Data-Driven In-Situ Geomechanical Characterization in Shale Reservoirs

Abstract

Abstract Compressional and shear travel time logs (DTC and DTS) acquired using sonic logging tools are crucial for subsurface geomechanical characterization. In this study, 13 ‘easy-to-acquire’ conventional logs were processed using 6 shallow learning models, namely ordinary least squares (OLS), partial least squares (PLS), elastic net (EN), LASSO, multivariate adaptive regression splines (MARS), and artificial neural network (ANN), to successfully synthesize DTC and DTS logs. Among the 6 models, ANN outperforms other models with R2 of 0.87 and 0.85 for the syntheses of DTC and DTS logs, respectively. The 6 shallow learning models are trained and tested with 8481 data points acquired from a 4240-feet depth interval of a shale reservoir in Well 1, and the trained models are deployed in Well 2 for purposes of blind testing against 2920 data points from 1460-feet depth interval. Following that, 5 clustering algorithms are applied on the 13 ‘easy-to-acquire’ logs to identify clusters and compare them with the prediction performance of the shallow learning models used for log synthesis. Dimensionality reduction algorithm is used to visualize the characteristics of the clustering algorithm. Hierarchical clustering, DBSCAN, and self-organizing map (SOM) algorithms are sensitive to outliers and did not effectively differentiate the input data into consistent clusters. Gaussian mixture model can well differentiate the various formations, but the clusters do not have a strong correlation with the prediction performance of the log-synthesis models. Clusters identified using K-means method have a strong correlation with the prediction performance of the shallow learning models. By combining the predictive shallow learning models for log synthesis with the K-means clustering algorithm, we propose a reliable workflow that can synthesize the DTC and DTS logs, as well as generate a reliability indicator for the predicted logs to help an user better understand the performance of the shallow learning models during deployment.