Quantitative measurement on coal components through the interpretation model of geophysical log: A case study from the Qaidam Basin, NW China

Liu, Shiming; Li, Rui; Tang, Shuheng; Zhao, Cunliang; Liu, Bangjun; Diao, Junwei; Xi, Zhaodong

doi:10.1177/01445987211020450

Cited by 3 publications

(6 citation statements)

References 35 publications

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“…Correlation analysis revealed significant correlations between the industrial component contents, the A ad content correlated significantly with the FC ad content, and both correlated with the V daf content. The A ad content, as an industrial component, correlated significantly with other industrial component contents, and is typically the first to be evaluated (Liu et al, 2021).…”

Section: Resultsmentioning

confidence: 99%

“…The correlation between the A ad content and the response values of the four curves (DEN, GR, AC, and RD) was outstanding, in which the A ad content correlated significantly with the response values of DEN and GR (Figures 4A,B). The coalbeds are not radioactive; thus, the response values of the GR curves are low, and their radioactivity mainly originates from clay minerals during coal formation (Shao et al, 2013;Liu et al, 2021), indicating that in addition to the DEN curve, the GR curve can also be an important curve for predicting A ad content. In addition, the response values of the AC curve and the logarithmically processed RD also exhibit slight correlation with the A ad content (Figures 4C,D); however, the correlation was weaker than that of GR and DEN, the correlation between the CAL curve and A ad content was insignificant, and the response of the CNL curve exhibited almost no correlation with the A ad content (Figures 4E,F).…”

Section: Evaluation Prediction Of a Admentioning

confidence: 99%

“…The V daf content is dependent on several factors, such as the degree of coalification and sedimentary environment (J. Hou et al, 2014;Liu et al, 2021). It is also an important factor in determining the degree of coalification of coal rocks.…”

Section: Evaluation Prediction Of V Dafmentioning

confidence: 99%

“…With the development of mathematical methods, multiple regression (Zhang, 2013), machine learning , and other methods have been gradually introduced to construct linear and nonlinear relationships between industrial components and geophysical logging curves. For example, (Liu et al, 2021) constructed linear relationships between the geophysical logging curves and industrial components and obtained promising results in the Qaidam Basin, whereas (Zhou and O'Brien, 2016) constructed relationships between the geophysical logging curves and multiple parameters, such as industrial components, using the Gaussian radial basis function (RBF) method, exhibiting the feasibility of a machine learning approach, and validated it in several studies. Similarly, methods such as fuzzy mathematics (Huang et al, 2020) have been applied to predict the contents of industrial components.…”

Section: Introductionmentioning

confidence: 99%

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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

et al. 2023

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The content of industrial components of coalbeds, one of the main parameters of coalbed methane (CBM) reservoirs, is crucial in the entire coal mine resource exploration and exploitation process. Currently, using geophysical logging data to determine the content of industrial components is the most widely implemented method. In this study, the PZ block in the Qinshui Basin was employed as a target block to evaluate ash (Aad), fixed carbon (FCad), volatile matter (Vdaf), and moisture (Mad) under the air-dry (AD) base condition based on the autocorrelation between the geophysical logging curves and industrial component contents combined with the OBGM (1, N) model. The results indicate that 1) the geophysical logging curves combined with the OBGM (1, N) model can accurately predict the Aad and FCad contents and an increase in geophysical logging curve types can effectively improve the model performance, compared to using a single geophysical logging curve for prediction. 2) When predicting the Vdaf content, using the geophysical logging curves combined with Aad and FCad contents had the highest prediction accuracy. Further, prediction bias does not exist, compared to using only the geophysical logging curve or the autocorrelation between the industrial component contents. The entire evaluation process begins with an assessment of the Aad and FCad contents. Then, the Vdaf content was assessed using the content of these two industrial components combined with geophysical logging data. Finally, the Mad content was calculated using the volumetric model. Accurate application results were obtained for the verification of new wells, demonstrating the efficacy of the method and procedure described in this study. 3) The OBGM (1, N) model has the highest prediction accuracy compared with the multiple regression and GM (0, N) models, which have the same computational cost. The geophysical logging interpretation model of the proposed coalbed industrial component contents is simple to calculate and suitable for small samples, providing a new method for the evaluation process of industrial component contents.

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Section: Resultsmentioning

confidence: 99%

Section: Evaluation Prediction Of a Admentioning

confidence: 99%

Section: Evaluation Prediction Of V Dafmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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

et al. 2023

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“…The GGL, LLD, GR, and CAL logging curves are the most closely related to coal macrolithotypes. However, the CAL response is easily affected by the drilling process; − thus, GGL (inversely proportional to the density value), LLD, and GR logging curves are usually selected for coal macrolithotype identification and its evaluation.…”

Section: Resultsmentioning

confidence: 99%

Coal Macrolithotype Distribution and Its Genetic Analyses in the Deep Jiaozuo Coalfield Using Geophysical Logging Data

et al. 2021

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Coal macrolithotypes are closely correlated with coal macerals and pore–fracture structures, which greatly influence the changes in gas content and the coal structure. Traditional macrolithotype identification in coalbed methane (CBM) wells mostly depends on core drilling observation, which is expensive, time-consuming, and difficult for broken core extraction. Geophysical logging is a quick and effective method to address this issue. We obtained coal cores from 75 wells in the deep regions of the Jiaozuo Coalfield, northern China, quantitatively analyzed the logging cutoff number corresponding to various macrolithotypes, and established natural γ (GR), deep lateral resistivity (LLD), and γ–γ log (GGL) response rules for each coal macrolithotype. The formation mechanisms of different coal macrolithotypes are discussed from the perspective of coal facies and pore structures. The results show that GGL decreased but GR and LLD increased from bright coal to dull coal. Most coal macrolithotypes can be distinguished based on the established thresholds of various logging curves. However, excessively high or low ash yields significantly affect the validity of identification. The vertical coal macrolithotypes attributed to the peat marsh environment in Shanxi Formation mostly comprise three to six sublayers; dull or semi-dull coals are predominant close to the 2 1 coal seam, and the bright or semi-bright types usually appear in the middle part. The semi-bright and bright coals are usually vitrinite rich, whereas the semi-dull and dull coals are primarily inertinite rich. For pore structure arguments, the highest average specific surface area ( S BET ) and the total pore volume ( V BJH ) are found in bright coals, followed by dull and semi-bright coals; those of semi-dull coals are the lowest. However, S BET and V BJH change significantly for different samples, even though the coal macrolithotype is the same. Therefore, the macrolithotype is not the key factor determining the coal parameters of pore structures. Rapid and effective identification of coal macrolithotypes can help determine the CBM enrichment area, the CBM well location, and the exploration horizon.

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Interpreting coal component content in logging data by combining gray relational analysis and hybrid neural network

Bai

Liu²,

Tan

et al. 2023

Interpretation

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The coal component content is an important parameter during the coal resources exploration and exploitation. Previous logging curve regression and single neural network methods have the disadvantages of low accuracy and weak generalization ability in calculating coal component content. In this study, a GRA-HNN method was proposed by combining grey relational analysis (GRA) and hybrid neural network (HNN) to predict coal component content in logging data. First, the correlation degree between different conventional logging data and coal components was calculated using the GRA method, and logging curves with a correlation degree = 0.7 were selected as the input training data set. Then, a back propagation neural network (BPNN), support vector machine (SVM) neural network, and radial basis function (RBF) neural network of different coal components were constructed based on the selected optimal input logging data, and the weighted average strategy was used to form a HNN prediction model. Finally, the GRA-HNN method was used to predict the coal component content of coalbed methane production wells in Panji mining area. The application results showed that the coal component content predicted by the GRA-HNN method has the highest accuracy compared to the logging curve regression method and its single neural network model, with a maximum average relative error of 13.4%. Besides, the accuracy of coal component content predicted by some single intelligent models is not always higher than the logging curve regression method, indicating that the neural network model is not necessarily suitable for all coal component content predictions. The proposed GRA-HNN method not only optimizes the prediction performance of a single neural network model by selecting effective input parameters, but also comprehensively considers the prediction effect of several neural network models, which strengthens the generalization ability of neural network model and increases the log interpretation accuracy of coal component content.

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Quantitative measurement on coal components through the interpretation model of geophysical log: A case study from the Qaidam Basin, NW China

Cited by 3 publications

References 35 publications

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

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

Coal Macrolithotype Distribution and Its Genetic Analyses in the Deep Jiaozuo Coalfield Using Geophysical Logging Data

Interpreting coal component content in logging data by combining gray relational analysis and hybrid neural network

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