A new method to predict brittleness index for shale gas reservoirs: Insights from well logging data

Ye, Yapei; Tang, Shuheng; Xi, Zhaodong; Jiang, Dexin; Duan, Yang

doi:10.1016/j.petrol.2021.109431

Cited by 18 publications

(6 citation statements)

References 35 publications

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“…The greater the variance of the new variable, the less the original information is lost. The new variable with the greatest variance was defined as the first and second principal components (Hower et al, 2021;Ye et al, 2022). Due to the large difference in the order of magnitude of each indicator, data should be standardized first in data processing, and the original data should be converted into dimensionless data to make it more adequately comparable.…”

Section: Principal Component Analysismentioning

confidence: 99%

Section: Potential Evaluation and Favorable Area Prediction Of Cbmmentioning

confidence: 99%

“…However, there exists limited research on CBM selection using TOPSIS. In addition, factors of CBM development potential evaluation not only restrict and influence each other, but also possess repeated information among a large number of indicators, resulting in the complexity and uncertainty of CBM selection evaluation and decision-making system (Hower et al, 2021; Ye et al, 2022; Zhang et al, 2021a). Principal component analysis is a type of evaluation method that can deal with information jumbled in decision systems and evaluation systems.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of coalbed methane resources in Xinjing Baoan block based on PCA, TOPSIS, & MLFM

Chen¹,

Jiang²,

Wu³

et al. 2022

Energy Exploration & Exploitation

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Coalbed methane (CBM) resources are abundant in the Baoan blocks of the Xinjing coalfield, China; their exploration and development remain in infancy. Suitable development blocks can be selected by evaluating their development potential; that of the No. 8 and 15 CBM systems in the Baoan blocks was evaluated using three quantitative evaluation methods via existing geological data: multi-level fuzzy mathematics (MLFM), technique for order preference by similarity to ideal solution (TOPSIS), and principal component analysis (PCA). The results demonstrated that the CBM development potential in the central part of the Baoan blocks is low and unbecoming. The favorable areas of the No. 8 coal seam are distributed in the northeast and northwest, and that of No. 15 in the northwest and southwest Differences exist in quantitative selection; with limited data, the evaluation of the TOPSIS method is considerably different from actuality, and is unbecoming for evaluating the development potential.

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Section: Principal Component Analysismentioning

confidence: 99%

Section: Potential Evaluation and Favorable Area Prediction Of Cbmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of coalbed methane resources in Xinjing Baoan block based on PCA, TOPSIS, & MLFM

Chen¹,

Jiang²,

Wu³

et al. 2022

Energy Exploration & Exploitation

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“…These two parameters were inversely proportional. Investigation that focused on BI prediction was deeply, and it was commonly used to characterize fracturablity and reservoir quality (Jarvie et al 2007;Rickman et al 2008;Hucka and Das, 1974;Altindag, 2003;Dubey et al 2019;Wood, 2022;Ye et al 2022). Generally, BI was associated with lithology, rock mechanics and pore structure.…”

Section: Introductionmentioning

confidence: 99%

Fractured Tight Glutenite Reservoir Pore Structure Characterization and Deliverability Prediction Based on Geophysical Well Logging

Hu¹,

Tuo²,

Chen³

et al. 2023

Preprint

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Tight glutenite rservoirs characterization and effective hydrocarbon-bearing formation identification faced great challenge due to ultra-low porosity, utra-low permeability and complicated pore structure. In addition, tight glutenite reservoirs generally had no natural productive capacity, fracturing fracture-building technique always needed to improve hydrocarbon production capacity. Pore structure characterization and friability prediction were essential in improving such type of reservoir evaluation. In this study, fractured tight glutenite reservoirs in Permian Jiamuhe Formation that located in northwest margin of Junggar Basin, northwest China was chosen as an example, 25 typical core samples were drilled and simultaneously applied for mercury injection capillary pressure (MICP), nuclear magnetic resonance (NMR) and whole-rock mineral X-ray diffraction experiments. The limitation of pore structure characterization based on NMR logging was analyzed, and a novel method of synthetizing pseudo pore-throat radius (Rc) distribution from porosity frequency spectra, and used to characterize fractured formation pore structure was established, the porosity frequency spectra were extracted from electrical image logging. Based on whole-rock mineral X-ray diffraction experimental data, quartz and calcite were considered as the fragile mineral in our target formation, and rock mineral component ratio method was used to predict brittleness index (BI). The statistical model that raised by Jin et al. (2015) was used to predict two types of fracture toughness KIC and KIIC. BI, KIC and KIIC were combined to characterize tight glutenite reservoirs friability (Frac). Combining with maximal pore-throat radius (Rmax, reflected rock pore structure) and Frac, our target formations were classified into four clusters. Meanwhile, relationships among Rmax, Frac and daily hydrocarbon production per meter (DI) was analyzed, and positively relations among them was observed. Formations with good pore structure and high Frac always contained high deliverability, and vice versa. A model to predict fractured tight glutenite reservoirs DI from well logging data was established. Comparison of predicted DI with the extracted results from drill stem test (DST) data illustrated the reliability of our raised models. This would be valueable in determining optimal hydrocarbon production intervals and formulating reasonable developed plans.

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“…Two distinct approaches to establishing BI involve using either geomechanical (Rickman et al, 2008) or mineralogical analysis (Jarvie et al, 2007). The geomechanical methods involve estimating Young's modulus and Poisson's ratio (Guo et al, 2012;Gholami et al, 2016;Zhang et al, 2016;Wood and Hazra, 2017) using information either from a shear-wave acoustic well logs, or by experimentally deriving complete stress-strain curves (Altindag, 2003;Ye et al, 2022), or by extracting attributes from recorded seismic surveys (Mlella et al, 2020;Ore and Gao, 2021). Brittle formations are characterized by displaying low Poisson's ratio and high Young's modulus values.…”

Section: Introductionmentioning

confidence: 99%

Predicting brittleness indices of prospective shale formations from sparse well-log suites assisted by derivative and volatility attributes

Wood

2022

Adv. Geo-Energy Res.

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A new method to predict brittleness index for shale gas reservoirs: Insights from well logging data

Cited by 18 publications

References 35 publications

Evaluation of coalbed methane resources in Xinjing Baoan block based on PCA, TOPSIS, & MLFM

Evaluation of coalbed methane resources in Xinjing Baoan block based on PCA, TOPSIS, & MLFM

Fractured Tight Glutenite Reservoir Pore Structure Characterization and Deliverability Prediction Based on Geophysical Well Logging

Predicting brittleness indices of prospective shale formations from sparse well-log suites assisted by derivative and volatility attributes

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