Data-Driven In-Situ Sonic-Log Synthesis in Shale Reservoirs for Geomechanical Characterization

He, Jiabo; Li, Hao; Misra, Siddharth

doi:10.2118/191400-PA

Data-Driven In-Situ Sonic-Log Synthesis in Shale Reservoirs for Geomechanical Characterization

Authors: Jiabo He (University of Melbourne) | Hao Li (University of Oklahoma) | Siddharth Misra (University of Oklahoma)
DOI: https://doi.org/10.2118/191400-PA
Document ID: SPE-191400-PA
Publisher: Society of Petroleum Engineers
Source: SPE Reservoir Evaluation & Engineering
Volume: 22
Issue: 04
Publication Date: November 2019

Document Type: Journal Paper
Pages: 1,225 - 1,239
Language: English
ISSN: 1094-6470
Copyright: 2019.Society of Petroleum Engineers
Disciplines
Keywords: machine learning, sonic logs, data driven
Downloads: 9 in the last 30 days
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Summary

Compressional-travel-time (DTC) and shear-travel-time (DTS) logs acquired using sonic-logging tools are crucial for subsurface geomechanical characterization. In this study, 13 “easy-to-acquire” conventional logs were processed using six shallow-regression-type supervised-learning models—namely, ordinary least squares (OLS), partial least squares (PLS), ElasticNet (EN), least absolute shrinkage and selection operator (LASSO), multivariate adaptive regression splines (MARS), and artificial-neural network (ANN)—to successfully synthesize DTC and DTS logs. Among the six models, ANN outperforms other models with R² of 0.87 and 0.85 for the syntheses of DTC and DTS logs, respectively. The six shallow-learning models are trained and tested with 8,481 data points acquired from a 4,240-ft-depth interval of a shale reservoir in Well 1, and the trained models are deployed in Well 2 for purposes of blind testing against 2,920 data points from 1,460-ft-depth interval. After that, five clustering algorithms are applied on subsamples of 13 “easy-to-acquire” logs to identify clusters and compare them with the log-synthesis performance of the shallow-learning models. A dimensionality-reduction algorithm, t-distributed stochastic neighbor embedding (t-SNE), is used to visualize the petrophysical/statistical characteristics of the clustering algorithm. Hierarchical-clustering, density-based spatial clustering of application with noise (DBSCAN), and self-organizing-map (SOM) algorithms are sensitive to outliers and did not effectively differentiate the input data into consistent clusters. A Gaussian-mixture model can differentiate the various formations, but the clusters do not have a strong correlation with the performance of the log-synthesis models. Clusters identified using the K-means method have a strong correlation with the performance of the shallow-learning models. By combining the shallow-learning models for log synthesis with the K-means clustering, we propose a reliable workflow that can synthesize the DTC and DTS logs, as well as generate a reliability indicator for the synthesis logs to help the user better understand the performance of the shallow-learning models during deployment in new wells.

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References

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Data-Driven In-Situ Sonic-Log Synthesis in Shale Reservoirs for Geomechanical Characterization

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