Mineralogical and morphological characterization of Older Cambay Shale from North Cambay Basin, India: Implication for shale oil/gas development

Kumar, Sumit; Das, Sumani; Bastia, Rabi; Ojha, Keka

doi:10.1016/j.marpetgeo.2018.07.020

Cited by 53 publications

(33 citation statements)

References 25 publications

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“…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].…”

Section: Introductionsupporting

confidence: 52%

“…Wang et al [26] developed a revised ΔlogR model to estimate the TOC for Devonian shale from the FR, Δt, RHOB, and gamma ray (GR). In Wang's model, ΔlogR is calculated by equation ( 5) or (6), while the TOC is calculated by equation (7). e authors reported that the accuracy of the ΔlogR model was improved after including the gamma ray.…”

Section: Empirical Correlations For Toc Estimationmentioning

confidence: 99%

“…Recently, 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].…”

Section: Introductionmentioning

confidence: 99%

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

Siddig

Mahmoud

Elkatatny

et al. 2021

Computational Intelligence and Neuroscience

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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.

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“…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].…”

Section: Introductionsupporting

confidence: 52%

Section: Empirical Correlations For Toc Estimationmentioning

confidence: 99%

See 1 more Smart Citation

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

Siddig

Mahmoud

Elkatatny

et al. 2021

Computational Intelligence and Neuroscience

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“…This in turn is controlled by the different depositional environments. Marine shales are considered to be relatively enriched with higher amounts of brittle minerals as compared to shales deposited in lacustrine and fluvial environments [25]. The present study was conducted to investigate the mineralogical compositions, pore characteristics and factors controlling the pores development in the early Maastrichtian Mamu Formation, Anambra Basin.…”

Section: Introductionmentioning

confidence: 99%

Mineralogical and pore structure of organic-rich deltaic shales and sub-bituminous coals from early Maastrichtian Mamu Formation, Anambra Basin, Nigeria

Faboya

Sonibare

et al. 2020

SN Appl. Sci.

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The Mamu Formation source rocks in the Anambra Basin are targets for shale gas reservoir. However, fundamental data on the critical pore structures for assessing the shale reservoir capabilities are lacking. In this study, the source rocks were investigated for their mineralogical compositions and pore structure characteristics by X-ray diffraction, accelerated surface area and porosimetry and field emission scanning electron microscopy (FE-SEM). The bulk mineralogy of the samples is dominated by kaolinite and quartz with average percentage composition of 52.0 and 41.5, respectively, suggesting that the rocks are potential candidates for fracking. The total average pore volume and specific surface area in the samples are 0.0421 cm 3 /g and 22.43 m 2 /g, respectively. The shale samples show higher amounts of total pore volumes relative to the sub-bituminous coals. No obvious organic pore is observed in the shale samples, but some inherited primary organic matter pores of plant origin exist in the sub-bituminous coals. Therefore, inorganic minerals pores are the major contributors to the pore development in the immature shales. The FE-SEM results reveal that the pore network in the shales is dominated by intraparticle pores and fractures. The observed well-developed fractures within the Mamu shales that may have resulted from tectonic events are capable of positively influencing the petrophysical properties of shales in the basin and by extension assist in shale gas exploration and development in the entire Lower Benue Trough.

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“…Since unconventional resources are more complex, tight, and less permeable, unconventional reservoirs exploration is more challenging and demanding in contrast to conventional reservoirs [ 5 ]. However, considerable discoveries of unconventional resources have been announced around the globe, namely, in North and South America, Middle East, and North Africa, which represents a significant addition to the total world oil reserves [ 6 , 7 ]. Unconventional resources, in contrast to conventional reservoirs, are self-storing and self-generating reservoirs; consequently, evaluating their hydrocarbon generation potential is critical.…”

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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Mineralogical and morphological characterization of Older Cambay Shale from North Cambay Basin, India: Implication for shale oil/gas development

Cited by 53 publications

References 25 publications

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

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

Mineralogical and pore structure of organic-rich deltaic shales and sub-bituminous coals from early Maastrichtian Mamu Formation, Anambra Basin, Nigeria

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

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