0 (HC2) and the Shapley additive explanations (SHAP) interpretation method. HC2, which excels in processing time-domain seismic data, leverages multiple base classifiers in diverse domains and employs the cross-validation accuracy weighted probabilistic ensemble (CAWPE) technique to aggregate predictions, enhancing accuracy and generalization. Notably, the probability values from HC2 enable uncertainty quantification, enabling geologists and engineers to consider the model's uncertainty in decision-making, thereby reducing risks. SHAP facilitates global and local interpretation, enabling the measurement of seismic attribute and elastic parameter importance and optimizing feature combinations for improved classification performance. Experimental results using real data demonstrate the superiority of the proposed workflow over classical machine learning and time series models, specifically in terms of single-well experiment accuracy and lateral continuity of seismic profiles.

Automated summary

This study proposes a workflow that combines HC2 and SHAP methods to address the issues of interpretability and prediction accuracy in seismic facies classification. HC2 improves accuracy and generalization by using multiple base classifiers and probability aggregation, and quantifies uncertainty. SHAP method provides global and local explanations and optimizes feature combinations for improved classification performance.