Abstract
Unconventional resources have recently gained a lot of attention, and as a consequence, there has been an increase in research interest in predicting total organic carbon (TOC) as a crucial quality indicator. TOC is commonly measured experimentally; however, due to sampling restrictions, obtaining continuous data on TOC is difficult. Therefore, different empirical correlations for TOC have been presented. However, there are concerns about the generalization and accuracy of these correlations. In this paper, different machine learning (ML) techniques were utilized to develop models that predict TOC from well logs, including formation resistivity (FR), spontaneous potential (SP), sonic transit time (Δt), bulk density (RHOB), neutron porosity (CNP), gamma ray (GR), and spectrum logs of thorium (Th), uranium (Ur), and potassium (K). Over 1250 data points from the Devonian Duvernay shale were utilized to create and validate the model. These datasets were obtained from three wells; the first was used to train the models, while the data sets from the other two wells were utilized to test and validate them. Support vector machine (SVM), random forest (RF), and decision tree (DT) were the ML approaches tested, and their predictions were contrasted with three empirical correlations. Various AI methods' parameters were tested to assure the best possible accuracy in terms of correlation coefficient (R) and average absolute percentage error (AAPE) between the actual and predicted TOC. The three ML methods yielded good matches; however, the RF-based model has the best performance. The RF model was able to predict the TOC for the different datasets with R values range between 0.93 and 0.99 and AAPE values less than 14%. In terms of average error, the ML-based models outperformed the other three empirical correlations. This study shows the capability and robustness of ML models to predict the total organic carbon from readily available logging data without the need for core analysis or additional well interventions.
Highlights
Due to the continuous oil and gas exploitation, conventional hydrocarbon reserves are gradually depleted, and the production rates of the current reservoirs are significantly declining.Conventional hydrocarbon reserves are gradually depleting due to the continuous oil and gas exploitation and the production rates of the current reservoirs dramatically dropped [1, 2]
Considerable discoveries of unconventional resources have been announced around the globe, namely, in North and South America, Middle East, and North Africa, which represents a significant addition to the total world oil reserves [6, 7]
Results and Discussion e Artificial intelligence (AI) models were trained for total organic carbon (TOC) estimation based on eight well log data of RHOB, Δt, CNL, formation resistivity (FR), gamma ray (GR), and spectral GR. e training dataset consisted of 891 data points from Well-A, while the testing dataset contains 291 data points from Well-C. is section presents the results obtained using each method
Summary
Due to the continuous oil and gas exploitation, conventional hydrocarbon reserves are gradually depleted, and the production rates of the current reservoirs are significantly declining. Total organic carbon (TOC), which has been widely considered as a quantification of the hydrocarbon generation potentials [8,9,10], is one of the most efficient parameters that evaluate the quality of unconventional resources [11]. One concern about these empirical correlations is the low accuracy of the predictions when used with different datasets. The application of different AI techniques in TOC prediction in Devonian shale formation from the well logs will be tested. A sensitivity analysis was conducted to investigate the importance of input logging parameters in the predicted TOC values
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