Startseite Rock physics model for deep coal-bed methane reservoir based on equivalent medium theory: A case study of Carboniferous-Permian in Eastern Ordos Basin
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Rock physics model for deep coal-bed methane reservoir based on equivalent medium theory: A case study of Carboniferous-Permian in Eastern Ordos Basin

  • Bing Zhang , Haifeng Zhang und Fuyou Pan EMAIL logo
Veröffentlicht/Copyright: 19. Dezember 2024
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Abstract

The deep coal-bed methane (CBM) resources represented by the Benxi Formation in the Eastern Ordos Basin have enormous potential and have achieved industrial breakthroughs in recent years. Rock physics modeling is a key research topic for predicting deep CBM reservoirs, but the relationship between parameters such as vitrinite reflectance (Ro), coal rock composition, total gas content, ash content, porosity, and elastic parameters is not clear, necessitating further research on rock physics models suitable for deep CBM reservoirs. On the basis of optimizing the skeleton parameters of the coal matrix (ash content, coal rock type), the porosity parameters of CBM reservoirs are obtained by using the nuclear magnetic resonance method. Equivalent calculation of adsorbed gas and total gas content using adsorbed gas as part of the coal matrix. Based on the measured data, calculate the pore fluid, temperature, and pressure data by taking the average or predicting the curve. Considering the geological characteristics and relevant background of deep CBM reservoirs in the Eastern Ordos Basin, a seismic rock physics model of hydrocarbon reservoirs considering the influence of CBM reservoirs was constructed. The specific process involves using coal matrix + adsorbed gas + matrix pores + cleat (or crack) pores as the dry skeleton, filling with water + free gas as the fluid, and using anisotropic rock physics modeling ideas to complete saturated coal rock physics modeling. By comparing the predicted longitudinal and transverse wave curves with actual measurements, the trends of the two are basically consistent, with a relative error of less than 1%, indicating that the model parameters are reasonably selected.

Keywords: coal-bed reservoir; Benxi Formation; rock physics model; matrix pore; matrix framework parameter

1 Introduction

In recent years, China’s coal-bed methane (CBM) exploration and development have entered a fast lane, and two major CBM industry bases have been established in the southern part of the Qinshui Basin and the Eastern Ordos Basin [1,2,3]. In Fukang, Northeast Tiefa, Zhijin, Guizhou, and some areas in the east of the Junggar Basin, large-scale capacity construction of CBM has been realized, and the exploration field of CBM has been expanded from shallow to medium depth [1,2,3]. In China, the deep CBM resources with a burial depth between 1,500 and 3,000 m are 30.4 × 1012 m3, showing great potential for exploitation [3,4]. Although breakthroughs have been made in deep CBM exploration, the selection of key parameters for exploration and development assessment and the lack of adaptive engineering technology due to increased burial depth have constrained the effective development of deep CBM. How to accurately predict and evaluate reservoir physical parameters (mineral composition, pore, and cleat [or crack] structure, fluid type, etc.). It has become a focus of attention in CBM exploration. The core problem lies in constructing the mapping relationship between reservoir physical parameters and seismic parameter responses. As a bridge between seismic parameter response and CBM reservoir parameters [5], rock physics modeling is influenced by parameters such as Ro, coal rock composition, total gas content, ash content, and porosity, and the regularity of the relationship with changes in elastic parameters is unclear. Therefore, selecting a suitable prediction method is the key to predicting the controlling factors of gas content in deep CBM reservoirs. Compared to sandstone and shale reservoirs, CBM reservoirs generally have characteristics such as multiple layers, thin thickness, wide lateral distribution, and scattered vertical distribution [6,7,8], which pose great challenges for reservoir prediction. At present, there is relatively little research on the rock physical properties of CBM reservoirs, which cannot effectively explain the relationship between elastic parameters and physical property parameters.

Previous studies have conducted extensive rock physics modeling studies on conventional reservoirs including sandstone and carbonate rocks to simulate and analyze the pore cleat (or crack) structure, brittleness characteristics, anisotropy characteristics, and seismic parameter responses [9,10,11]. Some studies attempt to achieve inversion prediction and evaluation of reservoir physical parameters (mineral composition, fluid type, pore and cleat [or crack] parameters, etc.) based on seismic data by rock physics modeling [12,13]. In the study reported by Liu [14], given the differences between CBM and conventional gas reservoirs, a method for treating adsorbed gas in CBM reservoir modeling is proposed. In 2017, the relationship between CBM content and seismic parameter response has been studied [15]. But unlike conventional reservoirs, CBM reservoirs have characteristics such as adsorbed gas and dual pore systems [5]. There are both matrix pores and cleat (or crack) pores developed, and the typical occurrence mode of CBM is adsorption gas, with some free gas and dissolved gas. At present, rock physics modeling is rarely applied to CBM reservoirs, and the limiting factor is that the equivalent calculation problem of adsorbed gas and dual pores unique to CBM reservoirs has not been effectively solved [3,4], but the key parameters including adsorbed gas content and dual pore media in models proposed by previous studies were obtained through equivalent calculations [5,16]. However, the geological characteristics of CBM reservoirs in different areas vary significantly, and the results of equivalent conversion will definitely affect the applicability of the model.

Therefore, based on experimental measurement of adsorbed gas and dual pore media, integrated with the theory of equivalent media, appropriate key modeling parameters are selected to construct a rock physical model of CBM reservoirs, and the rationality of the model is verified.

2 Geological setting

The study area is located in the Eastern Ordos Basin, spanning Shanxi and Shaanxi provinces, within the boundaries of Xingxian and Linxian (Figure 1a). The Late Carboniferous to Middle Permian in this area underwent a paleogeographic evolution process dominated by marine sedimentation, including surface marine, transitional marine, and fluvial clastic rock sedimentation, during which multiple marine intrusion events occurred [17]. In the Late Paleozoic and Early Carboniferous (Figure 1b), the North China Block ended 150 million years of uplift and erosion, began to sink as a whole, and underwent sedimentation [18]. The sedimentation of the Benxi Formation in the Ordos Basin has the property of filling and leveling, and the sedimentary thickness is mainly controlled by the paleo-topography of the Ordovician weathering crust [19]. Generally, sedimentary strata are thicker in the shallow, concave areas of paleo-topography. As a typical stratigraphic formation formed during the critical period of land-sea transition, the Benxi Formation of the Carboniferous System has a rapid rate of sedimentary facies change and complex lithological combinations [20]. The thickness of the Benxi Formation in the study area gradually thickens from west to east, with a thickness mostly ranging from 10 to 70 m [21].

Figure 1 (a) Location of the study area and the CBM wells. (b) Stratigraphic column of Carboniferous Benxi Formation in Eastern Ordos Basin.
Figure 1

(a) Location of the study area and the CBM wells. (b) Stratigraphic column of Carboniferous Benxi Formation in Eastern Ordos Basin.

3 Samples and methods

Coal-bed nuclear magnetic resonance (NMR) interpretation is collected from 22 wells in Eastern Ordos Basin. A total of 17 coal-bed samples were collected from Wells A, B, C, D, and E for vitrinite reflectance (Ro), maceral composition, and low-field NMR measurement. The plug coal-bed samples were first dried at 110°C for 24 h to remove residual moisture in the samples. After 12 h of vacuum degassing, the samples were saturated with high-purity distilled water at a pressure of 25 MPa for 2 days. After the saturation was completed, the sample was taken out, and the NMR T 2 spectrum was tested after standing in the saturated fluid for 12 h. To reflect the occurrence characteristics of different types of fluids, two groups of parallel plug samples saturated with water were treated with different centrifugation speeds and drying temperatures, and NMR tests were performed after each centrifugation and drying. The centrifugation time of each sample is fixed at 30 min, and the drying time is fixed at 24 h. The NMR test parameters are as follows: the echo interval (TE) is 0.055 ms, the number of echoes is 12,000, the cumulative sampling times (NS) is 64 times, and the waiting time is 4,000 ms. The experimental instrument is the NMRC012V NMR instrument produced by Suzhou Niumag Company, China.

4 Results

4.1 Coal matrix skeleton parameters

4.1.1 Parameter calculation process

Coal quality characteristics are important parameters for characterizing coal rock, mainly including microscopic composition characteristics, industrial composition, and macroscopic characteristics. The vitrinite reflectance of the CBM reservoir in the Eastern Ordos Basin is about 0.9–1.5%, belonging to the middle stage of metamorphism, mainly composed of fat coal, coking coal, and semibright coal. Overall coal rock quality is good. As the coalification process progresses, the various functional group structures and microscopic component properties in coal show a gradually homogenized trend. For the mid-low-rank coal rock skeleton in the study area, it is mainly characterized by coal rock type and ash content (Figure 2).

Figure 2 Selection of matrix skeleton parameters for deep CBM reservoirs.
Figure 2

Selection of matrix skeleton parameters for deep CBM reservoirs.

4.1.2 Ash content

Ash content refers to the solid residue left after coal has been fully burned and all mineral changes have been completed, which is mainly composed of clay and non-combustible mineral residue (Figure 3a). The ash content of the coal reservoirs in the study area is mainly about 10–30%, belonging to a moderate ash content level (Figure 3b and c). The overall structure of the coal body is mainly composed of primary structure, with less fragmentation and good compressibility of the reservoir. Ash content not only has a serious impact on the proportion of organic matter, porosity, and cleat development of coal reservoirs but also has a particularly unfavorable effect on the adsorption of methane by coal, seriously affecting the gas content of CBM reservoirs.

Figure 3 (a) Pie chart of mineral proportion in deep CBM reservoir. (b) Pie chart of industrial composition proportion in deep CBM of Taiyuan Formation. (c) Pie chart of industrial composition proportion in deep CBM reservoir of Benxi Formation.
Figure 3

(a) Pie chart of mineral proportion in deep CBM reservoir. (b) Pie chart of industrial composition proportion in deep CBM of Taiyuan Formation. (c) Pie chart of industrial composition proportion in deep CBM reservoir of Benxi Formation.

Industrial analysis of ash content data and intersection analysis of lithology sensitivity curves were carried out using core samples from coal reservoirs in the study area. The results show that there is a certain correlation between the ash content of coal reservoirs and density, gamma, and sound waves. Therefore, a multiple curve multiple regression method is used to estimate ash content and further uses the relationship between ash content and other components to predict fixed carbon, volatile matter, and moisture curves (Figure 4).

Figure 4 Crossplot of ash content and logging data in deep CBM reservoirs. (a) Density vs ash content. (b) DTC vs ash content. (c) GR vs ash content. (d) DTS vs ash content.
Figure 4

Crossplot of ash content and logging data in deep CBM reservoirs. (a) Density vs ash content. (b) DTC vs ash content. (c) GR vs ash content. (d) DTS vs ash content.

4.1.3 Macrolithotype of coal-bed

The vitrinite reflectance (Ro) of the Benxi Formation coal reservoir in the study area is mainly distributed around 0.9–1.5%, mainly composed of semi-bright coal and bright coal, belonging to the middle-rank coal. At a moderate degree of metamorphism, the homogeneity of microscopic components in CBM reservoirs is poor, and the distribution range of microscopic component content is wide. The maceral compositions are mainly composed of vitrinite, with a content of about 65–95%. Coal types are mainly composed of bright coal, while macroscopic coal rock types are mainly semibright coal (Figure 5). Quantitative evaluation of coal rock types using the content of vitrinite components. The biggest difference between vitrinite and inertinite is in their hardness and compressive strength, so elastic parameter curves are used for prediction (Figure 6).

Figure 5 Vitrinite reflectance (Ro), vitrinite content, and macroscopic coal rock types in deep CBM reservoirs. (a) Ro statistical histogram of deep CBM reservoirs in Benxi Formation. (b) Ro statistical histogram of deep CBM reservoirs in Shanxi Formation. (c) Vitrinite content histogram of deep CBM reservoirs in Benxi Formation. (d) Vitrinite histogram of deep CBM reservoirs in Shanxi Formation. (e) Macroscopic coal rock types of deep CBM reservoirs in Benxi Formation. (f) Macroscopic coal rock types of deep CBM reservoirs in Shanxi Formation.
Figure 5

Vitrinite reflectance (Ro), vitrinite content, and macroscopic coal rock types in deep CBM reservoirs. (a) Ro statistical histogram of deep CBM reservoirs in Benxi Formation. (b) Ro statistical histogram of deep CBM reservoirs in Shanxi Formation. (c) Vitrinite content histogram of deep CBM reservoirs in Benxi Formation. (d) Vitrinite histogram of deep CBM reservoirs in Shanxi Formation. (e) Macroscopic coal rock types of deep CBM reservoirs in Benxi Formation. (f) Macroscopic coal rock types of deep CBM reservoirs in Shanxi Formation.

Figure 6 Intersection diagram of vitrinite content and elastic parameters in deep CBM reservoirs. (a) Vitrinite content vs peak compressive strength. (b) Vitrinite content vs elastic modulus.
Figure 6

Intersection diagram of vitrinite content and elastic parameters in deep CBM reservoirs. (a) Vitrinite content vs peak compressive strength. (b) Vitrinite content vs elastic modulus.

4.2 Matrix porosity by equivalent calculation method

Coal reservoirs are a type of dual porosity medium rock layer, which includes both matrix pores and cleat (or crack) pores. The pore network is complex, and the pore size is mainly in the range of micrometers to nanometers [22,23,24]. Whether it is conventional experimental testing or well-logging interpretation, accurate characterization of coal matrix pores and cleat (or crack) pores is highly challenging. NMR logging technology can directly measure the density of hydrogen nuclei in the storage space and is not affected by the rock skeleton. NMR porosity is determined by ascertaining the correlation between NMR signals and porosity [25,26,27]. Subsequently, free fluid porosity and bound fluid porosity can be obtained by measuring again after the pore fluid is discharged through centrifugal testing (Figure 7). Previous research has shown that usually more than 90% of CBM is adsorbed on the surface of matrix pores, with almost no seepage effect, so equivalent elastic parameters cannot be calculated by fluid replacement [5]. Cleat (crack) pores, possessing superior porosity and permeability, constitute the primary pore type in coal reservoirs. The analysis revealed pore size primarily below 100 nm in the study area’s Benxi Formation deep CBM reservoirs, while a minor fraction encompasses mesopores exceeding 100 nm (Figure 8a). The average pore size is 19.6 nm (Figure 8b), and the permeability is less than 0.001 mD. NMR data show a distinct bimodal distribution of the deep CBM reservoirs’ T 2 values within the Benxi Formation, with minimal differences between the two peaks (Figure 9). The minor peak spans 3–25 ms, reflecting bound porosity, and the significant peak spans 26–33 ms, indicating movable porosity. Over 75% of these pores are bound, signifying poor pore connectivity. The bimodal pattern primarily highlights bound porosity, followed by movable pores (Figure 10).

Figure 7 (a) Nuclear magnetic T 2 spectrum of deep CBM reservoirs. (b) Typical T 2 spectrum and porosity distribution diagram of NMR testing in deep CBM reservoirs.
Figure 7

(a) Nuclear magnetic T 2 spectrum of deep CBM reservoirs. (b) Typical T 2 spectrum and porosity distribution diagram of NMR testing in deep CBM reservoirs.

Figure 8 (a) Pore-size distribution of deep CBM reservoir in Taiyuan Formation. (b) Pore-size distribution of deep CBM reservoir in Benxi Formation.
Figure 8

(a) Pore-size distribution of deep CBM reservoir in Taiyuan Formation. (b) Pore-size distribution of deep CBM reservoir in Benxi Formation.

Figure 9 Characteristics of NMR logging curves in deep CBM reservoirs.
Figure 9

Characteristics of NMR logging curves in deep CBM reservoirs.

Figure 10 Interpretation results of NMR porosity in deep CBM reservoirs.
Figure 10

Interpretation results of NMR porosity in deep CBM reservoirs.

Considering the internal heterogeneity of the CBM reservoirs in this area, the main reason for the high clay pores in the deep CBM reservoirs of the Benxi Formation is, on the one hand, related to the large number of thin-gangue interbeds in the coal intervals. The overall ash content is relatively high, resulting in higher clay pores. The calculation method for clay pores is the minimum T 2 value and the minimum bound pore value, almost all of which are bound fluid pores. In the calculation of skeleton pores, they are classified as bound pores. Due to the lack of standard methods for calculating the porosity of the coal bed, several porosity calculation methods were tested and calibrated with actual test data. The multiple regression methods had the best effect. Overall, the porosity prediction based on multiple regression methods has a high degree of matching with NMR test results, with a consistent relative relationship and trend, and a correlation coefficient of about 70% (Figure 11).

Figure 11 Cross plot of nuclear magnetic porosity and logging data in deep CBM reservoirs. (a) P-sonic vs NMR porosity. (b) NPHI vs NMR porosity. (c) GR vs NMR porosity. (d) Density vs NMR porosity.
Figure 11

Cross plot of nuclear magnetic porosity and logging data in deep CBM reservoirs. (a) P-sonic vs NMR porosity. (b) NPHI vs NMR porosity. (c) GR vs NMR porosity. (d) Density vs NMR porosity.

4.3 Total gas content by the equivalent calculation method

The matrix porosity of coal reservoirs is usually less than 10%, and the aspect ratio of matrix pores (i.e., the ratio of matrix pore width to diameter) is used αP usually falls within the range of 0.1–1 (Figure 12), making it difficult to meet the conditions required by Kuster–Toksöz theory [5]. Moreover, in a dynamic adsorption equilibrium state, more than 90% of CBM is adsorbed on the surface of coal matrix pores without any seepage effect, and the equivalent elastic parameters cannot be calculated by fluid replacement.

Figure 12 Approximate description of geometrical characteristics of coal matrix and adsorbed gas [5].
Figure 12

Approximate description of geometrical characteristics of coal matrix and adsorbed gas [5].

For the calculation of the equivalent elastic modulus of the dry coal matrix skeleton containing matrix pores and adsorbed gas, based on the idea proposed by Liu of using adsorbed gas as a part of coal matrix for equivalent calculation [14], a self-compatible approximation model is used to describe the dry coal matrix skeleton as a mixture of adsorbed gas, matrix pores, and coal matrix phases (Figure 12). The specific content is as follows: matrix pores = adsorbed gas volume + remaining matrix pores. According to the actual situation, the shape of the coal matrix and adsorbed gas is set to spherical. In the calculation process, the adsorbed gas is considered an independent solid phase rather than a fluid. Assuming that the adsorbed gas is uniformly adsorbed on the surface of the matrix pores, it does not affect the aspect ratio of the matrix pores (the red and blue arrows in Figure 3 show the aspect ratio of the matrix pores before and after adsorption). The bulk modulus and density of the adsorbed gas are typical equivalent values measured by Zou et al. at 4 MPa [16], which are 7.5 MPa and 0.5 g/cm3, respectively. For the calculation of the total gas content curve of coal reservoirs, previous studies have mostly used actual sampling data and multisensitivity curve logging interpretation methods to obtain it. This time, typical formulas and multiple regression methods are used for calculation. Total gas content = −2.05745 + 0.00195693 × DEPTH + 0.0121332 × DTS − 0.00723678 × GR − 0.0141831 × DT − 3.54587 × RHOB + 0.117409 × CAL + 0.00166508 × AAD + 0.921259 × log10 (RESD) (Figure 13).

Figure 13 Predicting formation pressure and geothermal curve (pressure recovery measurement).
Figure 13

Predicting formation pressure and geothermal curve (pressure recovery measurement).

4.4 Pore fluids by the equivalent calculation method

The pore fluid is mainly composed of methane and free water, and the parameters of temperature, pressure, mineralization degree, gas-specific gravity, and gas/oil ratio are calculated by taking the mean value or predicting the curve based on the measured data (Table 1).

Table 1

Analysis data of water and gas samples from deep CBM methane reservoirs

Well Strata Chloride value (mg/L) Water type Mineralization degree (g/L) Methane proportion (%)
A Taiyuan 44,752 CaCl2 89.24 —
B Taiyuan 25,928 CaCl2 27.97 92.56
D Taiyuan 24,368 CaCl2 27.30 92.59
C Taiyuan 25,835 CaCl2 28.35 94.13
E Taiyuan 70,542 CaCl2 120.37 95.09
A Benxi 32,010 CaCl2 51.81 89.37
A Benxi 16,032 CaCl2 29.43 96.38
C Shanxi 37,409 CaCl2 64.22 —
C Benxi 10,688 CaCl2 19.50 93.56
D Benxi 24,241 CaCl2 50.91 86.04
E Shanxi 10,912 CaCl2 19.29 95.19
E Taiyuan 23,642 CaCl2 40.40 96.21
E Taiyuan 27,077 CaCl2 45.64 98.91
A Taiyuan 16,994 CaCl2 37.14 —
A Taiyuan 34,202 CaCl2 60.90 91.26
B Taiyuan 21,376 CaCl2 38.29 90.90
B Taiyuan 21,376 CaCl2 38.02 97.54
B Taiyuan 633.15 CaCl2 38.14 97.15
Average value 26,000 CaCl2 45.94 93.79

5 Discussion

5.1 Rock physics modeling process

Various parameter calculation models for the study area were established, and various parameter curves were predicted. Starting from the classical models of low porosity and cleat (or crack), lithology was developed such as tight sandstone, carbonate rock, and shale searching for suitable framework, cleat (or crack), and fluid prediction models for local coal rocks. Based on the geological characteristics and relevant background of deep CBM reservoirs in the study area, a seismic rock physical model of hydrocarbon reservoirs considering the influence of coal reservoirs was constructed. The specific process is divided into several parts, including background rock matrix construction, pore filling, coal bed coupling, and fluid replacement (Figure 14).

Figure 14 Schematic diagram of rock physical modeling process for deep CBM reservoirs.
Figure 14

Schematic diagram of rock physical modeling process for deep CBM reservoirs.

First, the Voigt–Reuss–Hill model was utilized to mix background minerals such as synbiotic minerals [28], and epigenetic minerals according to the volume content of each component. This model characterizes the equivalent rock modulus of the mineral matrix by taking the average of equal stress and equal strain. Set the porosity of the coal matrix (including the volume of adsorbed gas), the aspect ratio of the matrix pores, and the shape of the coal matrix particles and adsorbed gas as spheres. Assuming that the adsorbed gas is uniformly adsorbed on the surface of the matrix pores, that is, it does not affect the aspect ratio of the matrix pores; using a self-compatible approximation model to calculate the equivalent longitudinal and transverse wave velocities of the dry skeleton of coal matrix after mixing adsorbed gas, matrix pores, and coal matrix. Set the porosity of cracks and shape them as thin coins. Calculate the density of cracks and add them to the coal matrix dry skeleton using a DEM model [29,30,31]. Calculate the equivalent longitudinal and transverse wave velocities of the coal matrix dry skeleton containing cracks. Finally, the Brown–Korringa anisotropic fluid substitution theory was used to fill the fluid into the cleats (or cracks) [32], and the equivalent longitudinal and transverse wave velocities of the rock physical model of the CBM reservoir were calculated (Table 2).

Table 2

Rock physical modeling process for deep CBM reservoirs

Test serial number Matrix mixture Fluid mixture Skeleton model Cleat (crack) model Anisotropic model Fluid substitution model Conclusion
1 Voigt–Reuss–Hill [28] Wood equation DEM SCA Backus [33] Gassmann [34] The effect is not ideal. Wood equation uniform saturation is not applicable to coal seams
2 Voigt–Reuss–Hill [28] Brie’s equation DEM SCA Backus [33] Gassmann [34] The effect is good, but the SCA model is not suitable for low-porosity models
3 Voigt–Reuss–Hill [28] Brie’s equation DEM + SCA SCA Backus [33] Gassmann [34] Poor effect, insufficient constraint force on the original curve in fluid substitution
4 Voigt–Reuss–Hill [28] Brie’s equation DEM SCA Backus [33] Brown and Korringa [32] The effect is poor, but there is improvement
5 Voigt–Reuss–Hill [28] Brie’s equation DEM Hudson Backus [33] Brown and Korringa [32] The effect is poor, and the cleats (cracks) in the Hudson model are isolated
6 Voigt–Reuss–Hill [28] Brie’s equation DEM Xu and Payne [10] Backus [33] Brown and Korringa [32] The effect is poor, the overall predicted value is low, and it is not suitable for the cleats (cracks) in coal reservoirs
7 Voigt–Reuss–Hill [28] Brie’s equation DEM DEM Backus [33] Brown and Korringa [32] Ideal effect, uniform distribution of cleats (cracks) in the model, VTI anisotropy

5.2 Modeling effect evaluation

The key to the accuracy and applicability of seismic rock physics modeling lies in closely following the characteristics of the specific reservoir being studied. The main characteristics of the deep CBM reservoir are adsorbed gas as part of the dry coal skeleton and dual-porosity media. In this study, the specific data of adsorbed gas and dual porosity media are obtained through experimental investigation, rather than being converted through models or formulas as in previous studies. Therefore, the results are more accurate and more conducive to improving the accuracy of the model. Overall, the coal matrix + adsorbed gas + matrix pores + cleat (or crack) pores are used as the dry coal skeleton, and the water + free gas is used as the fluid filling. The anisotropic rock physics modeling approach is used to complete saturated coal rock physics modeling. By comparing the predicted P-wave and S-wave curves with actual measurements, the two trends are basically consistent, with a relative error of less than 1%, indicating that the selection of model parameters is reasonable (Figures 15 and 16).

Figure 15 Comparison between rock physics modeling and predicted longitudinal and transverse wave curves and measured curves (Benxi Formation deep CBM reservoir).
Figure 15

Comparison between rock physics modeling and predicted longitudinal and transverse wave curves and measured curves (Benxi Formation deep CBM reservoir).

Figure 16 Prediction of intersection between longitudinal and transverse wave curves and measured curves in rock physical modeling of deep CBM reservoirs (based on 27 typical wells).
Figure 16

Prediction of intersection between longitudinal and transverse wave curves and measured curves in rock physical modeling of deep CBM reservoirs (based on 27 typical wells).

6 Conclusions

Rock physics modeling is the key to characterizing the mapping relationship between physical properties, fluid parameters, and seismic parameters of deep CBM reservoirs. In the modeling process, key factors such as coal matrix skeleton parameters, matrix porosity, adsorbed gas volume, and pore fluid must be considered. Coal rock type and ash content are the main characteristics of the deep CBM reservoir matrix framework. The matrix porosity is mainly dominated by bound porosity, followed by movable porosity. In the modeling process, focusing on the two special properties of adsorbed gas and dual pores in CBM reservoirs, adsorbed gas is regarded as a solid phase similar to coal matrix, and dual pores are divided into matrix pores and cleat (or crack) pores. The modeling process can be summarized as coal matrix, adsorbed gas, matrix pores, and cleat (or crack) pores forming the dry coal skeleton, filled with water and free gas as fluids, using anisotropic rock physics modeling ideas. After comparing the predicted longitudinal and transverse wave curves with actual measurements, the two trends are basically consistent, with a relative error of less than 1%, indicating that the selection of rock physical model parameters for deep CBM reservoirs is reasonable.

  1. Funding information: The research was supported by the National Natural Science Foundation of China (Grant No. 42272195) and the Major Science and Technology Projects of CNOOC China Limited (No. CNOOC-KJ 135 ZDXM 40).

  2. Author contributions: Bing Zhang contributed as the major author of the article. Bing Zhang and Fuyou Pan conceived the project. Haifeng Zhang and Bing Zhang collected the samples. Fuyou Pan and Bing Zhang analyzed the samples. All authors contributed to the article and approved the submitted version.

  3. Conflict of interest: The authors declare that they have no known competing financial interests or personal relationships that might have influenced the work presented in this article.

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Received: 2024-08-08
Revised: 2024-10-09
Accepted: 2024-11-17
Published Online: 2024-12-19

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

Artikel in diesem Heft

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  3. The research of common drought indexes for the application to the drought monitoring in the region of Jin Sha river
  4. Evolutionary game analysis of government, businesses, and consumers in high-standard farmland low-carbon construction
  5. On the use of low-frequency passive seismic as a direct hydrocarbon indicator: A case study at Banyubang oil field, Indonesia
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https://doi.org/10.1515/geo-2022-0742
Schlagwörter für diesen Artikel
coal-bed reservoir; Benxi Formation; rock physics model; matrix pore; matrix framework parameter
Creative Commons
BY 4.0

Artikel in diesem Heft

  1. Regular Articles
  2. Theoretical magnetotelluric response of stratiform earth consisting of alternative homogeneous and transitional layers
  3. The research of common drought indexes for the application to the drought monitoring in the region of Jin Sha river
  4. Evolutionary game analysis of government, businesses, and consumers in high-standard farmland low-carbon construction
  5. On the use of low-frequency passive seismic as a direct hydrocarbon indicator: A case study at Banyubang oil field, Indonesia
  6. Water transportation planning in connection with extreme weather conditions; case study – Port of Novi Sad, Serbia
  7. Zircon U–Pb ages of the Paleozoic volcaniclastic strata in the Junggar Basin, NW China
  8. Monitoring of mangrove forests vegetation based on optical versus microwave data: A case study western coast of Saudi Arabia
  9. Microfacies analysis of marine shale: A case study of the shales of the Wufeng–Longmaxi formation in the western Chongqing, Sichuan Basin, China
  10. Multisource remote sensing image fusion processing in plateau seismic region feature information extraction and application analysis – An example of the Menyuan Ms6.9 earthquake on January 8, 2022
  11. Identification of magnetic mineralogy and paleo-flow direction of the Miocene-quaternary volcanic products in the north of Lake Van, Eastern Turkey
  12. Impact of fully rotating steel casing bored pile on adjacent tunnels
  13. Adolescents’ consumption intentions toward leisure tourism in high-risk leisure environments in riverine areas
  14. Petrogenesis of Jurassic granitic rocks in South China Block: Implications for events related to subduction of Paleo-Pacific plate
  15. Differences in urban daytime and night block vitality based on mobile phone signaling data: A case study of Kunming’s urban district
  16. Random forest and artificial neural network-based tsunami forests classification using data fusion of Sentinel-2 and Airbus Vision-1 satellites: A case study of Garhi Chandan, Pakistan
  17. Integrated geophysical approach for detection and size-geometry characterization of a multiscale karst system in carbonate units, semiarid Brazil
  18. Spatial and temporal changes in ecosystem services value and analysis of driving factors in the Yangtze River Delta Region
  19. Deep fault sliding rates for Ka-Ping block of Xinjiang based on repeating earthquakes
  20. Improved deep learning segmentation of outdoor point clouds with different sampling strategies and using intensities
  21. Platform margin belt structure and sedimentation characteristics of Changxing Formation reefs on both sides of the Kaijiang-Liangping trough, eastern Sichuan Basin, China
  22. Enhancing attapulgite and cement-modified loess for effective landfill lining: A study on seepage prevention and Cu/Pb ion adsorption
  23. Flood risk assessment, a case study in an arid environment of Southeast Morocco
  24. Lower limits of physical properties and classification evaluation criteria of the tight reservoir in the Ahe Formation in the Dibei Area of the Kuqa depression
  25. Evaluation of Viaducts’ contribution to road network accessibility in the Yunnan–Guizhou area based on the node deletion method
  26. Permian tectonic switch of the southern Central Asian Orogenic Belt: Constraints from magmatism in the southern Alxa region, NW China
  27. Element geochemical differences in lower Cambrian black shales with hydrothermal sedimentation in the Yangtze block, South China
  28. Three-dimensional finite-memory quasi-Newton inversion of the magnetotelluric based on unstructured grids
  29. Obliquity-paced summer monsoon from the Shilou red clay section on the eastern Chinese Loess Plateau
  30. Classification and logging identification of reservoir space near the upper Ordovician pinch-out line in Tahe Oilfield
  31. Ultra-deep channel sand body target recognition method based on improved deep learning under UAV cluster
  32. New formula to determine flyrock distance on sedimentary rocks with low strength
  33. Assessing the ecological security of tourism in Northeast China
  34. Effective reservoir identification and sweet spot prediction in Chang 8 Member tight oil reservoirs in Huanjiang area, Ordos Basin
  35. Detecting heterogeneity of spatial accessibility to sports facilities for adolescents at fine scale: A case study in Changsha, China
  36. Effects of freeze–thaw cycles on soil nutrients by soft rock and sand remodeling
  37. Vibration prediction with a method based on the absorption property of blast-induced seismic waves: A case study
  38. A new look at the geodynamic development of the Ediacaran–early Cambrian forearc basalts of the Tannuola-Khamsara Island Arc (Central Asia, Russia): Conclusions from geological, geochemical, and Nd-isotope data
  39. Spatio-temporal analysis of the driving factors of urban land use expansion in China: A study of the Yangtze River Delta region
  40. Selection of Euler deconvolution solutions using the enhanced horizontal gradient and stable vertical differentiation
  41. Phase change of the Ordovician hydrocarbon in the Tarim Basin: A case study from the Halahatang–Shunbei area
  42. Using interpretative structure model and analytical network process for optimum site selection of airport locations in Delta Egypt
  43. Geochemistry of magnetite from Fe-skarn deposits along the central Loei Fold Belt, Thailand
  44. Functional typology of settlements in the Srem region, Serbia
  45. Hunger Games Search for the elucidation of gravity anomalies with application to geothermal energy investigations and volcanic activity studies
  46. Addressing incomplete tile phenomena in image tiling: Introducing the grid six-intersection model
  47. Evaluation and control model for resilience of water resource building system based on fuzzy comprehensive evaluation method and its application
  48. MIF and AHP methods for delineation of groundwater potential zones using remote sensing and GIS techniques in Tirunelveli, Tenkasi District, India
  49. New database for the estimation of dynamic coefficient of friction of snow
  50. Measuring urban growth dynamics: A study in Hue city, Vietnam
  51. Comparative models of support-vector machine, multilayer perceptron, and decision tree ‎predication approaches for landslide ‎susceptibility analysis
  52. Experimental study on the influence of clay content on the shear strength of silty soil and mechanism analysis
  53. Geosite assessment as a contribution to the sustainable development of Babušnica, Serbia
  54. Using fuzzy analytical hierarchy process for road transportation services management based on remote sensing and GIS technology
  55. Accumulation mechanism of multi-type unconventional oil and gas reservoirs in Northern China: Taking Hari Sag of the Yin’e Basin as an example
  56. TOC prediction of source rocks based on the convolutional neural network and logging curves – A case study of Pinghu Formation in Xihu Sag
  57. A method for fast detection of wind farms from remote sensing images using deep learning and geospatial analysis
  58. Spatial distribution and driving factors of karst rocky desertification in Southwest China based on GIS and geodetector
  59. Physicochemical and mineralogical composition studies of clays from Share and Tshonga areas, Northern Bida Basin, Nigeria: Implications for Geophagia
  60. Geochemical sedimentary records of eutrophication and environmental change in Chaohu Lake, East China
  61. Research progress of freeze–thaw rock using bibliometric analysis
  62. Mixed irrigation affects the composition and diversity of the soil bacterial community
  63. Examining the swelling potential of cohesive soils with high plasticity according to their index properties using GIS
  64. Geological genesis and identification of high-porosity and low-permeability sandstones in the Cretaceous Bashkirchik Formation, northern Tarim Basin
  65. Usability of PPGIS tools exemplified by geodiscussion – a tool for public participation in shaping public space
  66. Efficient development technology of Upper Paleozoic Lower Shihezi tight sandstone gas reservoir in northeastern Ordos Basin
  67. Assessment of soil resources of agricultural landscapes in Turkestan region of the Republic of Kazakhstan based on agrochemical indexes
  68. Evaluating the impact of DEM interpolation algorithms on relief index for soil resource management
  69. Petrogenetic relationship between plutonic and subvolcanic rocks in the Jurassic Shuikoushan complex, South China
  70. A novel workflow for shale lithology identification – A case study in the Gulong Depression, Songliao Basin, China
  71. Characteristics and main controlling factors of dolomite reservoirs in Fei-3 Member of Feixianguan Formation of Lower Triassic, Puguang area
  72. Impact of high-speed railway network on county-level accessibility and economic linkage in Jiangxi Province, China: A spatio-temporal data analysis
  73. Estimation model of wild fractional vegetation cover based on RGB vegetation index and its application
  74. Lithofacies, petrography, and geochemistry of the Lamphun oceanic plate stratigraphy: As a record of the subduction history of Paleo-Tethys in Chiang Mai-Chiang Rai Suture Zone of Thailand
  75. Structural features and tectonic activity of the Weihe Fault, central China
  76. Application of the wavelet transform and Hilbert–Huang transform in stratigraphic sequence division of Jurassic Shaximiao Formation in Southwest Sichuan Basin
  77. Structural detachment influences the shale gas preservation in the Wufeng-Longmaxi Formation, Northern Guizhou Province
  78. Distribution law of Chang 7 Member tight oil in the western Ordos Basin based on geological, logging and numerical simulation techniques
  79. Evaluation of alteration in the geothermal province west of Cappadocia, Türkiye: Mineralogical, petrographical, geochemical, and remote sensing data
  80. Numerical modeling of site response at large strains with simplified nonlinear models: Application to Lotung seismic array
  81. Quantitative characterization of granite failure intensity under dynamic disturbance from energy standpoint
  82. Characteristics of debris flow dynamics and prediction of the hazardous area in Bangou Village, Yanqing District, Beijing, China
  83. Rockfall mapping and susceptibility evaluation based on UAV high-resolution imagery and support vector machine method
  84. Statistical comparison analysis of different real-time kinematic methods for the development of photogrammetric products: CORS-RTK, CORS-RTK + PPK, RTK-DRTK2, and RTK + DRTK2 + GCP
  85. Hydrogeological mapping of fracture networks using earth observation data to improve rainfall–runoff modeling in arid mountains, Saudi Arabia
  86. Petrography and geochemistry of pegmatite and leucogranite of Ntega-Marangara area, Burundi, in relation to rare metal mineralisation
  87. Prediction of formation fracture pressure based on reinforcement learning and XGBoost
  88. Hazard zonation for potential earthquake-induced landslide in the eastern East Kunlun fault zone
  89. Monitoring water infiltration in multiple layers of sandstone coal mining model with cracks using ERT
  90. Study of the patterns of ice lake variation and the factors influencing these changes in the western Nyingchi area
  91. Productive conservation at the landslide prone area under the threat of rapid land cover changes
  92. Sedimentary processes and patterns in deposits corresponding to freshwater lake-facies of hyperpycnal flow – An experimental study based on flume depositional simulations
  93. Study on time-dependent injectability evaluation of mudstone considering the self-healing effect
  94. Detection of objects with diverse geometric shapes in GPR images using deep-learning methods
  95. Behavior of trace metals in sedimentary cores from marine and lacustrine environments in Algeria
  96. Spatiotemporal variation pattern and spatial coupling relationship between NDVI and LST in Mu Us Sandy Land
  97. Formation mechanism and oil-bearing properties of gravity flow sand body of Chang 63 sub-member of Yanchang Formation in Huaqing area, Ordos Basin
  98. Diagenesis of marine-continental transitional shale from the Upper Permian Longtan Formation in southern Sichuan Basin, China
  99. Vertical high-velocity structures and seismic activity in western Shandong Rise, China: Case study inspired by double-difference seismic tomography
  100. Spatial coupling relationship between metamorphic core complex and gold deposits: Constraints from geophysical electromagnetics
  101. Disparities in the geospatial allocation of public facilities from the perspective of living circles
  102. Research on spatial correlation structure of war heritage based on field theory. A case study of Jinzhai County, China
  103. Formation mechanisms of Qiaoba-Zhongdu Danxia landforms in southwestern Sichuan Province, China
  104. Magnetic data interpretation: Implication for structure and hydrocarbon potentiality at Delta Wadi Diit, Southeastern Egypt
  105. Deeply buried clastic rock diagenesis evolution mechanism of Dongdaohaizi sag in the center of Junggar fault basin, Northwest China
  106. Application of LS-RAPID to simulate the motion of two contrasting landslides triggered by earthquakes
  107. The new insight of tectonic setting in Sunda–Banda transition zone using tomography seismic. Case study: 7.1 M deep earthquake 29 August 2023
  108. The critical role of c and φ in ensuring stability: A study on rockfill dams
  109. Evidence of late quaternary activity of the Weining-Shuicheng Fault in Guizhou, China
  110. Extreme hydroclimatic events and response of vegetation in the eastern QTP since 10 ka
  111. Spatial–temporal effect of sea–land gradient on landscape pattern and ecological risk in the coastal zone: A case study of Dalian City
  112. Study on the influence mechanism of land use on carbon storage under multiple scenarios: A case study of Wenzhou
  113. A new method for identifying reservoir fluid properties based on well logging data: A case study from PL block of Bohai Bay Basin, North China
  114. Comparison between thermal models across the Middle Magdalena Valley, Eastern Cordillera, and Eastern Llanos basins in Colombia
  115. Mineralogical and elemental analysis of Kazakh coals from three mines: Preliminary insights from mode of occurrence to environmental impacts
  116. Chlorite-induced porosity evolution in multi-source tight sandstone reservoirs: A case study of the Shaximiao Formation in western Sichuan Basin
  117. Predicting stability factors for rotational failures in earth slopes and embankments using artificial intelligence techniques
  118. Origin of Late Cretaceous A-type granitoids in South China: Response to the rollback and retreat of the Paleo-Pacific plate
  119. Modification of dolomitization on reservoir spaces in reef–shoal complex: A case study of Permian Changxing Formation, Sichuan Basin, SW China
  120. Geological characteristics of the Daduhe gold belt, western Sichuan, China: Implications for exploration
  121. Rock physics model for deep coal-bed methane reservoir based on equivalent medium theory: A case study of Carboniferous-Permian in Eastern Ordos Basin
  122. Enhancing the total-field magnetic anomaly using the normalized source strength
  123. Shear wave velocity profiling of Riyadh City, Saudi Arabia, utilizing the multi-channel analysis of surface waves method
  124. Effect of coal facies on pore structure heterogeneity of coal measures: Quantitative characterization and comparative study
  125. Inversion method of organic matter content of different types of soils in black soil area based on hyperspectral indices
  126. Detection of seepage zones in artificial levees: A case study at the Körös River, Hungary
  127. Tight sandstone fluid detection technology based on multi-wave seismic data
  128. Characteristics and control techniques of soft rock tunnel lining cracks in high geo-stress environments: Case study of Wushaoling tunnel group
  129. Influence of pore structure characteristics on the Permian Shan-1 reservoir in Longdong, Southwest Ordos Basin, China
  130. Study on sedimentary model of Shanxi Formation – Lower Shihezi Formation in Da 17 well area of Daniudi gas field, Ordos Basin
  131. Multi-scenario territorial spatial simulation and dynamic changes: A case study of Jilin Province in China from 1985 to 2030
  132. Review Articles
  133. Major ascidian species with negative impacts on bivalve aquaculture: Current knowledge and future research aims
  134. Prediction and assessment of meteorological drought in southwest China using long short-term memory model
  135. Communication
  136. Essential questions in earth and geosciences according to large language models
  137. Erratum
  138. Erratum to “Random forest and artificial neural network-based tsunami forests classification using data fusion of Sentinel-2 and Airbus Vision-1 satellites: A case study of Garhi Chandan, Pakistan”
  139. Special Issue: Natural Resources and Environmental Risks: Towards a Sustainable Future - Part I
  140. Spatial-temporal and trend analysis of traffic accidents in AP Vojvodina (North Serbia)
  141. Exploring environmental awareness, knowledge, and safety: A comparative study among students in Montenegro and North Macedonia
  142. Determinants influencing tourists’ willingness to visit Türkiye – Impact of earthquake hazards on Serbian visitors’ preferences
  143. Application of remote sensing in monitoring land degradation: A case study of Stanari municipality (Bosnia and Herzegovina)
  144. Optimizing agricultural land use: A GIS-based assessment of suitability in the Sana River Basin, Bosnia and Herzegovina
  145. Assessing risk-prone areas in the Kratovska Reka catchment (North Macedonia) by integrating advanced geospatial analytics and flash flood potential index
  146. Analysis of the intensity of erosive processes and state of vegetation cover in the zone of influence of the Kolubara Mining Basin
  147. GIS-based spatial modeling of landslide susceptibility using BWM-LSI: A case study – city of Smederevo (Serbia)
  148. Geospatial modeling of wildfire susceptibility on a national scale in Montenegro: A comparative evaluation of F-AHP and FR methodologies
  149. Geosite assessment as the first step for the development of canyoning activities in North Montenegro
  150. Urban geoheritage and degradation risk assessment of the Sokograd fortress (Sokobanja, Eastern Serbia)
  151. Multi-hazard modeling of erosion and landslide susceptibility at the national scale in the example of North Macedonia
  152. Understanding seismic hazard resilience in Montenegro: A qualitative analysis of community preparedness and response capabilities
  153. Forest soil CO2 emission in Quercus robur level II monitoring site
  154. Characterization of glomalin proteins in soil: A potential indicator of erosion intensity
  155. Power of Terroir: Case study of Grašac at the Fruška Gora wine region (North Serbia)
  156. Special Issue: Geospatial and Environmental Dynamics - Part I
  157. Qualitative insights into cultural heritage protection in Serbia: Addressing legal and institutional gaps for disaster risk resilience
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Heruntergeladen am 15.9.2025 von https://www.degruyterbrill.com/document/doi/10.1515/geo-2022-0742/html
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