Abstract

Precise estimation of total organic carbon (TOC) is extremely important for the successful characterization of an unconventional shale reservoir. Indirect traditional continuous TOC prediction methods from well-logs fail to provide accurate TOC in complex and heterogeneous shale reservoirs. A workflow is proposed to predict a continuous TOC profile from well-logs through various ensemble learning regression models in the Goldwyer shale formation of the Canning Basin, WA. A total of 283 TOC data points from ten wells is available from the Rock-Eval analysis of the core specimen where each sample point contains three to five petrophysical logs. The core TOC varies largely, ranging from 0.16 wt % to 4.47 wt % with an average of 1.20 wt %. In addition to the conventional MLR method, four supervised machine learning methods, i.e., ANN, RF, SVM, and GB are trained, validated, and tested for continuous TOC prediction using the ensemble learning approach. To ensure robust TOC prediction, an aggregated model predictor is designed by combining the four ensemble-based models. The model achieved estimation accuracy with R2 value of 87%. Careful data preparation and feature selection, reconstruction of corrupted or missing logs, and the ensemble learning implementation and optimization have improved TOC prediction accuracy significantly compared to a single model approach.

Highlights

  • Three-fourth of global sedimentary rock are composed of shale, which plays a major role in hydrocarbon generation, migration, and trapping the hydrocarbon either in other rock types or within the shale rock

  • Random forest outperforms with the least mean squared error (MSE) and highest R2 value compared to other models (Table 5)

  • In this paper we have demonstrated the importance of missing data samples by generating synthetic NPHI and RHOB logs through supervised machine learning regressor model when performing unconventional prospect analysis to understand organic richness of the Goldwyer formation

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Summary

Introduction

Three-fourth of global sedimentary rock are composed of shale, which plays a major role in hydrocarbon generation, migration, and trapping the hydrocarbon either in other rock types (e.g., sandstone, or reservoir rock) or within the shale rock (acting as both source and reservoir rock). This self-produced and self-accumulated source–reservoir system is the unconventional rock. Neutron porosity (NPHI) and RHOB are the most useful combination to discriminate lithology and compute total porosity They are regularly run-in combination with a caliper (CAL) and gamma ray (GR) log as a basic petrophysical measurement. When check shot data or compressional sonic travel time (DT) are available, transform based technique is useful [39]

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