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As a kind of clean energy, the exploration and development of coalbed methane (CBM) are of great importance and significance. In this paper, the CBM reservoir parameters of a working area in Western Guizhou Province, China, were predicted by using 3D seismic exploration technology, and the sweet-spot area was evaluated based on the prediction resul...
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... landform is mainly mountainous and hilly. The overall elevation trend of the work area is high in the west and low in the east, with an altitude between 1600 and 2100 m and a local height difference of more than 300 m ( Figure 1a). The surface slope of the work area is relatively slow, mostly at 15-30° and partially above 40° (Figure 1b). ...
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... overall elevation trend of the work area is high in the west and low in the east, with an altitude between 1600 and 2100 m and a local height difference of more than 300 m ( Figure 1a). The surface slope of the work area is relatively slow, mostly at 15-30° and partially above 40° (Figure 1b). ...
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... to the previous test basis of poststack wave impedance inversion, poststack geostatistical inversion, and prestack simultaneous inversion, we have a better understanding of the vertical and horizontal variation functions of the seismic, wavelet, horizon framework, and geostatistical parameters in this area. Therefore, this prestack geostatistical inversion parameter test is more targeted, and the inversion results are of good quality, as shown in Figure 10a-c. The prestack geostatistical inversion of P-wave impedance (AI), S-wave impedance (SI), and density (DEN) has good correlation with wells, reasonable vertical resolution, clear boundary of geological body, high precision, mainly density result, good stability, and vertical resolution. ...
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... shown in Figure 11a, in the enrichment section of the coal seam (upper coal seam) in the C605-C504 interval, the thickness area of the coal seam is located in the middle of the work area, with the southern belt running in the near northwest direction and the northern belt running in the near northeast direction. As shown in Figure 11b, in the enrichment section of the coal seam (lower coal seam) in the C409-C406 interval, the thick area of coal seam thickness is located in the middle of the work area. ...
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... shown in Figure 11a, in the enrichment section of the coal seam (upper coal seam) in the C605-C504 interval, the thickness area of the coal seam is located in the middle of the work area, with the southern belt running in the near northwest direction and the northern belt running in the near northeast direction. As shown in Figure 11b, in the enrichment section of the coal seam (lower coal seam) in the C409-C406 interval, the thick area of coal seam thickness is located in the middle of the work area. Except for the area with complex structures, other areas with relatively uniform structures have a relatively stable thick coal seam distribution. ...
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... logging curve characteristics in the Liupanshui area have a good correlation with the test results of gas content in the coal core, and the gas content has a good correlation with photoelectric index, density, and sound wave. The correlation coefficients are 0.63, 0.76, and 0.72, respectively (as shown in Figure 12a-c). ...
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... (b) (c) By consulting the logging interpretation report in this area, the following formula is used in the report: VQ = EXP (4.493 + 0.002408 × AC − 0.412 × DEN)/10, where AC and DEN, respectively, represent the acoustic time difference and density curves of logging, but the gas content interpretation result curve (black curve in Figure 13) provided by logging obviously contains a gamma (GR) curve. Because the above equation provided in the logging interpretation report is used, only the AC and DEN curves are used to calculate the gas content (green curve in Figure 13), and the correlation with the gas content curve provided by the logging interpretation is only 83%. ...
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... (b) (c) By consulting the logging interpretation report in this area, the following formula is used in the report: VQ = EXP (4.493 + 0.002408 × AC − 0.412 × DEN)/10, where AC and DEN, respectively, represent the acoustic time difference and density curves of logging, but the gas content interpretation result curve (black curve in Figure 13) provided by logging obviously contains a gamma (GR) curve. Because the above equation provided in the logging interpretation report is used, only the AC and DEN curves are used to calculate the gas content (green curve in Figure 13), and the correlation with the gas content curve provided by the logging interpretation is only 83%. If the GR curve is added, three parameters are used to calculate the gas content, as shown by the red curve in Figure 13, and the correlation coefficient with the gas content result curve provided by the logging interpretation reaches 91%, so observe the details of the gas content interpretation of the C409 coal seam. ...
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... the above equation provided in the logging interpretation report is used, only the AC and DEN curves are used to calculate the gas content (green curve in Figure 13), and the correlation with the gas content curve provided by the logging interpretation is only 83%. If the GR curve is added, three parameters are used to calculate the gas content, as shown by the red curve in Figure 13, and the correlation coefficient with the gas content result curve provided by the logging interpretation reaches 91%, so observe the details of the gas content interpretation of the C409 coal seam. It also coincides with the shape of the GR curve, indicating that the calculation formula of the gas content interpretation model provided in the logging interpretation report does not coincide with the gas content interpretation curve in the logging interpretation result curve. ...
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... on the elastic parameter acoustic wave × density = longitudinal wave impedance (AI), which is the most commonly used elastic parameter, the calculation model of the gas content is re-fit: Vgas = 28.8241 − 0.00441113 × AI. (Figure 13 cyan curve). The uncertainty caused by the GR curve is avoided, and only the elastic curve is included, which is beneficial to obtain the spatial distribution characteristics of the gas content by seismic inversion prediction. ...
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... shown in Figure 14a, the profile of the gas content in the three-dimensional well connection by using longitudinal wave impedance conversion shows that the gas content is in high agreement with the gas content interpreted by logging at well points, and the variation between wells can conform to the law of seismic data, with high longitudinal resolution and certain response to thin layers. It can be seen from the figure that the areas with high gas content are mainly concentrated in the middle of the lower coal seam. ...
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... can be seen from the figure that the areas with high gas content are mainly concentrated in the middle of the lower coal seam. Based on this three-dimensional data volume, the gas content plans of the C605-C504 and C409-C406 layers (as shown in Figure 14b,c) are calculated, respectively. As shown in Figure 14b, the high-value area of gas content in the C605-C504 interval is distributed in the south-central part of the work area, showing a strip-like distribution with a nearly northsouth trend. ...
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... on this three-dimensional data volume, the gas content plans of the C605-C504 and C409-C406 layers (as shown in Figure 14b,c) are calculated, respectively. As shown in Figure 14b, the high-value area of gas content in the C605-C504 interval is distributed in the south-central part of the work area, showing a strip-like distribution with a nearly northsouth trend. As shown in Figure 14c, the area with high gas content in the C409-C406 interval is distributed in the middle of the work area, with relatively low gas content near the fault and relatively high gas content in areas where the fault is undeveloped. ...
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... shown in Figure 14b, the high-value area of gas content in the C605-C504 interval is distributed in the south-central part of the work area, showing a strip-like distribution with a nearly northsouth trend. As shown in Figure 14c, the area with high gas content in the C409-C406 interval is distributed in the middle of the work area, with relatively low gas content near the fault and relatively high gas content in areas where the fault is undeveloped. The predicted results are in good agreement with geological laws. ...
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... shown in Figure 15, the coal bodies; thickness distribution plan of C605-C504 is predicted. The thickness of the primary structure coal is the largest among the three types, and the thickness area is mainly distributed in the west of the work area, showing a striplike distribution in the axial direction near the northwest. ...
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... comparison, the thickness distribution of the other two types of coal structure is relatively scattered. As shown in Figure 16, the prediction plan of the thickness distribution of coal bodies in the C409-C406 interval shows that the thickness of the primary structure coal is also the largest among the three types. The thick value areas are mainly distributed in the areas with few fractures in the work area, the fragmented coal is scattered, and the structure coal is mainly distributed in the south of the work area in a banded pattern. ...
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... the curvature of the C605-C504 and C409-C406 layers in this area is calculated. As shown in Figure 17, the maximum positive curvature of the C605 top surface and the C409 top surface curvature plan are in good agreement with the fracture. ...
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... Young's modulus of C605-C504 and C409-C406 layers in this area is calculated. The main parameters used are P-wave velocity, S-wave velocity, and density, which are directly obtained by prestack geostatistical inversion, as shown in Figure 16, the C605-C504 and C409-C406 layers, the low Young's modulus area (blue area in Figure 18), and fracture. ...
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... Young's modulus of C605-C504 and C409-C406 layers in this area is calculated. The main parameters used are P-wave velocity, S-wave velocity, and density, which are directly obtained by prestack geostatistical inversion, as shown in Figure 16, the C605-C504 and C409-C406 layers, the low Young's modulus area (blue area in Figure 18), and fracture. ...
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... obtaining the curvature and Young's modulus, the coal seam thickness of the main coal seam development section predicted by inversion is used to calculate the in situ stress. The result is as shown in Figure 19. The high-value area of in situ stress is consistent with the distribution height of faults, and the high-value area of in situ stress can be avoided by considering avoiding faults within 200 m in the subsequent well location deployment. ...
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... evaluation parameters are shown in Table 2. Based on this standard, the single-parameter sweet-spot plan is calculated for the three evaluation parameters of coal seam thickness, gas content, and coal body structure. As shown in Figure 21, the sweet-spot areas of coal seam thickness are mainly distributed in the middle of the work area, showing a strip-like distribution. As shown in Figure 22, the sweet spots of coal seam CBM content are mainly distributed in the south-central part of the work area, which is distributed in strips. ...
Citations
... Some geophysical methods, such as ground electromagnetic method and resistivity method, have limited effectiveness in deep coalbed methane exploration, which limits their application in abandoned coal mine deep coalbed methane exploration. Although seismic exploration can provide deeper detection depths, its resolution may not be sufficient to accurately depict the details of coal seams and coalbed methane enrichment areas under complex geological conditions [17]. The geological structure of abandoned coal mine areas is usually complex, such as faults, folds, etc. [18]. ...
Underground coal mining results in large goafs and numerous abandoned mines that contain substantial amounts of coalbed methane. If this methane is not used and controlled, it will escape into the atmosphere through geological fractures and can result in serious greenhouse gas effects and environmental damage. Exploring and developing the coalbed methane resources of abandoned mines can not only improve coal mine safety and protect the ecological environment but also reuse waste and mitigate energy shortages. Geophysical methods have made some progress in detecting abandoned coal mines, but there are still some challenges and difficulties. The resolution of seismic exploration may not be enough to accurately describe the details of coal seams and CBM rich areas, and the effect of resistivity method in deep CBM exploration is limited. In addition, the geological structure of abandoned coal mines is usually more complex, such as faults, folds, etc., which makes the application of exploration methods more difficult and increases the difficulty of data interpretation. Therefore, it is necessary to develop and perfect exploration technology continuously including the application of geophysical big data, deep learning, and artificial intelligence inversion to realize the accurate detection and evaluation of CBM resources in abandoned coal mines.
... The coal structure, rank, thickness, overburden thickness, tectonic stress, permeability, and hydrogeology of the region all have a significant impact on the potential CBM exploration and development (Cao and Peng, 1995;Singh, 2011;Tian, 2015;Niu et al., 2021;Yan et al., 2021). Wei et al. (2022) studied CBM reservoir parameter prediction and sweet-spot evaluation based on 3D seismic exploration in western Guizhou, considering coal seam thickness, CBM content, coal body structure, and in situ stress. Shu et al. (2023) applied seismic and logging data to predict the coal body structure and thickness to describe the relation between TDC (tectonically deformed coal) and CBM resources and production. ...
The Shuicheng area in Guizhou (China) contains extensive coalbed methane (CBM) resources in the Permian Longtan Formation and it has high potential for large-scale exploration and development. However, exploration and development have started in a few areas in these regions. Evaluating CBM exploration potential (EP) areas is crucial for efficient exploration. Using an artificial neural network (ANN) model, this research established a CBM EP model for evaluating prospective areas in the Dahebian, Shenxianpo, and Tudiya CBM blocks. Thirty-four coal seams exist in the research area, with coal seams 01# and 07# highly developed exclusively in Dahebian and Shenxianpo blocks, coal seams 09#, 11#, 12#, 13#, and 14# throughout the three blocks, and coal seam 25# in the southern portion of Tudiya blocks. The coal seam 11# is the thickest (0.2–11.48 m), with buried depth of 102–1522 m, and is characterized by a wide range of properties variation: low- to high-rank coal (Ro% 0.6–2.5), gas content range of 0.43–20.12 m3/t, and weak to highly deformed by a string of normal and reverse faults. Considering six key evaluation parameters (thickness, buried depth, gas content, rank, structural and roof sealing conditions), an EP model was developed, and the analytic hierarchy process membership function and pairwise comparison matrix were used to calculate the weight of each parameter. A mathematical model has determined the comprehensive CBM EP coefficient and the evaluation scores ranged 0–1; the high scores represent high CBM EP. In total, 101,965 data points of the six input neurons and one target neuron were extracted from the 3D parameter model and the result of the EP model, respectively, and were used to train the ANN model. The model performed high accuracy based on R and R2 and minimum mean square error and root mean square error. The results reflect that the model construction was 99% accurate, and the established EP model in this study was also 99% precise and adequate for evaluating the favorable areas for exploration. The results show that the EP values of > 0.8 represents medium to thick coal seam, weakly deformed medium-rank coal having high gas content and buried within 600–1200 m. The EP values of 0.65–0.8 indicate medium thick, medium to high-rank coal with weak-moderately deformed coal having a depth range of 400–1200 m. In areas with EP values of < 0.65, coal seams are thin, highly deformed, middle to high-rank coal with medium to high gas content and < 400 and > 1200 m buried depth. Two very high-potential (VHP), three high-potential, and two medium-potential areas were identified; among them, VHP-1 in the Biandanwan area had the most significant potential, which is supported by CBM development data. This model can significantly impact successful CBM exploration and development of complex geological settings areas in the Guizhou province in the future.
... It is advantageous to directly estimate the reservoir parameters (e.g., pore parameters) using seismic data [23][24][25]. Furthermore, petrophysical reservoir properties have also been evaluated by using seismic methods based on extended elastic impedance inversion [26], pre-stack elastic inversion [27], and quantitative interpretations [28,29]. ...
Tight sandstones produce an increasing amount of natural gas worldwide. Apart from identifying the gas enrichment, the predictions of lithology and permeable zones are crucial for the prediction of tight gas sandstones. In the present study, a seismic inversion method is developed based on rock physical modeling, by which it is possible to directly predict the lithology and pore structure in tight formations. The double-porosity model is used as a modeling tool in considering complex pore structures. Based on the model, the microfracture porosity is then predicted using logging data, which are used as a factor to estimate microfractures. Parameters representing the lithology and pore structure are proposed and estimated using logging data analyses and rock physical modeling based on the framework of the Poisson impedance. Thereafter, a new AVO equation is established and extended to the form of an elastic impedance for a direct prediction of the lithology and pore structure parameters. Real data applications show that the indicators of lithology and permeable zones are consistent with the production status. They agree with the petrophysical properties measured in wellbores, thereby proving the applicability of the proposed method for the effective characterization of tight gas sandstones.
