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Shale gas resource is heterogeneously distributed in Permian Gufeng Formation in the Xuancheng area, Anhui Province. This research aims to investigate the pore size distribution of the Permian Gufeng shales via 16 core samples analyses, combined with x‐ray diffraction, low‐pressure nitrogen gas adsorption (N2‐GA), CO2 gas adsorption (CO2‐GA) and scanning electron microscopy (SEM). N2‐GA shows that the nanopores (diameter < 10 nm) are dominated and developed in the samples. The Brunauer–Emmett–Teller (BET) specific surface area (SSA) of the samples ranges from 6.7051 to 29.4384 m2/g, with a mean of 16.0931 m2/g. CO2‐GA results illustrate that micropore is well developed, especially the pores with a diameter of 0.5–0.6 and 0.8 nm. The total organic carbon (TOC) content of the Gufeng shale ranges from 0.42% to 12.82% with an average content of 6.44%, which has both positive correlation with pore SSA and pore volume, while carbonate mineral content has a positive correlation with pore SSA and the volume of macropore and mesopore. Clay mineral content is a negative correlation with pore SSA and micropore volume. Therefore, the high‐maturity Permian Gufeng shale could be more preferential high‐quality gas shale reservoir for further exploration and development in the Xuancheng area, which has more than 4% TOC content, relatively higher carbonate mineral content and relatively lower clay mineral content. [ABSTRACT FROM AUTHOR]

Development of unconventional shale gas reservoirs (SGRs) has been boosted by the advancements in two key technologies: horizontal drilling and multi-stage hydraulic fracturing. A large number of multi-stage fractured horizontal wells (MsFHW) have been drilled to enhance reservoir production performance. Gas flow in SGRs is a multi-mechanism process, including: desorption, diffusion, and non-Darcy flow. The productivity of the SGRs with MsFHW is influenced by both reservoir conditions and hydraulic fracture properties. However, rare simulation work has been conducted for multi-stage hydraulic fractured SGRs. Most of them use well testing methods, which have too many unrealistic simplifications and assumptions. Also, no systematical work has been conducted considering all reasonable transport mechanisms. And there are very few works on sensitivity studies of uncertain parameters using real parameter ranges. Hence, a detailed and systematic study of reservoir simulation with MsFHW is still necessary. In this paper, a dual porosity model was constructed to estimate the effect of parameters on shale gas production with MsFHW. The simulation model was verified with the available field data from the Barnett Shale. The following mechanisms have been considered in this model: viscous flow, slip flow, Knudsen diffusion, and gas desorption. Langmuir isotherm was used to simulate the gas desorption process. Sensitivity analysis on SGRs’ production performance with MsFHW has been conducted. Parameters influencing shale gas production were classified into two categories: reservoir parameters including matrix permeability, matrix porosity; and hydraulic fracture parameters including hydraulic fracture spacing, and fracture half-length. Typical ranges of matrix parameters have been reviewed. Sensitivity analysis have been conducted to analyze the effect of the above factors on the production performance of SGRs. Through comparison, it can be found that hydraulic fracture parameters are more sensitive compared with reservoir parameters. And reservoirs parameters mainly affect the later production period. However, the hydraulic fracture parameters have a significant effect on gas production from the early period. The results of this study can be used to improve the efficiency of history matching process. Also, it can contribute to the design and optimization of hydraulic fracture treatment design in unconventional SGRs. [ABSTRACT FROM AUTHOR]

Assessment of production potential and prediction of sweet spots in unexploited shale gas wells are crucial technologies for achieving a high success rate in drilling. Most existing methods are only applicable for yield forecasting of shale gas wells with known geological and drilling/completion factors after production. In addition, the shale gas reservoir in the Longmaxi Formation in the southern Sichuan Basin of China is characterized by low porosity, low permeability, and diverse gas reservoir states. Thus, forecasting production from unexploited wells and predicting the exact location of the sweet spot under complex geological conditions have become challenging topic for both academia and industry. According to the first law of geography, shale gas wells that are closer in space have more similar geologic features. Based on this foundation, a machine-learning prediction model based on spatial correlation (SC-MLPM) is proposed for evaluating potential production of shale gas wells in the Changning play located in Longmaxi Formation in the southern Sichuan basin, China. Specifically, an improved K -nearest neighbor algorithm (SC-KNN) based on the spatial correlation characteristics of the exploited wells is designed to estimate the relevant geological factors of the unexploited wells in the same area. In addition, an extreme gradient lifting model based on Huber loss function (Hu-XGB) is developed to predict the potential production of unexploited shale gas wells, and then the shale gas sweet spot area in the research area is predicted quickly and accurately. The experimental results on 88 shale gas wells from Changning play show that the mean relative error of the prediction results of the proposed method is 9.8%. Furthermore, the results also determine that the sweet spots of shale gas wells in the research area are mainly developed in the northwest and northeast directions, and gradually become worse in the southeast direction. Eventually, experimental results for the Fox Creek play in Canada show that the proposed model obtains better predictive performance as the number of gas wells increases, and also verifies the generalizability. This work provides a basis for the efficient development of the research area. • A machine-learning prediction model based on spatial correlation is proposed. • An improved KNN algorithm is designed on the first law of geography. • An extreme gradient lifting model is developed to predict potential production. [ABSTRACT FROM AUTHOR]