[Retracted] Utilization of Artificial Neural Network in Predicting the Total Organic Carbon in Devonian Shale Using the Conventional Well Logs and the Spectral Gamma Ray

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.

A New Empirical Correlation for Pore Pressure Prediction Based on Artificial Neural Networks Applied to a Real Case Study

“…Additionally, the influence of the number of layers in the ANN model architecture was explored, ranging from single-layer configurations to models with two or three layers. Within each layer, varying neuron counts, ranging from 4 to 30 neurons per layer, were examined to assess their impact on the model's performance [31].…”

An Improved Computational Learning-Based Model for Estimating Total Organic Carbon in Unconventional Shale Reservoirs

“…[28][29][30][31][32] Artificial neural network (ANN) has been the most commonly utilized computational learning technique for predicting TOC in studies. [33][34][35][36][37][38][39] Compared to traditional approaches such as ΔlogR, an ANN performed excellently in these studies due to its capability to draw out patterns between the range of input well logs and measured TOC data. On the contrary constant tuning of the ANN parameters such as number of hidden nodes, biases, and weights to achieve the best performing model structure, ANN suffers intrinsic drawbacks such as overfitting, low computational speed, and converging at local minima.…”

Evaluation of the wellbore drillability while horizontally drilling sandstone formations using combined regression analysis and machine learning models

“…Recently, different machine learning models were successfully applied to different aspects of science and engineering (Najafzadeh 2019;Saberi-Movahed et al 2020;Thanh et al 2020;Elzain et al 2021;Thanh et al 2022a;Thanh and Lee 2022;Thanh et al 2022b), and petroleum engineering is not an exception (Barbosa et al 2019;Elkatatny et al 2019;Mahmoud et al 2020b;Mahmoud et al 2020d;Alsaihati et al 2021;Siddig et al 2021). Recent research was performed to enhance ROP prediction using machine learning capabilities, and in these studies, different machine learning techniques, input parameters, and other technical aspects related to the drilling operations and well planning were considered for ROP prediction while drilling carbonate formation (Mahmoud et al 2020a;Osman et al 2021), natural gas-bearing sandstone formation (Al-AbdulJabbar et al 2022a), and complex lithology formations (Gamal et al 2020).…”