Abstract
Total organic carbon (TOC) is an essential parameter used in unconventional shale resources evaluation. Current methods that are used for TOC estimation are based, either on conducting time-consuming laboratory experiments, or on using empirical correlations developed for specific formations. In this study, four artificial intelligence (AI) models were developed to estimate the TOC using conventional well logs of deep resistivity, gamma-ray, sonic transit time, and bulk density. These models were developed based on the Takagi-Sugeno-Kang fuzzy interference system (TSK-FIS), Mamdani fuzzy interference system (M-FIS), functional neural network (FNN), and support vector machine (SVM). Over 800 data points of the conventional well logs and core data collected from Barnett shale were used to train and test the AI models. The optimized AI models were validated using unseen data from Devonian shale. The developed AI models showed accurate predictability of TOC in both Barnett and Devonian shale. FNN model overperformed others in estimating TOC for the validation data with average absolute percentage error (AAPE) and correlation coefficient (R) of 12.02%, and 0.879, respectively, followed by M-FIS and SVM, while TSK-FIS model showed the lowest predictability of TOC, with AAPE of 15.62% and R of 0.832. All AI models overperformed Wang models, which have recently developed to evaluate the TOC for Devonian formation.
Highlights
Due to the advances in horizontal drilling and multi-stage fracturing, the possibility of producing hydrocarbon from unconventional hydrocarbon resources, such as shale oil and shale gas is significantly increased
Four artificial intelligence (AI) models were developed to estimate total organic carbon (TOC) based on the application of the Takagi-Sugeno-Kang fuzzy interference system (TSK-FIS), Mamdani fuzzy interference system (M-FIS), functional neural network (FNN), and support vector machine (SVM)
The core samples collected from Barnett shale (Fort Worth Basin (FWB), North Texas, USA) and Devonian Duvernay shale (Western Canada Sedimentary Basin (WCSB)) were analyzed for TOC estimation
Summary
Due to the advances in horizontal drilling and multi-stage fracturing, the possibility of producing hydrocarbon from unconventional hydrocarbon resources, such as shale oil and shale gas is significantly increased. Wang et al [12] revised the ∆logR models and developed new empirical correlations for TOC estimation in Devonian shale formation as a function of the DR, DT, RHOB, and gamma-ray (GR) In their models, Wang et al [12] suggested to include GR log to enhance TOC estimation, and they used more common thermal indicators such as vitrinite reflectance (Ro) or Tmax instead of LOM, which simplify the use of Wang et al [12] models, since the conversion between (Tmax or Ro) and LOM is not required. Four artificial intelligence (AI) models were developed to estimate TOC based on the application of the Takagi-Sugeno-Kang fuzzy interference system (TSK-FIS), Mamdani fuzzy interference system (M-FIS), functional neural network (FNN), and support vector machine (SVM) These models use conventional well logs of DR, GR, DT, and RHOB, collected from the Barnett shale formation. AI techniques have been applied successfully in other fields like social media [36,37]
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