Southern Qinshui Basin Research Articles

Analysis of Carboniferous‐Permian coal‐accumulating environment and coalbed methane enrichment in the southern Qinshui Basin

Global research on the geological particularities of coal‐accumulating environments and the enrichment patterns of coalbed methane (CBM) is still relatively weak. This paper studies the geological characteristics and coal accumulation patterns of Carboniferous‐Permian coal‐bearing seams in the southern Qinshui Basin. The results show that the coal‐bearing strata in the basin represent the offshore marine and continental intercalated coal seam sediments developed. The southern part of the basin is dominated by lacustrine facies, and the delta front sub‐facies is locally developed. The characteristics of coal accumulation environment in the southern Qinshui Basin are characterized by basement properties, fault networks and coal‐bearing sedimentary facies, which collectively control the spatial and temporal distribution characteristics of the coal‐rich belt. The differences in coal accumulation environment in the southern basin affect the tectonic evolution, the thickness of the coal seam roof and floor and the hydrogeological conditions of the coal‐rich area, which further control the CBM enrichment in the Taiyuan‐Shanxi Formation. The aim of this study is to explore the factors of CBM enrichment and gas control mode, so as to enrich and improve the theory and method of CBM exploration, which will also help identify the appropriate underground geological structure environment for carbon sequestration (CCS), reduce greenhouse gas emissions and effectively respond to the challenge of global warming.

Fracturing Construction Curves and Fracture Geometries of Coals in the Southern Qinshui Basin, China: Implication for Coalbed Methane Productivity.

Hydraulic fracturing technology has become a common practice to enhance the permeability of coal seams, and its effectiveness significantly impacts the productivity of coalbed methane (CBM) wells. In this study, multiple data sets, including fracturing reports, productivity data, and microseismic monitoring, were utilized to analyze the factors influencing fracturing effectiveness and gas well production at the southern margin of the Qinshui Basin, China, especially the Zhengzhuang and Fanzhuang blocks. Statistics revealed that the fracturing displacement, liquid consumption, and sand consumption were 8 m3/min, 17.69-1386.52 m3 (averaging 728.42 m3), and 28.5-46.1 m3 (averaging 40 m3), respectively. There were differences in the types of fracturing curves among blocks or well groups, and the natural fracture system was identified as the key factor affecting their characteristics. The coal seams with high density of microfractures in Zhengzhuang reduce the fracture threshold of reservoirs during hydraulic fracturing, resulting in a lower fracture response ratio than that of Fanzhuang (71.0 vs 80.3%). Well groups with higher microfracture width in coal seams (Group-ZC and DS) experienced a further reduction in fracture response (averaging 67.2 vs 80.3%), and the connection of hydraulic fractures (HFs) with these high-permeability microfractures lead to an increase in the proportion of fluctuating fracturing curves (averaging 52.2 vs 33.6%). Regional structural features and fracturing effectiveness jointly affected the production of CBM wells. The productivity in Zhengzhuang was lower than that in Fanzhuang due to the highly developed faults and deeply buried coal seams. Shallow coal seams with a high width of microfractures and a low-stress environment were easily supported by a proppant, forming a complex HF network and yielding a high productivity (Group-ZC and DS). For other deeper well groups, proppant migration became unfavorable once high-angle and complex branch HF clusters formed in coal seams, leading to local low-efficiency wells and fluctuating fracturing curves.

Identification of Interwell Interference Based on Hydrogeochemical Characteristics of Produced Water from Coalbed Methane Wells: A Case Study in the Southern Qinshui Basin, China

Summary Interwell interference is the superposition effect of coalbed methane (CBM) reservoir pressure. This study aims to provide a new direction for the quantitative analysis of interwell interference from the hydrogeochemical characteristics of produced water from CBM wells. A total of 24 produced water samples collected from the Panhe (PH) group, Shizhuangnan (SZN)-1 group, and SZN-2 group in Qinshui Basin were selected for the comparative analysis. The water type of all water samples is characterized by Na-HCO3, with Na+ being the main total dissolved solids (TDS) provider. The self-similar major ionic characteristics of the PH and SZN-2 groups are prone to the occurrence of interwell interference. The δD and δ18O show that the main source of produced water is atmospheric circulating water. The similar isotope characteristics of produced water in the PH and SZN-2 groups represent that there is remarkable interwell interference. Sr, As, Cu, Ga, Li, Rb, Sn, Mo, and V are selected as indicator elements. In the cluster analysis, all CBM wells form a single cluster in the PH and SZN-2 groups in the first three iterations, indicating interwell interference. According to the established fuzzy discriminative model, interwell interference is divided into two types—strong interwell interference and weak interwell interference. Most CBM wells in the PH and SZN-2 groups show strong interwell interference. This study can provide theoretical foundations for the dynamic pressure regulation and well pattern infilling of CBM wells.

The Influence of Coal Body Structure on Coal Fines’ Output Characteristics in the Southern Qinshui Basin

Large amounts of coal fines are discharged from coalbed methane wellheads in the Qinshui Basin, obstructing the continuity of drainage; their extraction poses significant hazards. This paper recognized the coal body structure of 30 coalbed methane wells in the study region, using the integrated identification method of logging curve and tectonic curvature. The research found that the primary structural coal output of coal fines concentration averaged 0.237 g/L, the average content of particle size 10–100 μm was 58.88%, the average range of particle size 1–10 μm was 22.91%, and the main form was irregular columns and lumps. The average concentration of fractured structural coal fines was 1.169 g/L, the average content of particle size 10–100 μm was 41.73%, the average range of particle size 1–10 μm was 31.77%, and the main form was balls and lumps. The average concentration of granulated-mylonitic structured coal fines was 3.156 g/L, the average content of particle size 10–100 μm was 25.26%, the average range of particle size 1–10 μm was 57.59%, and the coal fines were mainly in the form of clusters and flaky aggregates.

Geochemical Characteristics and Development Significances of Constant and Trace Elements from Coalbed Methane Co-Produced Water: A Case Study of the Shizhuangnan Block, the Southern Qinshui Basin

Geochemical Characteristics and Development Significances of Constant and Trace Elements from Coalbed Methane Co-Produced Water: A Case Study of the Shizhuangnan Block, the Southern Qinshui Basin

Spatial and Temporal Change of Vegetation SIF based on Downscaled GOSIF data in Coalbed Methane Enrichment Area —— A Case Study of Southern Qinshui Basin

Spatial and Temporal Change of Vegetation SIF based on Downscaled GOSIF data in Coalbed Methane Enrichment Area —— A Case Study of Southern Qinshui Basin

Main controlling factor of coalbed methane enrichment area in southern Qinshui Basin, China

Despite the significant progress made in coalbed methane (CBM) exploration and development in recent years, understanding of CBM enrichment mechanisms remains limited. This study aims to elucidate the CBM enrichment mechanism in the southern Qinshui Basin, China, by analyzing characteristics of global CBM basins and building a geological model of the study area. Field analyses are conducted to predict sweet spots of high CBM abundance and production potential. The findings reveal a high-yield model of CBM accumulation at relatively elevated structural positions within enriched areas. Compared to other global basins, low permeability poses the primary challenge for CBM development in China. Coal seam thickness shows minimal variation in southern Qinshui Basin, exerting negligible impact on CBM productivity. The shallow burial depth of coal seams in this region results in low stress, conferring high permeability conducive to high CBM yields. In situ stress conditions exert a primary control on the development of microfracture systems, which in turn govern reservoir permeability. This work provides new insights into CBM enrichment patterns in the southern Qinshui Basin. The proposed high-yield model enables better understanding of favorable conditions for CBM accumulation. Overall, this study represents a valuable contribution toward unlocking China’s CBM potential through improved geological characterization.

Fuzzy pattern recognition model of geological sweetspot for coalbed methane development

From the perspective of geological zone selection for coalbed methane (CBM) development, the evaluation parameters (covering geological conditions and production conditions) of geological sweetspot for CBM development are determined, and the evaluation index system of geological sweetspot for CBM development is established. On this basis, the fuzzy pattern recognition (FPR) model of geological sweetspot for CBM development is built. The model is applied to evaluate four units of No.3 Coal Seam in the Fanzhuang Block, southern Qinshui Basin, China. The evaluation results are consistent with the actual development effect and the existing research results, which verifies the rationality and reliability of the FPR model. The research shows that the proposed FPR model of geological sweetspot for CBM development does not involve parameter weighting which leads to uncertainties in the results of the conventional models such as analytic hierarchy process and multi-level fuzzy synthesis judgment, and features a simple computation without the construction of multi-level judgment matrix. The FPR model provides reliable results to support the efficient development of CBM.

Impacts of vertical variation of coal seam structure on hydraulic fracturing and resultant gas and water production: A case study on the Shizhuangnan Block, Southern Qinshui Basin, China

Hydraulic fracturing plays a vital role in the development of coalbed methane (CBM). Coal seam structures can, however, affect the fracturing operations, further affecting gas and water production from the coal seam. Within the research area—the Shizhuangnan Block, Southern Qinshui Basin, China—three types of coal body textures are relevant, including original, cataclastic, and granulated structure. This research describes the impacts of different coal seam types on fracturing operations and gas and water production. The results show that different types of coal seams have different impacts on hydraulic fracturing and the resultant production of gas and water. The coal seams are classified according to the vertical variation characteristics of the coal seam structures. The first type of coal seam (Type I) only develops the original and cataclastic coal structure. The effects of fracturing reconstruction for Type I are good. A high degree of coal breakage and fracture formation is observed. Gas production from CBM wells in Type I coalbeds is usually high because the water in the coal reservoir can be discharged smoothly at an early stage. The second type of coal seam (Type II) develops an additional layer of granulated coal compared with Type I. During the fracturing process, breakage of the coal seam is obvious, and the granulated coal can easily produce fine-grained coal that can block the pores and fractures, which causes an increase and fluctuation of oil pressure and can affect the effectiveness of fracturing. The gas production curve from CBM wells in Type II coal seams is mostly bimodal and water production depends on whether the pulverized coal blocks the pores and fractures. The third type of coal seam (Type III) develops two additional layers of granulated coal compared with Type I, with a greater proportion of granulated coal present. A low degree of coal breakage and fracture formation is observed, and fractures are easily blocked by pulverized coal. The effects of fracturing reconstruction for Type III are bad. Forming an effective seepage channel in this type of coal seam and extending the fracture to the far end is difficult. The gas production from CBM wells in Type III coal seams is usually low, and water production is generally low during the whole drainage period.

Evaluation of Reconstruction Potential for Low-Production Vertical Wells of CBM in the Southern Qinshui Basin

Production practice has shown that not all low-production coalbed methane (CBM) wells can be reconstructed into high-production wells through secondary stimulation, so it is necessary and timely to establish an evaluation index system, form an evaluation method, and evaluate the reconstruction potential of low-production wells. Based on the development practice of CBM in the southern Qinshui Basin, this paper analyzes the influencing factors of low production in vertical wells from the aspects of coal and rock reservoir conditions, drilling and completion engineering, and drainage engineering. It is proposed that the evaluation of the reconstruction potential of low-production wells should focus on the quality of CBM resources, the difficulty of CBM desorption and diffusion, and the degree of damage to coal reservoirs caused by the initial reservoir stimulation. Twelve parameters, including gas content, gas saturation, reservoir pressure gradient, critical desorption–reservoir pressure ratio, and permeability, were systematically selected as evaluation indicators, and the grading reference values for each evaluation indicator were comparatively given. Then, a multi-factor comprehensive evaluation method for the reconstruction potential of low-production wells based on gray correlation analysis method was established. The reconstruction potential of low-production wells was divided into three levels: high, medium, and low. When reconstructing low-production wells, it is recommended to prioritize the low-production wells with high reconstruction potential, followed by those with medium reconstruction potential, while low-production wells with low reconstruction potential are not recommended for reconstruction. Finally, the evaluation method was used to evaluate the reconstruction potential of five low-production wells in a CBM block, and suggestions for the reconstruction order and reconstruction potential levels for each well were given.

Characterization of Coal Fines and their Production Controlling Factors: A Case Study from Southern Qinshui Basin, China

Characterization of Coal Fines and their Production Controlling Factors: A Case Study from Southern Qinshui Basin, China

Multi-stage gas diffusion and its implications for the productivity of coalbed methane in the southern Qinshui Basin, north China

Multi-stage gas diffusion and its implications for the productivity of coalbed methane in the southern Qinshui Basin, north China

Microbiome of High-Rank Coal Reservoirs in the High-Production Areas of the Southern Qinshui Basin.

To study the distribution features of microorganisms in distinct hydrological areas of the southern Qinshui Basin, C-N-S microorganisms were studied using 16S RNA sequencing, metagenome sequencing and geochemical technologies, showing the high sensitivity of microorganisms to the hydrodynamic dynamics of coal. The hydrodynamic intensity of the #3 coal gradually decreased from the runoff areas to the stagnant areas. The stagnant zones have higher reservoir pressure, methane content, δ13CDIC and TDS and lower SO42-, Fe3+ and NO3- concentrations than the runoff areas. C-N-S-cycling microorganisms, including those engaged in methanogenesis, nitrate respiration, fermentation, nitrate reduction, dark oxidation of sulfur compounds, sulfate respiration, iron respiration, chlorate reduction, aromatic compound degradation, denitrification, ammonification and nitrogen fixation, were more abundant in the stagnant areas. The relative abundance of C-N-S functional genes, including genes related to C metabolism (e.g., mcr, mer, mtr, fwd and mtd), N metabolism (e.g., nifDKH, nirK, narGHI, nosZ, amoB, norC and napAB) and sulfur metabolism (e.g., dsrAB and PAPSS), increased in the stagnant zones, indicating that there was active microbiological C-N-S cycling in the stagnant areas. The degradation and fermentation of terrestrial plant organic carbon and coal seam organic matter could provide substrates for methanogens, while nitrogen fixation and nitrification can provide nitrogen for methanogens, which are all favorable factors for stronger methanogenesis in stagnant areas. The coal in the study area is currently in the secondary biogenic gas generation stage because of the rising of the strata, which recharges atmospheric precipitation. The random forest model shows that the abundance of C-N-S microorganisms and genes could be used to distinguish different hydrological zones in coal reservoirs. Since stagnant zones are usually high-gas-bearing zones and high-production areas of CBM exploration, these microbiological indicators can be used as effective parameters to identify high-production-potential zones. In addition, nitrate respiration and sulfate respiration microorganisms consumed NO3- and SO42-, causing a decrease in the content of these two ions in the stagnant areas.

Intelligent classification of coal structure using multinomial logistic regression, random forest and fully connected neural network with multisource geophysical logging data

The structure of coal indicates the degree of its fragmentation after tectonic movement, which affects the exploration and development of coalbed methane (CBM). Although coal core observations are the most convenient and intuitive way of identifying the coal structure, they are not applicable for use in unexplored coal seams without CBM wells, and they are also very time-consuming. In comparison, geophysical-logging interpretation of the coal structure is more efficient and economical. However, although qualitative methods, such as principal component analysis (PCA), can be used to identify the coal structure with geophysical logging, the interpretation is limited by the calculation ability, and improvements are required based on the structure of an empirical model. Multinomial logistic regression (MLR), random forest (RF), and deep fully connected neural network (DNN) are effective machine learning methods more accurate than the traditional method that with model-aided identification. In this respect, the MLR method is a classical method based on mathematical linear regression, and it has a low construction cost; RF is an ensemble learning algorithm based on a decision tree and use of a bagging algorithm; and DNN is a deep learning model based on self-built feature engineering that has high classification accuracy under a large amount of data training and provides obvious advantages in visual coal classification problems. In this work, the three machine learning methods, MLR, RF, and DNN, were used to identify the coal structure. Two sets of logging data comprising different quantities from the Anze Block of the southern Qinshui Basin, North China, were selected to quantitatively compare the accuracy of coal structure identification with partial coal core observation. The results showed that for 210 and 840 samples, respectively, the accuracy was 76% and 77% for MLR, 83% and 86% for RF, and 82% and 86% for DNN. These results show that the MLR and DNN methods are superior for use with minimal and maximum amounts of data, respectively, and the RF method provides overall accuracy. Furthermore, an algorithmic classification of the coal structure was established, and the geological factors controlling the predicted structure, such as geostress, coal seam thickness, and burial depth, were distinguished.

Prediction of coalbed methane content based on seismic identification of key geological parameters: a case in a study area, Southern Qinshui Basin

Prediction of coalbed methane content based on seismic identification of key geological parameters: a case in a study area, Southern Qinshui Basin

CBM Gas Content Prediction Model Based on the Ensemble Tree Algorithm with Bayesian Hyper-Parameter Optimization Method: A Case Study of Zhengzhuang Block, Southern Qinshui Basin, North China

Gas content is an important parameter for evaluating coalbed methane reservoirs, so it is an important prerequisite for coalbed methane resource evaluation and favorable area optimization to predict the gas content accurately. To improve the accuracy of CBM gas content prediction, the Bayesian hyper-parameter optimization method (BO) is introduced into the random forest algorithm (RF) and gradient boosting decision tree algorithm (GBDT) to establish CBM gas content prediction models using well-logging data in the Zhengzhuang block, south of Qinshui Basin, China. As a result, the GBDT model based on the BO method (BO-GBDT model) and the RF model based on the BO method (BO-RF model) were proposed. The results show that the mean-square-error (MSE) of the BO-RF model and the BO-GBDT model can be reduced by 8.83% and 37.94% on average less than that of the RF and GBDT modes, indicating that the accuracy of the models optimized by the BO method is improved. The prediction effect of the BO-GBDT model is better than that of the BO-RF model, especially in low gas content wells, and the R-squared (RSQ) of the BO-GBDT model and the BO-RF model is 0.82 and 0.66. The accuracy order of different models was BO-GBDT > GBDT > BO-RF > RF. Compared with other models, the gas content curve predicted by the BO-GBDT model has the best fitness with the measured gas content. The rule of gas distribution predicted by all four models is consistent with the measured gas content distribution.

Fracture propagation induced by hydraulic fracturing using microseismic monitoring technology: Field test in CBM wells in Zhengzhuang region, Southern Qinshui Basin, China

Hydraulic fracturing is an important technical measure for coalbed methane (CBM) development, and the propagation of fractures in the target coal seam induced by hydraulic fracturing is related to the stimulation performance in CBM recovery. Therefore, effective monitoring of fracture development during reservoir fracturing is critical for CBM engineering. In this paper, the microseismic technology was used to monitor the spatial and temporal characteristics of the fracture extension in the CBM well during hydraulic fracturing in Zhengzhuang Region, Southern Qinshui Basin, China. Based on the microseismic fracture scanning data and imaging processing, the three-dimensional shape of fractures in vertical wells after fracturing can be quantified, and for the vertical well ZHSY-1, the main fracture direction is identified as NE106°, and the fracture length is 426 m, and the fracture area of coal seam is 1.6 × 105 m2. It is found that fracturing does not develop continuously in time and space with fracturing fluid injection, and the fracture regions are scattered throughout the space, and the formation and development of fracture regions are intermittent. After fracturing with a large amount of fracturing fluid injection in Well ZHSY-1, the fracture area has been significantly increased, and the well gas production has been significantly improved, which is confirmed by the field CBM well data. This study provides a field application case for studying the effect of hydraulic fracturing fracture propagation using microseismic technology, which can be used as a reference for fracturing engineering in CBM development.

Field experiment of stress sensitivity effect in the Mabidong CBM block, southern Qinshui Basin, China

Coalbed methane (CBM) has made significant contributions to global energy supply and carbon neutrality. The stress sensitivity effect (SSE) is an important factor that influences CBM production, and it indicates that reservoir properties change with stress. Although there is abundant research on the SSE in CBM, most studies are based on theoretical analyses or laboratory physical simulations. Only few studies focused on the SSE of CBM during field production. Therefore, this study aims to investigate the SSE through a field experiment. To this end, a field well from the Mabidong CBM block in the southern Qinshui Basin is selected. Then, the corresponding experimental installations are set up. A parametric inversion method based on the Newman product method and type curve matching is established to interpret the experimental data. The entire experimental process includes six stages of pressure build tests, lasting for 290 days. The field experiment data are inverted using the parametric inversion method. The SSE in the CBM during production is revealed by analyzing the variations in the formation parameters of each stage. The results indicate that both the permeability and fracture half-length are related to the average pressure. The average pressure declines as the production proceeds, and this leads to an increase in the effective stress and a decrease in the permeability and fracture half-length. Relationships between permeability, fracture half-length, and average pressure are derived, and they can be used to better interpret the dynamic production data of CBM wells in the Mabidong Block. Moreover, these results indicate that decreasing the flow rate can mitigate the SSE, especially when the gas desorbs. Thus, during CBM production, the flow rate needs to be strictly controlled for maintaining a stable pressure drop and reducing reservoir damage caused by the SSE.

Identification of coal structures by semi-supervised learning based on limited labeled logging data

Coal structure is a critical parameter in coalbed methane (CBM) development due to its significant impacts on methane enrichment, fluid flow and hydraulic fracturing. Traditional statistical analysis and data-driven machine learning methods for coal structure identification are highly dependent on the labeled logging data and have potential limitations when labeled logging data is limited. To address this issue, this paper proposed a semi-supervised learning method based on Laplacian support vector machine (LapSVM) to identify coal structure by using few labeled logging data. By mining the structure information from abundant unlabeled data, LapSVM can improve the model performance and alleviate the over-reliance on labeled data. To evaluate and verify the effectiveness and reliability of the proposed LapSVM method in coal structure identification, datasets collected from 32 CBM wells in the southern Qinshui Basin, China, are utilized in this study. The particle swarm optimization (PSO) is adopted for parameter optimization of LapSVM models. For the LapSVM model, the addition of unlabeled data is conducive to enhance model accuracy, and unavoidably increases the computational cost at the same time. The comparison of training, testing and blind-well test results between the LapSVM and standard support vector machine (SVM) models indicates that the LapSVM outperforms traditional SVM and possesses higher accuracy and generalization in coal structure identification. It has been demonstrated that the LapSVM can be a reliable tool for coal structure identification when limited labeled logging data is available.

Stress sensitivity of coal reservoir and its impact on coalbed methane production in the southern Qinshui Basin, north China

Stress sensitivity of coal reservoir and its impact on coalbed methane production in the southern Qinshui Basin, north China