Quantitative interpretation of coal industrial components using a gray system and geophysical logging data: A case study from the Qinshui Basin, China

Guo, Jianhong; Zhang, Zhansong; Xiao, Hang; Zhang, Chaomo; Zhu, Linqi; Wang, Can

doi:10.3389/feart.2022.1031218

Cited by 5 publications

(1 citation statement)

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“…As shown in Figure 14, the logging data correspond to the radial direction (Figure 14a), while the core resistivity is vertical (Figure 14b); that is, there is a difference of 90 • between the simulation results and the response of logging data. For the response of logging data, taking a single fracture as an example, the resistivity response increases with the increase in fracture angle, and the corresponding core resistivity is a low-angle fracture, which is also consistent [33]. For the solution of fracture angle, many scholars use dual laterolog data.…”

Section: The Difference Between Core Resistivity and Logging Resistivitymentioning

confidence: 63%

Numerical Simulation Study on the Influence of Cracks in a Full-Size Core on the Resistivity Measurement Response

Zheng,

Zhang,

Guo

et al. 2024

Energies

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The development of fractured oil fields poses a formidable challenge due to the intricate nature of fracture development and distribution. Fractures profoundly impact core resistivity, making it crucial to investigate the mechanism behind the resistivity response change in fracture cores. In this study, we employed the theory of a stable current field to perform a numerical simulation of the resistivity response of single-fracture and complex-fracture granite cores, using a full-size granite core with cracks as the model. We considered multiple parameters of the fracture itself and the formation to explore the resistivity response change mechanism of the fracture core. Our findings indicate that, in the case of a core with a single fracture, the angle, width, and length of the fracture (fracture occurrence) significantly affect core resistivity. When two fractures run parallel for a core with complex fractures, the change law of core resistivity is similar to that of a single fracture. However, if two fractures intersect, the relative position of the two fractures becomes a significant factor in addition to the width and length of the fracture. Interestingly, a 90° difference exists between the change law of core resistivity and the change law of the resistivity logging response. Furthermore, the core resistivity is affected by matrix resistivity and the resistivity of the mud filtrate, which emphasizes the need to calibrate the fracture dip angle calculated using dual laterolog resistivity with actual core data or special logging data in reservoirs with different geological backgrounds. In the face of multiple fractures, the dual laterolog method has multiple solutions. Our work provides a reference and theoretical basis for interpreting oil and gas in fractured reservoirs based on logging data and holds significant engineering guiding significance.

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Section: The Difference Between Core Resistivity and Logging Resistivitymentioning

confidence: 63%

Numerical Simulation Study on the Influence of Cracks in a Full-Size Core on the Resistivity Measurement Response

Zheng,

Zhang,

Guo

et al. 2024

Energies

Self Cite

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Prediction of Coal Body Structure of Deep Coal Reservoirs Using Logging Curves: Principal Component Analysis and Evaluation of Factors Influencing Coal Body Structure Distribution

Chang,

Han,

Zhang

et al. 2024

Nat Resour Res

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Optimized Random Forest Method for 3D Evaluation of Coalbed Methane Content Using Geophysical Logging Data

Guo,

Zhang,

Guo

et al. 2024

ACS Omega

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Accurate evaluation of coalbed methane (CBM) content is crucial for effective exploration and development. Traditional gas content measurement methods based on laboratory analysis of drill core samples are costly, whereas geophysical logging methods offer a cost-effective alternative by providing continuous high-resolution profiles of rock layer physical properties. However, the relationship between CBM content and geophysical logging data is complex and nonlinear, necessitating an advanced prediction method. This study focuses on the No. 3 coal seam in the Shizhuang South Block of the Qinshui Basin, utilizing geophysical logging data and 148 sets of laboratory core samples. We employed the Random Forest (RF) method optimized with a simulated annealing-genetic algorithm (SA-GA) to develop the SA-GA-RF model for evaluating CBM content. The model's performance was validated using test data and new CBM well data, and it was applied to calculate the vertical gas content profiles of No. 3 coal seam across 128 wells. The SA-GA-RF model demonstrated an average relative error of 13.13% in the test data set, outperforming Backpropagation Neural Network (BPNN), Least Squares Support Vector Machine (LSSVM), Extreme Learning Machine (ELM), and multivariate regression (MR) methods. The model also exhibited strong generalizability in new wells and improved modelbuilding efficiency compared to traditional cross-validation grid search methods. The construction of a three-dimensional CBM content model, incorporating well coordinates and elevation data, allowed for detailed identification of high gas content areas and layers. This three-dimensional model offers a more precise characterization than traditional two-dimensional isopleth maps, providing valuable insights for CBM exploration, reserve evaluation, and production optimization.

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Quantitative interpretation of coal industrial components using a gray system and geophysical logging data: A case study from the Qinshui Basin, China

Cited by 5 publications

References 50 publications

Numerical Simulation Study on the Influence of Cracks in a Full-Size Core on the Resistivity Measurement Response

Numerical Simulation Study on the Influence of Cracks in a Full-Size Core on the Resistivity Measurement Response

Prediction of Coal Body Structure of Deep Coal Reservoirs Using Logging Curves: Principal Component Analysis and Evaluation of Factors Influencing Coal Body Structure Distribution

Optimized Random Forest Method for 3D Evaluation of Coalbed Methane Content Using Geophysical Logging Data

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