Evaluation of Favorable Area for Coalbed Methane Development Based on Local Variable Weight Theory.

The evaluation of a favorable area is crucial for the exploration and exploitation of coalbed methane (CBM) resources. In traditional evaluation methods, the weight of controlling factors for the evaluation of favorable area is often obtained from different models and calculation methods, and the constant weight is commonly used in the entire target area. The influence of the index value of controlling factors and the combination state of these values on the weight is consistently overlooked during the evaluation process. In view of this phenomenon, a new evaluation method based on variable weight theory was introduced to enhance the accuracy of the result from evaluation (i.e., favorable area for CBM development) in this paper. Based on the raw data of controlling factors, the evaluation area was divided into a finite number of regular grids; each grid could be seen as an evaluation unit, and different attribute values were assigned to them. The constant weights are determined by the analytic hierarchy process (AHP), while the variable weight of controlling factors in each unit was calculated by a partitioned variable weight model (VWM) which constructed based on variable weight theory. Finally, the VWM for the evaluation of favorable area was constructed and applied in the Weibei CBM field. The influence of variability in index values on the weight was taken into consideration in this model, which can complement the disadvantage of the constant weight model (CWM). The accuracy of the result from the evaluation of favorable areas for CBM development could be improved by using this VWM, which provides a reasonable idea and method for the selection of target areas in CBM fields.

In Situ Stress Distribution of Deep Coals and Its Influence on Coalbed Methane Development in the Shizhuang Block, Qinshui Basin, China.

The in situ stress plays a crucial role in variations in coal permeability, hydraulic fracturing behavior, and accordingly coalbed methane (CBM) productivity. As the depth increases, the effects of in situ stress will become more prominent. In the Shizhuang block, present-day stress magnitude and permeability of coals at depths >800 m were measured with multiple-cycle hydraulic fracturing and injection falloff test, respectively. The results show that most seams are under pressure reservoirs with pressure gradient <0.9 MPa/100 m. Horizontal stress magnitudes and gradients tend to increase (800-1200 m) and then decrease (>1200 m) with increasing depth. Strike-slip fault stress regimes are predominant, while seams with depth >1400 m are subjected to a normal fault stress regime. Coal permeability tends to decrease gradually with depth and tends to be convergence to 0.01 mD. Considering extremely low permeability of these deep seams, hydraulic fracturing must be applied to create seepage channels for gas and water drainage. Although the high horizontal stress difference in deep seams is favorable for the generation of longer and simple hydraulic fractures, there is no obvious relations between fracture length and gas productivity as poor-support fractures and limited sand migration distance. The current hydraulic fracturing missed the variable stress regimes and permeability at various depth, but used the similar fracturing schemes, leading to significant reduction in gas productivity with depth. Using the high viscosity fracturing fluid, great sand volume, big injection rate, and low sand concentration are recommended for hydraulic fracturing. Single-branch horizontal well with staged fracturing show better applicability for deep CBM extraction.

Cerebral Organoid Arrays for Batch Phenotypic Analysis in Sections and Three Dimensions.

Organoids can recapitulate human-specific phenotypes and functions in vivo and have great potential for research in development, disease modeling, and drug screening. Due to the inherent variability among organoids, experiments often require a large sample size. Embedding, staining, and imaging each organoid individually require a lot of reagents and time. Hence, there is an urgent need for fast and efficient methods for analyzing the phenotypic changes in organoids in batches. Here, we provide a comprehensive strategy for array embedding, staining, and imaging of cerebral organoids in both agarose sections and in 3D to analyze the spatial distribution of biomarkers in organoids in situ. We constructed several disease models, particularly an aging model, as examples to demonstrate our strategy for the investigation of the phenotypic analysis of organoids. We fabricated an array mold to produce agarose support with microwells, which hold organoids in place for live/dead imaging. We performed staining and imaging of sectioned organoids embedded in agarose and 3D imaging to examine phenotypic changes in organoids using fluorescence micro-optical sectioning tomography (fMOST) and whole-mount immunostaining. Parallel studies of organoids in arrays using the same staining and imaging parameters enabled easy and reliable comparison among different groups. We were able to track all the data points obtained from every organoid in an embedded array. This strategy could help us study the phenotypic changes in organoids in disease models and drug screening.

Prediction Method of Coal Texture Considering Longitudinal Resolution of Logging Curves and Its Application-Taking No. 15 Coal Seam in the Shouyang Block as an Example.

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.

Multiscale Analysis of Cellular Composition and Morphology in Intact Cerebral Organoids.

Cerebral organoids recapitulate in vivo phenotypes and physiological functions of the brain and have great potential in studying brain development, modeling diseases, and conducting neural network research. It is essential to obtain whole-mount three-dimensional (3D) images of cerebral organoids at cellular levels to explore their characteristics and applications. Existing histological strategies sacrifice inherent spatial characteristics of organoids, and the strategy for volume imaging and 3D analysis of entire organoids is urgently needed. Here, we proposed a high-resolution imaging pipeline based on fluorescent labeling by viral transduction and 3D immunostaining with fluorescence micro-optical sectioning tomography (fMOST). We were able to image intact organoids using our pipeline, revealing cytoarchitecture information of organoids and the spatial localization of neurons and glial fibrillary acidic protein positive cells (GFAP+ cells). We performed single-cell reconstruction to analyze the morphology of neurons and GFAP+ cells. Localization and quantitative analysis of cortical layer markers revealed heterogeneity of organoids. This pipeline enabled acquisition of high-resolution spatial information of millimeter-scale organoids for analyzing their cell composition and morphology.

Application of Deep Learning in Plant-Microbiota Association Analysis.

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.

Quantitative optimization of drainage strategy of coalbed methane well based on the dynamic behavior of coal reservoir permeability.

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.