Integrated TOC prediction and source rock characterization using machine learning, well logs and geochemical analysis: Case study from the Jurassic source rocks in Shams Field, NW Desert, Egypt

Shalaby, Mohamed Ragab; Jumat, Nurhazwana; Lai, Daphne Teck Ching; Malik, Owais Ahmed

doi:10.1016/j.petrol.2019.01.055

Cited by 63 publications

(11 citation statements)

References 21 publications

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“…The parameters which can be used to determine thermal maturity are Tmax, vitrinite reflectance (Ro%) and production index (PI) (Barker 1974;Peters and Cassa 1994;Shalaby et al 2011Shalaby et al , 2012a. The maturity parameters (Tmax and Ro%) were under investigations and evaluation by Shalaby et al (2019aShalaby et al ( , 2020. The study explains the prediction of these parameters in case of absence of geochemical analyses.…”

Section: Thermal Maturitymentioning

confidence: 99%

Geochemical characteristics and depositional environments of the Narimba Formation source rock, Bass Basin, Australia

Shalaby

Irwan

Osli

et al. 2020

J Petrol Explor Prod Technol

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This research aims to conduct source rock characterization on the Narimba Formation in the Bass Basin, Australia, which is made of mostly sandstone, shale and coal. The geochemical characteristics and depositional environments have been investigated through a variety of data such as rock–eval pyrolysis, TOC, organic petrography and biomarkers. Total organic carbon (TOC) values indicated good to excellent organic richness with values ranging from 1.1 to 79.2%. Kerogen typing of the examined samples from the Narimba Formation indicates that the formation contains organic matter capable of generating kerogen Type-III, Type-II-III and Type-II which is gas prone, oil–gas prone and oil prone, respectively. Pyrolysis maturity parameters (Tmax, PI), in combination with vitrinite reflectance and some biomarkers, all confirm that all samples are at early mature to mature and are in the oil and wet gas windows. The biomarkers data (the isoprenoids (Pr/Ph), CPI, isoprenoids/n-alkanes distribution (Pr/nC17 and Ph/nC18), in addition to the regular sterane biomarkers (C27, C28 and C29) are mainly used to evaluate the paleodepositional environment, maturity and biodegradation. It has been interpreted that the Narimba Formation was found to be deposited in non-marine (oxygen-rich) depositional environment with a dominance of terrestrial plant sources. All the analyzed samples show clear indication to be considered at the early mature to mature oil window with some indication of biodegradation.

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Section: Thermal Maturitymentioning

confidence: 99%

Geochemical characteristics and depositional environments of the Narimba Formation source rock, Bass Basin, Australia

Shalaby

Irwan

Osli

et al. 2020

J Petrol Explor Prod Technol

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“…In the 1980s, Schmoker first discovered the relationship between log data and organic matter abundance, and the density log value was used to calculate the organic carbon content. With the continuous development of technology, many methods using log information to predict TOC values have been found, such as the log curve superposition evaluation method (ΔLogR method and its modification method) [11][12][13], multiple linear regression evaluation methods [14], machine learning, and other mathematical analysis evaluation methods [15][16][17]. However, different methods have different scopes.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Method for TOC Prediction: Taking Wufeng and Longmaxi Shales in the Sichuan Basin, Southwest China as an Example

et al. 2021

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The total organic carbon content (TOC) is a core indicator for shale gas reservoir evaluations. Machine learning-based models can quickly and accurately predict TOC, which is of great significance for the production of shale gas. Based on conventional logs, the measured TOC values, and other data of 9 typical wells in the Jiaoshiba area of the Sichuan Basin, this paper performed a Bayesian linear regression and applied a random forest machine learning model to predict TOC values of the shale from the Wufeng Formation and the lower part of the Longmaxi Formation. The results showed that the TOC value prediction accuracy was improved by more than 50% by using the well-trained machine learning models compared with the traditional Δ Log R method in an overmature and tight shale. Using the halving random search cross-validation method to optimize hyperparameters can greatly improve the speed of building the model. Furthermore, excluding the factors that affect the log value other than the TOC and taking the corrected data as input data for training could improve the prediction accuracy of the random forest model by approximately 5%. Data can be easily updated with machine learning models, which is of primary importance for improving the efficiency of shale gas exploration and development.

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“…AI techniques are known to have the capability to generate high accuracy models; therefore, several studies utilized them in TOC prediction [26,27]. In the appendix, Table 2 summarizes the different research studies that utilized AI techniques to estimate the TOC from well logs [8,9,14,17,18,26,[28][29][30][31][32][33][34][35][36][37][38][39][40][41][42]. e used well logs include formation resistivity (FR), spontaneous potential (SP), sonic transit time (Δt), bulk density (RHOB), neutron porosity (CNP), gamma ray (GR), and spectrum logs of thorium ( ), potassium (K), and uranium (Ur).…”

Section: Introductionmentioning

confidence: 99%

Application of Various Machine Learning Techniques in Predicting Total Organic Carbon from Well Logs

Siddig

Ibrahim

Elkatatny

2021

Computational Intelligence and Neuroscience

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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.

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Integrated TOC prediction and source rock characterization using machine learning, well logs and geochemical analysis: Case study from the Jurassic source rocks in Shams Field, NW Desert, Egypt

Cited by 63 publications

References 21 publications

Geochemical characteristics and depositional environments of the Narimba Formation source rock, Bass Basin, Australia

Geochemical characteristics and depositional environments of the Narimba Formation source rock, Bass Basin, Australia

Machine Learning Method for TOC Prediction: Taking Wufeng and Longmaxi Shales in the Sichuan Basin, Southwest China as an Example

Application of Various Machine Learning Techniques in Predicting Total Organic Carbon from Well Logs

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