Influence of Clay, Calcareous Microfossils, and Organic Matter on the Nature and Diagenetic Evolution of Pore Systems in Mudstones

Mathia, Eliza; Rexer, Thomas; Thomas, K. Mark; Bowen, Leon; Aplin, Andrew C.

doi:10.1029/2018jb015941

Cited by 23 publications

(21 citation statements)

References 82 publications

(139 reference statements)

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“…Broad Ion Beam (BIB) -SEM, MICP, and gas sorption methods individually only generate partial descriptions of shale pore systems. In combination, they generate a quantitative description of the size distribution and connectivity of pores ranging in size from nanometer to micrometer [36,84]. Pore size and volume data have been placed into a mineralogical clay-rich, microfossil-rich and organic matter and textural framework in order to be more predictive about the pore systems of shales generally.…”

Section: Shalesmentioning

confidence: 99%

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Supercritical methane adsorption and storage in pores in shales and isolated kerogens

et al. 2020

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Shale gas is an important hydrocarbon resource in a global context. It has had a significant impact on energy resources in the US, but the worldwide development of this methane resource requires further research to increase the understanding of the relationship of shale structural characteristics to methane storage capacity. In this study a range of gas adsorption, microscopic, mercury injection capillary pressure porosimetry and pycnometry techniques were used to characterize the full range of porosity in a series of shales of different thermal maturity. Supercritical methane adsorption methods for shale under conditions which simulate geological conditions (up to 473 K and 15 MPa) were developed. These methods were used to measure the methane adsorption isotherms of Posidonia shales where the kerogen maturity ranged from immature, through oil window, to gas window. Subcritical methane and carbon dioxide adsorption studies were used for determining pore structure characteristics of the shales. Mercury injection capillary pressure porosimetry was used to characterize the meso and macro porosity of shales. The sum of the CO 2 sorption pore volume at 195 K and mercury injection capillary pressure pore volumes (1093-5.6 nm) were equal to the corresponding total pore volume (< 1093 nm) thereby giving an equation accounting for virtually all the available shale porosity. These measurements allowed quantification of all the available porosity in shales and were used for estimating the contributions of methane stored as 'free' compressed gas and as adsorbed gas to overall methane storage capacity of shales. Both the mineral and kerogen components of shale were studied by comparing shale and the corresponding isolated kerogens so that the relative contributions of these components could be assessed. The results show that the methane adsorption characteristics were much higher for the kerogens and represented 35-60% of the total adsorption capacity for the shales used in this study, which had total organic contents in range 5.8-10.9 wt%. Microscopy studies revealed that the pore systems in clay-rich, organic-rich and microfossil-rich parts of shale are very different, and also the importance of the inter-granular organic-mineral interface.

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Section: Shalesmentioning

confidence: 99%

“…Micrographs of HAR 7060 shale (oil window):[36,84]. a Back scattered electron micrographs calcareous shale with densely packed nanofossil aggregates.…”

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confidence: 99%

Supercritical methane adsorption and storage in pores in shales and isolated kerogens

et al. 2020

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“…However, several previous empirical correlations and artificial intelligence models for TOC determination were developed based on the continuous well logs and measured TOC determined from laboratory experiments using a limited number of core samples or drilled cuttings. en, the developed correlation could be used to reliably calculate the TOC for multiple wells [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

[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

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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“…Several authors developed empirical correlations to determine TOC, and these models were developed based on the experimental TOC measures on drilling cuttings or core samples and the corresponding well logs. en, these developed correlations are proposed to be applied to determine the TOC for different wells [15][16][17][18][19]. ese correlations are summarized in Table 1 [20][21][22][23][24].…”

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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Influence of Clay, Calcareous Microfossils, and Organic Matter on the Nature and Diagenetic Evolution of Pore Systems in Mudstones

Cited by 23 publications

References 82 publications

Supercritical methane adsorption and storage in pores in shales and isolated kerogens

Supercritical methane adsorption and storage in pores in shales and isolated kerogens

[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

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

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