Porosity estimation in kerogen-bearing shale gas reservoirs

Yu, Ho‐Shing; Wang, Zhenliang; Rezaee, Reza; Zhang, Yihuai; Han, Tongcheng; Arif, Muhammad; Johnson, Lukman

doi:10.1016/j.jngse.2018.02.012

Cited by 41 publications

(19 citation statements)

References 21 publications

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“…One category is the examination of images through processes such as two-dimensional (2D) image processing [14][15][16] and three-dimensional (3D) image reconstruction [17]. The other category is theoretical calculation, including low-field NMR [18,19], rock physics models [20,21], and calculations based on the mass balance principle [3,7,12,13,22]. However, the contributions of organic pores to total porosity vary significantly, and there is, thus, much discussion of the controlling factors of organic porosity and total porosity [14,[23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Total Organic Carbon Enrichment and Its Impact on Pore Characteristics: A Case Study from the Niutitang Formation Shales in Northern Guizhou

Liu

Tang

2019

Energies

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This study analyzes samples from the Lower Cambrian Niutitang Formation in northern Guizhou Province to enable a better understanding of total organic carbon (TOC) enrichment and its impact on the pore characteristics of over-mature marine shale. Organic geochemical analysis, X-ray diffraction, scanning electron microscopy, helium porosity, and low-temperature nitrogen adsorption experiments were conducted on shale samples. Their original TOC (TOCo) content and organic porosity were estimated by theoretical calculation, and fractal dimension D was computed with the fractal Frenkel–Halsey–Hill model. The results were then used to consider which factors control TOC enrichment and pore characteristics. The samples are shown to be dominated by type-I kerogen with a TOC content of 0.29‒9.36% and an equivalent vitrinite reflectance value of 1.72‒2.72%. The TOCo content varies between 0.64% and 18.17%, and the overall recovery coefficient for the Niutitang Formation was 2.16. Total porosity of the samples ranged between 0.36% and 6.93%. TOC content directly controls porosity when TOC content lies in the range 1.0% to 6.0%. For samples with TOC < 1.0% and TOC > 6.0%, inorganic pores are the main contributors to porosity. Additionally, pore structure parameters show no obvious trends with TOC, quartz, and clay mineral content. The fractal dimension D1 is between 2.619 and 2.716, and D2 is between 2.680 and 2.854, illustrating significant pore surface roughness and structural heterogeneity. No single constituent had a dominant effect on the fractal characteristics.

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Section: Introductionmentioning

confidence: 99%

Total Organic Carbon Enrichment and Its Impact on Pore Characteristics: A Case Study from the Niutitang Formation Shales in Northern Guizhou

Liu

Tang

2019

Energies

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“…According to the assumption of parallel arrangement for organic and inorganic matter, we introduce the organic matter volume fraction, 42 ψ, and obtain the total apparent liquid permeability, k aj , as the weighted sum of k j,om and k j,im by ψ. 43…”

Section: Multiple Liquid Transport Mechanisms In Matrixmentioning

confidence: 99%

“…The initial porosity of organic matter ϕ om,i is estimated using the kerogen porosity equation. 42 • We set the average radius of organic nanopores at the initial condition to be 10 nm in the numerical simulation, according to the pore size data from Eagle Ford Shale sample. 27 The ratio of R om /R im is set to be 10 based on the pore size distribution in organic and inorganic pores.…”

Section: Model Validationmentioning

confidence: 99%

Semianalytical method of two‐phase liquid transport in shale reservoirs and its application in fracture characterization

Zhang

Emami-Meybodi

2021

AIChE Journal

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This study presents a new semi-analytical method to simulate the two-phase liquid transport in hydraulic fractures (HF) and matrix system, which can be applied to characterize HF attributes and dynamics using the flowback data from hydraulically fractured shale oil wells. The proposed approach includes a fracture model for HF properties calculation and a matrix model capable of considering multiple liquid transport mechanisms in shale nanopores. The proposed method is first validated with the numerical simulation then applied to a field example in Eagle Ford shale. The numerical validation confirms that our method can accurately characterize fracture attributes and closure dynamics by closely estimating the initial fracture permeability, pore-volume, compressibility, and permeability modulus. Furthermore, the analysis results from numerical simulation and a field example both indicate a clear flow enhancement and deficit for oil and water transport, respectively, due to the slippage effect and variation of fluid properties inside nanopores.

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“…Holmes, et al [12] proposes another approach with triple combo logs to compute total in-place volumes of the unconventional shale reservoir. Organic matter within the rock matrix directly controls organic porosity and adsorbed gas [2,13,14]. Additionally, TOC affects mechanical anisotropy (Sone and Zoback, 2013a), geomechanical properties [15][16][17][18][19], and brittleness [3,4,20].…”

Section: Introductionmentioning

confidence: 99%

Ensemble Learning for Predicting TOC from Well-Logs of the Unconventional Goldwyer Shale

2021

Self Cite

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Precise estimation of total organic carbon (TOC) is extremely important for the successful characterization of an unconventional shale reservoir. Indirect traditional continuous TOC prediction methods from well-logs fail to provide accurate TOC in complex and heterogeneous shale reservoirs. A workflow is proposed to predict a continuous TOC profile from well-logs through various ensemble learning regression models in the Goldwyer shale formation of the Canning Basin, WA. A total of 283 TOC data points from ten wells is available from the Rock-Eval analysis of the core specimen where each sample point contains three to five petrophysical logs. The core TOC varies largely, ranging from 0.16 wt % to 4.47 wt % with an average of 1.20 wt %. In addition to the conventional MLR method, four supervised machine learning methods, i.e., ANN, RF, SVM, and GB are trained, validated, and tested for continuous TOC prediction using the ensemble learning approach. To ensure robust TOC prediction, an aggregated model predictor is designed by combining the four ensemble-based models. The model achieved estimation accuracy with R2 value of 87%. Careful data preparation and feature selection, reconstruction of corrupted or missing logs, and the ensemble learning implementation and optimization have improved TOC prediction accuracy significantly compared to a single model approach.

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Porosity estimation in kerogen-bearing shale gas reservoirs

Cited by 41 publications

References 21 publications

Total Organic Carbon Enrichment and Its Impact on Pore Characteristics: A Case Study from the Niutitang Formation Shales in Northern Guizhou

Total Organic Carbon Enrichment and Its Impact on Pore Characteristics: A Case Study from the Niutitang Formation Shales in Northern Guizhou

Semianalytical method of two‐phase liquid transport in shale reservoirs and its application in fracture characterization

Ensemble Learning for Predicting TOC from Well-Logs of the Unconventional Goldwyer Shale

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