A prediction model of coal structure based on logging parameters in Liupanshui Coalfield, Guizhou, China

Lv, Fang; Yang, Ruidong; Yi, Tongsheng; Gao, Wei; Wei, Cheng; Yan, Zhihua

doi:10.1007/s12517-021-08519-9

Cited by 3 publications

(2 citation statements)

References 57 publications

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“…As a momentous parameter for evaluating the stability of underground engineering, in situ stress measurement is very important for evaluating the permeability, recoverability and fracturing ability of coal reservoirs (Meng et al, 2010;Wang et al, 2019;Sang et al, 2022). The influence of in situ stress is also reflected in areas such as gas content (Bell, 2006;Salmachi et al, 2021), coal structure (Hu et al, 2017;Lv et al, 2021), wellbore track and borehole stability (Zoback et al, 2003;Liu et al, 2004). Stress has three main impacts on CBM exploration and development: stress on coal reservoir pressure and pore and fracture development characteristics; the influence of stress on the series transfer process of desorption, diffusion and seepage of coalbed methane; the influence of stress on the present underground occurrence state and availability of natural fractures, as well as the morphology and extension direction of artificial fractures formed by hydraulic fracturing, etc.…”

Section: Introductionmentioning

confidence: 99%

Characteristics of in situ stress field of coalbed methane reservoir and its influence on permeability in western Guizhou coalfield, China

Yang²,

Yi³

et al. 2023

Front. Earth Sci.

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In-situ stress is an important indicator for the preferential selection of coalbed methane (CBM) exploration dessert zones, and is a key factor affecting the production capacity of coalbed methane wells. Coal reservoir permeability is one of the key parameters to evaluate the recoverability and modifiability of coalbed methane and reflects the seepage capacity of coal reservoirs. In this study, in situ stress data were collected from multiple injection/fall-off tests of multiple parameter wells in western Guizhou province, China The relationships among parameters such as pore pressure (Pp), closure pressure (Pc), breakdown pressure (Pb), in situ stress, coal permeability, and depth were explore. Using Anderson’s classification method, the distribution of three different in situ stress states was counted. A new simplified model diagram of triaxial principal stress and depth in the study area is proposed by linearly fitting the triaxial principal stress and burial depth. The envelope equation and median equation of the lateral pressure coefficient k-value stress ratio with depth of burial obtained by Brown and Hoek method were calculated using hyperbolic regression algorithm. The k-values were found to be discrete at shallower depths and converge at deeper depths, gradually converging to .65. The control of in situ stress on the permeability of coal reservoirs was explored, and a strong positive correlation was found between the permeability and the Z-shaped variation of the lateral pressure coefficient k-values at shallow depths of 1,000 m. Also, the distribution pattern of vertical permeability basically corresponds to the stress transition zone from the strike-slip fault mode to the normal fault mode. The coal seam permeability has a strong sensitivity to effective in situ stress (EIS). In this study, the least squares method with multiple fitting of power exponents is applied to analyze the control mechanism of EIS on permeability in depth and reveal a new relationship between permeability and EIS that is different from that considered by previous authors. Summarizing the above research results, the vertical CBM in western Guizhou is divided into three development potential zones, and 400–1,000 m burial depth is the most favorable vertical development zone.

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Section: Introductionmentioning

confidence: 99%

Characteristics of in situ stress field of coalbed methane reservoir and its influence on permeability in western Guizhou coalfield, China

Yang²,

Yi³

et al. 2023

Front. Earth Sci.

Self Cite

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“…Using continuous logging parameters to predict coal structure is a valuable tool for geologists and mining engineers. By understanding the sensitivity between logging parameters and coal structure, it is possible to make reasonable predictions about the most suitable mining areas and to enhance hazard warning capabilities in the high fracture areas of coal mines, such as gas protrusions and roof collapses [2,3].…”

Section: Introductionmentioning

confidence: 99%

Coal Structure Prediction Based on Type-2 Fuzzy Inference System for Multi-Attribute Fusion: A Case Study in South Hengling Block, Qinshui Basin, China

et al. 2023

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The accurate prediction of coal structure is important to guide the exploration and development of coal reservoirs. Most prediction models are interpreted for a single sensitive coal seam, and the selection of sensitive parameters is correlated with the coal structure, but they ignore the interactions between different attributes. Part of it introduces the concept of the geological strength index (GSI) of coal rocks in order to achieve a multi-element macroscopic description and quantitative characterization of coal structure; however, the determination of coal structure involves some uncertainties among the properties of coal, such as lithology, gas content and tectonic fracture, due to their complex nature. Fuzzy inference systems provide a knowledge discovery process to handle uncertainty. The study shows that a type-2 fuzzy inference system (T2-FIS) with multi-attribute fusion is used to effectively fuse pre-stack and post-stack seismic inversion reservoir parameters and azimuthal seismic attribute parameters in order to produce more accurate prediction results for the Hengling block in the Shanxi area. The fuzzy set rules generated in this paper can provide a more reliable prediction of coal structure in the GSI system. The proposed system has been tested on various datasets and the results show that it is capable of providing reliable and high-quality coal structure predictions.

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Coal Structure Recognition Method Based on LSTM Neural Network

Chen,

Zhang

et al. 2024

Processes

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Coal structures exhibit considerable differences in rock properties and adsorption capacities. The physical properties of coal rocks are fundamental to understanding oil and gas reservoirs, while adsorption capacity directly impacts the gas content in coal seams. The accurate recognition of coal structures is essential for evaluating productivity and guiding coalbed methane well development. This study examines coal rocks of Benxi Formation in Ordos Basin. Using core photographs and logging curves, we classified the coal structures into undeformed coal, cataclastic coal, and granulated-mylonitized coal. AC, DEN, CAL, GR, and CN15 logging curves were selected to build a coal structure recognition model utilizing a long short-term memory (LSTM) neural network. This approach addresses the gradient vanishing and exploding issues often encountered in traditional neural networks, enhancing the model’s capacity to handle nonlinear relationships. After numerous iterations of learning and parameter adjustments, the model achieved a recognition accuracy of over 85%, with 32 hidden units, a minimum batch size of 28, and up to 150 iterations. Validation with independent well data not involved in the model building process confirmed the model’s effectiveness, meeting the practical needs of the study area. The results suggest that the study area is predominantly characterized by undeformed coal, with cataclastic coal and granulated-mylonitized coal more developed along fault trends.

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A prediction model of coal structure based on logging parameters in Liupanshui Coalfield, Guizhou, China

Cited by 3 publications

References 57 publications

Characteristics of in situ stress field of coalbed methane reservoir and its influence on permeability in western Guizhou coalfield, China

Characteristics of in situ stress field of coalbed methane reservoir and its influence on permeability in western Guizhou coalfield, China

Coal Structure Prediction Based on Type-2 Fuzzy Inference System for Multi-Attribute Fusion: A Case Study in South Hengling Block, Qinshui Basin, China

Coal Structure Recognition Method Based on LSTM Neural Network

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