The development of coalbed methane (CBM) is not only affected by geological factors, but also by engineering factors, such as artificial fracturing and drainage strategies. In order to optimize drainage strategies for wells in unique geological conditions, the characteristics of different stages of CBM production are accurately described based on the dynamic behavior of the pressure drop funnel and coal reservoir permeability. Effective depressurization is achieved by extending the pressure propagation radius and gas desorption radius to the well-controlled boundary, in the single-phase water flow stage and the gas–water flow stage, respectively, with inter-well pressure interference accomplished in the single-phase gas flow stage. A mathematic model was developed to quantitatively optimize drainage strategies for each stage, with the maximum bottom hole flow pressure (BHFP) drop rate and the maximum daily gas production calculated to guide the optimization of CBM production. Finally, six wells from the Shizhuangnan Block in the southern Qinshui Basin of China were used as a case study to verify the practical applicability of the model. Calculation results clearly indicate the differences in production characteristics as a result of different drainage strategies. Overall, if the applied drainage strategies do not achieve optimal drainage results, the coal reservoir could be irreversibly damaged, which is not conducive to expansion of the pressure drop funnel. Therefore, this optimization model provides valuable guidance for rational CBM drainage strategy development and efficient CBM production.
Unraveling the association between microbiome and plant phenotype can illustrate the effect of microbiome on host and then guide the agriculture management. Adequate identification of species and appropriate choice of models are two challenges in microbiome data analysis. Computational models of microbiome data could help in association analysis between the microbiome and plant host. The deep learning methods have been widely used to learn the microbiome data due to their powerful strength of handling the complex, sparse, noisy, and high-dimensional data. Here, we review the analytic strategies in the microbiome data analysis and describe the applications of deep learning models for plant–microbiome correlation studies. We also introduce the application cases of different models in plant–microbiome correlation analysis and discuss how to adapt the models on the critical steps in data processing. From the aspect of data processing manner, model structure, and operating principle, most deep learning models are suitable for the plant microbiome data analysis. The ability of feature representation and pattern recognition is the advantage of deep learning methods in modeling and interpretation for association analysis. Based on published computational experiments, the convolutional neural network and graph neural networks could be recommended for plant microbiome analysis.
The quantitative identification of the coal texture is of great importance as a crucial parameter for coalbed methane (CBM) reservoir evaluation. This study combined drilling core data, electrical imaging logging data, and four conventional logging data, namely, compensation density (DEN), natural γ (GR), deep lateral resistivity (RD), and acoustic time difference (AC), to achieve accurate inversion of coal texture in the Shouyang Block. Meanwhile, wavelet analysis and Fisher discriminant analysis were introduced to the inversion process to further improve the accuracy. Through the utilization of software packages, such as Matlab and SPSS, the establishment of the coal texture logging interpretation chart of the No. 15 coal seam in the Shouyang block was successfully realized. The outcome of this comprehensive study reveals that the coal texture logging interpretation chart is an effective tool for the identification and classification of each coal texture and gangue. Moreover, the validity and reliability of this method were tested and confirmed using wells CS-8 and CS-9 in the region, achieving an accuracy of 97.1 and 93.2%, respectively. This innovative method has significant prospects for predicting and evaluating the coal texture in the Shouyang Block, which can be further applied to other regions.
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