Selective methods of TOC content estimation for organic-rich interbedded mudstone source rocks

Zeng, Bang; Li, Meijun; Zhu, Jianhua; Wang, Xin; Shi, Yang; Zhu, Zhili; Guo, Hao; Fangzheng, Wang

doi:10.1016/j.jngse.2021.104064

Cited by 11 publications

(7 citation statements)

References 26 publications

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“…Therefore, conventional logging curves are used by petroleum geologists to estimate the TOC content of the formation due to its easy availability and vertical continuity (Fertl & Chilingar, 1988; Passey, Creaney, Kulla, Moretti, & Stroud, 1990; Schmoker, 1979). ∆logR model is the most widely used method for TOC estimation, but it is not applicable to mudstone interlayers, due to the rapidly changing lithology and the difficulty in determining a baseline (Zeng et al, 2021). In this study, a multilayer perceptron neural network (MLPNN) method was chosen to build the TOC prediction model for the source rocks, because the mudstone interlayers are generally occurring in the delta front and turbidite fan facies in the western region of the Fushan Depression (Figure 4).…”

Section: Discussionmentioning

confidence: 99%

Source rock evaluation within a sequence stratigraphic framework of the Palaeogene Liushagang Formation in the Fushan Depression, South China Sea

Zeng

Wang

et al. 2022

Geological Journal

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The Fushan Depression is one of the prolific hydrocarbon‐bearing depressions in the Beibuwan Basin, South China Sea. In this study, a total of 133 mudstone samples, 18 well‐logging curves, and 12 seismic sections were analysed to investigate the geochemical features and spatial distribution of effective source rocks of the Palaeogene Liushagang Formation within a sequence stratigraphic geochemical framework. The results suggest that depositional centres of mudstones northward migrated from the central Huangtong Sag during the Liushagang period. Good hydrocarbon generation potential source rocks containing Type II kerogen occur in the transgressive systems tract (TST)–highstand systems tract (HST) of lower Liushagang sequence (SQls3), the middle Liushagang sequence (SQls2), and the lowstand systems tract (LST)–TST of upper Liushagang sequence (SQls1). The occurrence of effective source rock kitchens, which are mainly controlled by the thermal maturity is distributed in the strip‐shaped area of SQls3 sequence, the whole Huangtong Sag of SQls2 sequence, and the central and northern part of Huangtong Sag of SQls1 sequence, respectively. Fewer hydrocarbon accumulations were found in the southern slope due to the relatively deeper hydrocarbon generation threshold depth and to the limited occurrence of effective source rock kitchens.

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Section: Discussionmentioning

confidence: 99%

Source rock evaluation within a sequence stratigraphic framework of the Palaeogene Liushagang Formation in the Fushan Depression, South China Sea

Zeng

Wang

et al. 2022

Geological Journal

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“…Also, the approach based on ΔlogR requires multiple variables such as the level of maturation and baseline resistivity which are not uniform especially in the frequently interbedded shale rocks due to their extreme heterogeneity. 13,15,19,33 New logging techniques such as nuclear magnetic resonance and lithology scanning tools offer developments in TOC quantification accuracy. 34 However, they incur prohibitively high costs and cannot be used conventionally, hence requiring new robust and more convenient methods for TOC quantification.…”

Section: Introductionmentioning

confidence: 99%

“…For example, the gross gamma ray is affected more by uranium, while the bulk density log overestimates the TOC in clay-rich formations. Also, the approach based on Δlog R requires multiple variables such as the level of maturation and baseline resistivity which are not uniform especially in the frequently interbedded shale rocks due to their extreme heterogeneity. ,,, …”

Section: Introductionmentioning

confidence: 99%

“…However, they incur prohibitively high costs and cannot be used conventionally, hence requiring new robust and more convenient methods for TOC quantification . Artificial intelligence (AI) has been considerably used in the last few years in oil and gas research, and much work has been made on the prediction of TOC based on core and well log data. ,− In most cases, AI methods are not globally applicable due to the heterogeneity of shales, which indicates the cruciality of studying the nature of the targeted fields and picking proper logs for the model. , Huang et al demonstrated one of the early applications of AI in predicting TOC using only three conventional (gamma ray, resistivity, and sonic) logs as an input and a pseudo-TOC log calculated from an empirical correlation following the Passey et al approaches . Subsequently, conventional logs have been fed as inputs, that is, gamma ray log, density log, acoustic log, deep and medium resistivity logs, and porosity log in addition to uranium (U), thorium (Th), and potassium (K) contents. − X-ray fluorescence elements’ data (such as copper and nickel) and thermal neutron porosity as well as conventional log combinations displayed correlation with the TOC in the investigated environment − and were proofing evidence of AI reliability in predicting TOC.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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Leveraging petrophysical and geological constraints for AI-driven predictions of total organic carbon (TOC) and hardness in unconventional reservoir prospects

Davy,

El-Husseiny,

Waheed

et al. 2024

Geomech. Geophys. Geo-energ. Geo-resour.

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Selective methods of TOC content estimation for organic-rich interbedded mudstone source rocks

Cited by 11 publications

References 26 publications

Source rock evaluation within a sequence stratigraphic framework of the Palaeogene Liushagang Formation in the Fushan Depression, South China Sea

Source rock evaluation within a sequence stratigraphic framework of the Palaeogene Liushagang Formation in the Fushan Depression, South China Sea

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

Leveraging petrophysical and geological constraints for AI-driven predictions of total organic carbon (TOC) and hardness in unconventional reservoir prospects

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