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The objective of this study is to construct a refined microscopic transport model that elucidates the transport mechanisms of gas flow within micro-nano pores and fractures. The collective impact of various microscopic transport mechanisms was explained through the apparent permeability model, specifically related to gases such as methane and carbon dioxide, within the shale matrix. The apparent permeability models, taking into account microscopic transport mechanisms such as slippage flow, Knudsen diffusion, transition flow, and surface diffusion, were established individually. Subsequently, the influencing factors on apparent permeability were analyzed. The results demonstrate that the apparent permeability of the shale reservoir matrix is significantly influenced by pore pressure, temperature, pore size, and total organic carbon (TOC). As pressure decreases, the apparent permeability of Knudsen diffusion and surface diffusion increases, while the apparent permeability of slippage flow decreases. In addition, the apparent permeability of the reservoir matrix initially decreases and then increases. With increasing temperature, the apparent permeability of slippage flow, Knudsen diffusion, and surface diffusion all increase, as does the apparent permeability of the reservoir matrix. As pore size increases, the apparent permeability of surface diffusion and Knudsen diffusion decreases, while the apparent permeability of slippage flow and the reservoir matrix increases. Furthermore, an increase in TOC leads to no change in the apparent permeability of slippage flow and Knudsen diffusion, but an increase in the apparent permeability of surface diffusion and the reservoir matrix. The model presented in this paper enhances the multi-scale characterization of gas microflow mechanisms in shale and establishes the macroscopic application of these micro-mechanisms. Moreover, this study provides a theoretical foundation for the implementation of carbon capture, utilization, and storage (CCUS) in shale gas production. [ABSTRACT FROM AUTHOR]

One of the main techniques for the exploitation of shale oil and gas is hydraulic fracturing, and the fracturing fluid (slick water) may interact with minerals during the fracturing process, which has a significant effect on the shale pore structure. In this study, the pore structure and fluid distribution of shale samples were analyzed by utilizing low-pressure liquid nitrogen adsorption (LP-N2GA) and nuclear magnetic resonance (NMR). The fractal analysis showed that the pore structure of the sample was strongly heterogeneous. It was also found that the effect of slick water on pore structure can be attributed to two phenomena: the swelling of clay minerals and the dissolution of carbonate minerals. The swelling and dissolution of minerals can exist at the same time, and the strength of them at different soaking times is different, leading to the changes in specific surface area and pore size. After the samples were soaked in the slick water for two days, the contact angle reached the minimum value (below 8°), which means the sample is strongly hydrophilic; then the contact angle increased to above 38° with longer soaking times. The connected pore space in the shale matrix is enlarged by the soaking processing. Therefore, an in-depth understanding of the interaction between the fracking fluid and shale is essential to deepen our understanding of changes in the pore structure in the reservoir and the long-term productivity of shale gas. [ABSTRACT FROM AUTHOR]

Horizontal well drilling and hydraulic fracturing technologies play an essential role in improving gas recovery from shale reservoirs. However, they are expensive technologies and resource intensive production strategies. Optimization design of horizontal well spacing and fracture stage placement helps in striking a balance between gas production and economic benefits. However, previous researches relied mainly on numerical simulation technique which is computationally expensive and time-consuming. To reduce the computational burden, a novel multi-fidelity support vector regression (MFSVR) surrogate model assisted horizontal well spacing and fracture stage placement integrated optimization method, namely WSF-MFSVR, is proposed in this study. In the WSF-MFSVR method, both low-fidelity (LF) and high-fidelity (HF) numerical simulation models were applied to establish the multi-fidelity (MF) surrogate model so as to lessen the computational burden and guarantee its quality. In order to enhance the evaluation accuracy, the particle swarm optimization (PSO) algorithm was adopted to find the optimal hyper-parameters of the MFSVR model. Two cases with different wells and fracture types based on the shale gas reservoir with Barnett shale properties were employed to verify the WSF-MFSVR method. The results indicated that a combination of 300 H F and 3500 LF samples was the most suitable for establishing the MFSVR model to approximate the numerical simulation model. In terms of computational efficiency, the WSF-MFSVR method was about 50 times faster than the HF numerical simulation model-based method. Furthermore, the relative hyper-area difference (RHD) and overall spread (OS) of the WSF-MFSVR method were similar to that of the HF numerical simulation model-based method. However, the RHD and OS of the WSF-MFSVR method were superior to that of the LF numerical simulation model-based and single-fidelity support vector regression model-assisted methods. The data of these indexes quantitatively showed the superior convergence and diversity of the final optimal solutions obtained by WSF-MFSVR method. • A novel integrated optimization design method for shale gas is established. • The multi-fidelity support vector regression is proposed to assist well spacing and fracture optimization. • This method provides a fast optimization approach for shale gas well space and fracture scheme. [ABSTRACT FROM AUTHOR]

Accurate design of fracturing parameters in shale gas reservoirs does not only increase the productivity and profitability of wells but also minimize the damage fracturing fluids cause to fluid-sensitive formations. However, obtaining a highly accurate and optimized fracturing parameters with rapid as well cost-effective simulation run is difficult to achieve. This study proposed a framework for optimizing shale gas fracturing parameters based on least squares support vector regression (LSSVR) prediction model and non-dominated sorting genetic algorithm-II (NSGA-II). This framework was named the multi-objective optimization prediction model (MOO-PM). In the proposed framework, the flowback ratio of fracturing fluid (FBR) and first month gas production (PROD) were considered as the objective functions while the fracturing parameters including horizontal length, number of fractured sections, fractured length, fracturing fluid injection rate, fracturing fluid viscosity, volume of fracturing fluid and proppant amount were chosen as the optimization variables. Firstly, based on field data the LSSVR prediction model was established. Secondly, the accuracy of the model was verified by the remaining field data. Subsequently, the Latin Hypercube method was used to construct a sample set of fracturing parameters within a predetermined range of variables and the corresponding objective functions were calculated by the LSSVR method. Finally, Pareto front was used to generate flowback ratio and first-month production by the NSGA-II algorithm. To the best of our knowledge, this is the first time that LSSVR prediction model and multi-objective method are applied simultaneously to optimize shale gas fracturing parameters. Furthermore, the novel multi-objective optimization framework was applied to data from the Weiyuan as well as the Changning shale gas reservoirs in Sichuan basin, southwest China. It was concluded that this framework could optimize fracturing parameters of shale gas reservoirs with higher accuracy and efficiency. [Display omitted] • A multi-objective optimization method for fracturing parameters of shale gas is established. • LSSVR prediction model and NSGA-II algorithm are proposed to obtain the optimal scenario. • MOO-PM framework could obtain optimized results with higher efficiency. [ABSTRACT FROM AUTHOR]

During hydraulic fracturing, there is a temperature difference between the injected water and formation rock for shale gas wells. The objective of this study is to investigate how this temperature difference changes with time, and how it affects multiphase-flow modeling during the shut-in and flowback periods. We conducted numerical simulations to investigate the behaviors of fracture temperature in shale gas wells. The results show a significant increase in fracture temperature during the shut-in and flowback periods. Sensitivity analysis suggests that this temperature increase is strongly related to the thermal conductivity of formation rock, matrix permeability, and initial reservoir temperature. Simulation scenarios were further compared to investigate the effect of temperature on flowback data analysis. Without considering the thermal effect, flowback data analysis may yield an earlier fracture cleanup and overestimated fracture volume. In addition, this study suggests that the thermal effect may also have implications for optimizing flowback operations. [ABSTRACT FROM AUTHOR]

Advanced production decline analysis methods have become more economic and efficient options due to technical and time limitations of well-testing operations in shale gas reservoirs. By normalizing rate with material balance pseudo-time, Blasingame type decline analysis can be applied to variable rate/variable pressure drop system, making the methodology more widely applicable. Blasingame decline analysis of variable rate/variable pressure drop case for multi-fractured horizontal well (MFHW) in shale has been less investigated, because of multi-scale storage spaces and multi-flow mechanisms. This article presents a semi-analytical Blasingame decline analysis model incorporating multi-flow mechanisms with shale's modified material balance pseudo-time. Laplace transformation, point source function, numerical discrete method, perturbation method, and Gaussian elimination method were employed to solve the mathematical problem caused by multi-flow mechanisms. Besides, the material balance pseudo-time was modified by considering adsorption and desorption. In this study, a workflow to investigate the effects of properties of hydraulic fracture (fracture half-length, number of fractures and fracture spacing) and multi-flow mechanisms (diffusion and desorption) on production decline behaviors through diagnostic Blasingame type curves is proposed. The results indicate that productivity is greater with a larger scale of fracture treatment, and better desorption of adsorbed gas is also beneficial to production. The decline rates affected by the fracture treatment scale and supplement capacity show two opposite trends, which is caused by interference between the fractures and boundary, respectively. Except that the data in the diffusion early flow period do not match very well, because the use of diffusion coefficient as a constant, the good fitting results on the whole verifies the model's validity and reliability. • A new Blasingame decline model of shale accommodating multi-mechanisms is established. • Effects of fracture and flow mechanism on production behaviors are analyzed. • Simulated result matches well with rate data. • Considering diffusivity as a constant may result in poor fitting result. [ABSTRACT FROM AUTHOR]

Horizontal well drilling and hydraulic fracturing are the most frequently adopted technologies for the commercial development of shale reservoirs. However, expensive production strategies are still implemented which results in high risk in investment. Integrated optimization of horizontal well spacing and hydraulic fracture stage placement helps in striking a balance between gas production and economic benefits. Previous studies relied heavily on numerical simulation models which are computationally expensive. In this study, a novel hybrid surrogate-assisted shale gas horizontal well spacing and hydraulic fracture stage placement multi-objective optimization method based on transfer stacking (SATS-WSF) is proposed to lessen the computational burden of the numerical simulation model run. In the SATS-WSF method, three widely used machine learning models, namely Gaussian process regression (GPR), radial basis function network (RBFN), and support vector regression (SVR) were applied to approximate the numerical simulation model as the source tasks. Then, a hybrid surrogate model transferring the three source tasks to the computationally expensive numerical simulation model was adopted to guide the optimization process of shale gas horizontal well spacing and hydraulic fracture stage placement. In addition, two sampling infill strategies called promising and uncertain were used to accelerate the searching process and to improve the quality of the final optimal solutions. Furthermore, two cases with different wells and fracture types based on the Barnett shale gas reservoir properties were employed to verify the effectiveness and efficiency of the SATS-WSF method. This method provides an intelligent approach for efficient decision-making of shale gas well space and fracture scheme. • A hybrid surrogate-assisted optimization method named SATS-WSF is proposed. • Infill sampling strategies are adopted to improve the performance of the hybrid surrogate model. • The optimization results demonstrated the superior performance of the proposed SATS-WSF method. [ABSTRACT FROM AUTHOR]

Forecasting productivity of shale gas has gained much attention in recent years. However, few researchers considered forecasting absolute open flow potential (AOFP). The conventional method of determining the AOFP of gas wells is through a systematic well testing which is time consuming and has adverse economic effects. Therefore, the objective of this research is to establish an efficient and applicable method for forecasting shale gas well AOFP. In this study, we combined one of the most classic unsupervised machine learning methods namely, principal component analysis (PCA) with least squares support vector regression (LSSVR) which is an efficient supervised machine learning method in forecasting the AOFP for shale gas wells. This method was named hybrid machine learning method based on PCA and LSSVR model (LSSVR-PCA). The proposed method was applied to the Weiyuan, Changning as well as Zhaotong shale gas reservoirs in the Sichuan basin, southwest China. Results showed that the proposed method could forecast AOFP with high accuracy and efficiency if adequate well data are accessible. This hybrid machine learning method also provides an intelligent approach for field engineers to forecast the AOFP and appropriately allocate production of gas wells. • A hybrid machine learning method for AOFP forecasting of shale gas is established. • Principal component analysis and support vector regression are proposed to forecast the AOFP. • Three cases in the Sichuan basin southwest China were studied with this method. [ABSTRACT FROM AUTHOR]