Abstract
Due to high oil and gas production and consumption, unconventional reservoirs attracted significant interest. Total organic carbon (TOC) is a significant measure of the quality of unconventional resources. Conventionally, TOC is measured experimentally; however, continuous information about TOC is hard to obtain due to the samples' limitations, while the developed empirical correlations for TOC were found to have modest accuracy when applied in different datasets. In this paper, data from Devonian Duvernay shale were used to develop an optimized empirical correlation to predict TOC based on an artificial neural network (ANN). Three wells' datasets were used to build and validate the model containing over 1250 data points, and each data point includes values for TOC, density, porosity, resistivity, gamma ray and sonic transient time, and spectral gamma ray. The three datasets were used separately for training, testing, and validation. The results of the developed correlation were compared with three available models. A sensitivity and optimization test was performed to reach the best model in terms of average absolute percentage error (AAPE) and correlation coefficient (R) between the actual and predicted TOC. The new correlation yielded an excellent match with the actual TOC values with R values above 0.93 and AAPE values lower than 14%. In the validation dataset, the correlation outperformed the other empirical correlations and resulted in less than 10% AAPE, in comparison with over 20% AAPE in other models. These results imply the applicability of this correlation; therefore, all the correlation's parameters are reported to allow its use on different datasets.
Highlights
As oil and gas production and consumption continue, this leads to the gradual diminishing of conventional hydrocarbon reserves worldwide
A correlation was developed based on the extracted parameters of the optimized artificial neural network (ANN) model to estimate the Total organic carbon (TOC) as a function of eight well logs of the formation resistivity (FR), Δt, RHOB, CNP, gamma ray (GR), and spectral gamma-ray logs of the Ur, and K. e following sections explain the different steps for optimizing the ANN model, extraction of the empirical correlation, and validation of the developed correlation
Training the Artificial Neural Networks. e ANN model was trained for TOC estimation based on eight well-log data of FR, Δt, RHOB, CNP, GR, and spectral gamma-ray logs of the Ur, and K. e training dataset consisted of 891 data points from Well-A
Summary
As oil and gas production and consumption continue, this leads to the gradual diminishing of conventional hydrocarbon reserves worldwide. Erefore, recently source rock and unconventional reservoirs attracted significant interest [3, 4]. The source rock reservoirs such as shale reservoirs were extensively discovered in many countries, including the United States, Canada, China, Argentina, Saudi. Arabia, United Arab Emirates, and Algeria; as a consequence, this resulted in a significant increase in the discovered hydrocarbon resources [6, 7]. Compared to the conventional reservoirs, the unconventional resources are self-generating and self-storing reservoirs; so, it is notoriously critical that the hydrocarbon generation potential of these kinds of reservoirs be evaluated. The unconventional resources are costly to develop, not easy to extract the hydrocarbon from these resources, and notoriously difficult to define the reservoir (i.e., net pay). The unconventional resources are costly to develop, not easy to extract the hydrocarbon from these resources, and notoriously difficult to define the reservoir (i.e., net pay). is confirms the importance of evaluating the ability of the unconventional resources for hydrocarbon generation in a cost-effective manner with high accuracy [4, 6]
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