Prediction of total organic carbon at Rumaila oil field, Southern Iraq using conventional well logs and machine learning algorithms

Handhal, Amna M.; Al-Abadi, Alaa M.; Chafeet, Hussein E.; Ismail, Maher J.

doi:10.1016/j.marpetgeo.2020.104347

Cited by 55 publications

(13 citation statements)

References 29 publications

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“…Indirect methods involve the utilization of petrophysical well logs and seismic data. A large number of models are reported in the literature for the prediction of geochemical parameters using composite well logs. ,− ,− …”

Section: Introductionmentioning

confidence: 99%

Data-Driven Approaches to Predict Thermal Maturity Indices of Organic Matter Using Artificial Neural Networks

et al. 2020

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Prediction of thermal maturity index parameters in organic shales plays a critical role in defining the hydrocarbon prospect and proper economic evaluation of the field. Hydrocarbon potential in shales is evaluated using the percentage of organic indices such as total organic carbon (TOC), thermal maturity temperature, source potentials, and hydrogen and oxygen indices. Direct measurement of these parameters in the laboratory is the most accurate way to obtain a representative value, but, at the same time, it is very expensive. In the absence of such facilities, other approaches such as analytical solutions and empirical correlations are used to estimate the organic indices in shale. The objective of this study is to develop data-driven machine learning-based models to predict continuous profiles of geochemical logs of organic shale formation. The machine learning models are trained using the petrophysical wireline logs as input and the corresponding laboratory-measured core data as a target for Barnett shale formations. More than 400 log data and the corresponding core data were collected for this purpose. The petrophysical wireline logs are γ-ray, bulk density, neutron porosity, sonic transient time, spontaneous potential, and shallow resistivity logs. The corresponding core data includes the experimental results from the Rock-Eval pyrolysis and Leco TOC measurements. A backpropagation artificial neural network coupled with a particle swarm optimization algorithm was used in this work. In addition to the development of optimized PSO-ANN models, explicit empirical correlations are also extracted from the fine-tuned weights and biases of the optimized models. The proposed models work with a higher accuracy within the range of the data set on which the models are trained. The proposed models can give real-time quantification of the organic matter maturity that can be linked with the real-time drilling operations and help identify the hotspots of mature organic matter in the drilled section.

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Section: Introductionmentioning

confidence: 99%

Data-Driven Approaches to Predict Thermal Maturity Indices of Organic Matter Using Artificial Neural Networks

et al. 2020

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“…However, the results provide a ranking of the well log data according to their association with the core-measured TOC content. Thus, we can identify and remove irrelevant and redundant features from the training dataset, reduce the complexity of the model by reducing the dimensionality of the input data, and improve the efficiency of the model [37]. Therefore, based on the results, we selected six logs (AC, DEN, CNL, K, TH, and RD) as input training features.…”

Section: Discussionmentioning

confidence: 99%

“…Due to the third artificial intelligence boom, machine learning has been widely used for lithology identification [23][24][25] and reservoir evaluation [26,27]. Machine learning methods for TOC content prediction include support vector machine (SVM) [28,29], Gaussian process regression (GPR) [30,31], extreme learning machine (ELM) [32,33], neural network [34,35], fuzzy clustering [36], and random forest (RF) [37]. Machine learning is data-driven, which improves the accuracy and efficiency of TOC prediction compared to conventional methods.…”

Section: Introductionmentioning

confidence: 99%

Total Organic Carbon Content Prediction in Lacustrine Shale Using Extreme Gradient Boosting Machine Learning Based on Bayesian Optimization

Liu¹,

Tian²,

Chen³

2021

Geofluids

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The total organic carbon (TOC) content is a critical parameter for estimating shale oil resources. However, common TOC prediction methods rely on empirical formulas, and their applicability varies widely from region to region. In this study, a novel data-driven Bayesian optimization extreme gradient boosting (XGBoost) model was proposed to predict the TOC content using wireline log data. The lacustrine shale in the Damintun Sag, Bohai Bay Basin, China, was used as a case study. Firstly, correlation analysis was used to analyze the relationship between the well logs and the core-measured TOC data. Based on the degree of correlation, six logging curves reflecting TOC content were selected to construct training dataset for machine learning. Then, the performance of the XGBoost model was tested using K -fold cross-validation, and the hyperparameters of the model were determined using a Bayesian optimization method to improve the search efficiency and reduce the uncertainty caused by the rule of thumb. Next, through the analysis of prediction errors, the coefficient of determination ( R 2 ) of the TOC content predicted by the XGBoost model and the core-measured TOC content reached 0.9135. The root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were 0.63, 0.77, and 12.55%, respectively. In addition, five commonly used methods, namely, Δ log R method, random forest, support vector machine, K -nearest neighbors, and multiple linear regression, were used to predict the TOC content to confirm that the XGBoost model has higher prediction accuracy and better robustness. Finally, the proposed approach was applied to predict the TOC curves of 20 exploration wells in the Damintun Sag. We obtained quantitative contour maps of the TOC content of this block for the first time. The results of this study facilitate the rapid detection of the sweet spots of the lacustrine shale oil.

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“…We used the artificial neural network (ANN) method to propose a new TOC model. The developed model was optimized to improve 52 TOC prediction SVM and RBF GR, AC, CNL, TH, U, K, PE, RT, and RHOB Huangping syncline basin in China Wang et al 53 TOC prediction nonlinear regression RT and DT logs Sichuan Basin in China Mahmoud et al 15 TOC prediction ANN DT, GR, RHOB, and RT Devonian Shale in America Zhao et al 54 TOC prediction nonlinear regression CNL Bakken formations in the USA Rui et al 55 TOC prediction SVM GR, RHOB, SP, DT, and RT Beibu Gulf formations in China Lawal et al 56 TOC prediction ANN XRD such as Al 2 O 3 , SiO 2 , CaO, and MgO Devonian formations in America Sultan 57 TOC prediction ANN and differential evolution DT, GR, RHOB, and RT Devonian formations in America Wang et al 46 TOC and S 2 estimations ANN NPHI, RHOB, DT, and RT Bohai Bay formations in China Handhal et al 14 prediction of TOC SVM, ANN, and random forest RHOB, GR, RLLD, DT, and NPHI Rumaila formations in Iran the prediction performance. Finally, an empirical correlation was proposed using the optimized ANN program to allow fast and direct application for the developed model.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Total Organic Carbon in Organic-Rich Shale Rocks Using Thermal Neutron Parameters

et al. 2023

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Total organic carbon (TOC) content is one of the crucial parameters that determine the value of the source rock. The TOC content gives important indications about the source rocks and hydrocarbon volume. Various techniques have been utilized for TOC quantification, either by geochemical analysis of source rocks in laboratories or using well logs to develop mathematical correlations and advanced machine learning models. Laboratory methods require intense sampling intervals to have an accurate understanding of the reservoir, and depending on the thickness of the interested formation, it can be time-consuming and costly. Empirical correlations based on well logs (e.g., density, sonic, gamma ray, and resistivity) showed fast predictions and very reasonable accuracies. However, other important parameters such as thermal neutron logs have not been studied yet as a potential input for providing reliable TOC predictions. Also, different studies estimate the TOC based on the well-logging data for various formations; however, limited studies were reported to predict the TOC for the Horn River Formation. Therefore, the objective of this study is to estimate the TOC variations based on the thermal neutron logs using one of the largest source rocks in Canada: The Horn River Formation. More than 150 data sets were collected and used in this work. The parameters of the artificial neural network (ANN) model were fine-tuned in order to improve the model’s prediction performance. Furthermore, an empirical correlation was developed utilizing the optimized ANN model to allow fast and direct application for the developed model. The developed correlation can predict the TOC with an average absolute error of 0.52 wt %. The proposed TOC model was able to outperform the previous models, and the coefficient of determination was increased from 0.28 to 0.73. Overall, the proposed TOC model can provide high accuracy for TOC ranges from 0.3 to 6.44 wt %. The developed model can provide a real-time quantification for the organic matter maturity, helping to allocate the zones of mature organic matter within the drilled formations.

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Prediction of total organic carbon at Rumaila oil field, Southern Iraq using conventional well logs and machine learning algorithms

Cited by 55 publications

References 29 publications

Data-Driven Approaches to Predict Thermal Maturity Indices of Organic Matter Using Artificial Neural Networks

Data-Driven Approaches to Predict Thermal Maturity Indices of Organic Matter Using Artificial Neural Networks

Total Organic Carbon Content Prediction in Lacustrine Shale Using Extreme Gradient Boosting Machine Learning Based on Bayesian Optimization

Prediction of Total Organic Carbon in Organic-Rich Shale Rocks Using Thermal Neutron Parameters

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