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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]

The purpose of this study is to determine the optimal conversion timing of follow-up thermal recovery approaches of post-CHOP for foamy extraheavy oil reservoirs. The microscopic visualization experiment and the one-dimensional sand pack experiment are conducted to investigate the influence of temperature on the foamy oil cold production process. According to the experimental results, it can be concluded that the temperature has great influence on foamy oil flow stage during the CHOP process. Therefore, it is necessary to study the optimal conversion timing of follow-up thermal recovery approaches after CHOP for the foamy extraheavy oil reservoir. Based on the analysis of the experimental results, the compositional foamy oil model is established by taking the effect of temperature into consideration. In the numerical model, the conversion timings of different thermal recovery approaches are investigated. The optimal conversion timings for cyclic steam stimulation (CSS) and steam flooding (SF) processes are the moments when the pressure drops to the pseudo-bubble point pressure. For the CSS method, excessive pressure cannot give full play to the production potential of CHOP stage; when the pressure is too low, it lacks enough energy to drive the heated crude oil to the wellbore. For the SF method, high pressure cannot fully release the latent heat of steam, and the content of dissolved gas (which will hinder the heat transfer) in oil phase is higher under high pressure, while the very low pressure leads to relatively high viscosity of crude oil; thus, the performance of the SF process becomes worse. For the SAGD process, the adverse effects of released solution gas in foamy extraheavy oil reservoir outweigh the positive effects. As a result, the CHOP period should be extended as long as possible to obtain a high recovery. In other words, the recovery process should be switched to the SAGD process at a relatively low formation pressure. The findings of this study could help for better understanding of the CHOP and post-CHOP thermal techniques for foamy extraheavy oil reservoirs, and it can provide guidance for reservoir engineers to make better use of the thermal recovery techniques to further improve the recovery performance of foamy extraheavy oil reservoirs. [ABSTRACT FROM AUTHOR]

The phase behavior of fluid is essential for predicting ultimate oil recovery and determining optimal production parameters. The pore size in shale porous media is nanopore, which causes different phase behaviors of fluid in unconventional reservoirs. Nanopores in shale media can be regard as semipermeable membrane to filter heavy components (sieving effect) in shale oil, which leads to the different distributions of fluid components and different phase behaviors. In addition, the phase behavior of fluid in nanopores can be significantly altered by large capillary pressure. In this paper, the phase behavior of fluid in shale reservoirs is investigated by a new two-phase flash algorithm considering sieving effect and capillary pressure. Firstly, membrane efficiency and capillary pressure are introduced to establish a thermodynamic equilibrium model that is solved by Rachford–Rice flash calculation and Newton–Raphson method. The capillary pressures in different pore sizes are calculated by the Young–Laplace equation. Then, the influences of sieving effect and capillary pressure on phase behavior are analyzed. The results indicate that capillary pressure can suppress the bubble point pressure of fluid in nanopores. The distributions of fluid components are different in various parts of shale media. In the unfiltered part, density and viscosity of fluid are higher. Finally, it is found that the membrane efficiency can be improved by CO2 injection. The minimum miscibility pressure for shale oil–CO2 system is also studied. The developed model provides a better understanding of the phase behavior of fluid in shale oil reservoirs. [ABSTRACT FROM AUTHOR]

CO2 flooding recovery strongly depends on the minimum miscibility pressure (MMP). Conventional tests to determine gas–oil MMP such as rising bubble apparatus and slim tube displacement are either costly or time consuming. In order to propose a quick and accurate model to determine MMP, a back-propagation neural network is presented for MMP prediction during pure and impure CO2 injections. Five new variables were screened as input parameters to the network. Next, the network was optimized using five evolutionary algorithms, and this work highlights that three of these evolutionary algorithms (e.g. Mind Evolutionary, Artificial Bee Colony, and Dragonfly) are firstly used to predict MMP. Then, data from the literature were input to the optimized network to train it. Statistical evaluation and graphical analyses were used to evaluate the performance of the proposed models and for comparison with published MMP correlates to obtain the optimal model for predicting MMP. The back-propagation model optimized using the dragonfly algorithm exhibited the highest accuracy among all those considered and MMP correlates; its coefficient of determination, average absolute percent relative error, root mean square error, and standard deviation were 0.965, 5.79%, 206.1, and 0.08, respectively. In addition, reservoir temperature was determined as the strongest MMP predictor (Pearson correlation = 0.63) based on sensitivity analysis. [ABSTRACT FROM AUTHOR]

In tilted fault block reservoirs with edge water, the remaining oil is usually formed and located at the structurally highest. The gas and water bidirectional displacement process is an effective method to recover this type of remaining oil in these reservoirs. However, there is no optimizing method to design the injection-production schedule of this process for the maximum oil recovery. In this study, the imaging and superposition principle, Pollock method, equivalence principle, and Buckley-Leverett method were combined to develop a novel semi-analytical method for optimizing the injection-production schedule. An example study was carried out for verifying the proposed semi-analytical method and validated with the numerical simulation method. Furthermore, sensitive factors of this displacement process were quantitatively analyzed by the proposed method. The results showed that the optimized synergy of the gas and water can maximize the oil recovery for the bidirectional displacement process. Compared with no injection, inadequate injection, and excess injection of gas, the breakthrough recovery can be increased by 3.7%, 0.8%, and 4.88%, respectively, by controlling the gas-water synergy. Meanwhile, under the gas-water synergy displacement condition, the oil recovery is increasing with the tilt angle of the reservoir increasing and the distance of well from edge water decreasing. This enhanced oil recovery process is more sensitive to low-middle tilt angles and more effective for small distances. The results obtained from this study can be used to improve the exploitation strategies of tilted fault block reservoir with edge water. • A novel semi-analytical approach is developed to optimize the gas and water bidirectional displacement process. • The dominance for synergy of gas and water bidirectional displacement is verified. • Sensitive factors of gas and water bidirectional displacement are quantitatively analyzed. [ABSTRACT FROM AUTHOR]

The adsorbed methane in shale determines the geological reserves of shale gas and production of the well. Therefore, it is of great importance to clarify the microscopic mechanism of methane adsorption in shale for evaluating the geological condition and formulating exploitation plan. In this study, the experimental data and the simulation result were combined to research the microscopic mechanism of methane adsorption in shale. The experimental data for 99 groups of low-temperature nitrogen adsorption and methane isothermal adsorption were collected, to calculate the number of adsorption layers of methane in shale at the micro-level. The surface, microfracture and micropore structures of the organic matter in shale were constructed by graphite structure with the hydroxyl group. Thereafter, they were used to simulate the interaction potential of the organic matter on methane molecules and the adsorption layer's number by using the theory of intermolecular interaction potential field. Furthermore, the effects of different fracture widths and pore diameters on methane adsorption were analyzed and verified. The results show that the adsorption capacity of methane in shale is related to shale's structure, and the order of the adsorption capacity is surface < microfracture < micropore. Meanwhile, the multilayer adsorption can occur in microfractures with a width of 1.44–1.59 nm and micropores with a diameter of 1.49–1.83 nm, whereas the surface structure just can be the monolayer adsorption. These quantitatively results establish a foundation for the development of adsorption theory for methane in the shale. • The number of adsorption layers is calculated by using experimental data. • The adsorption capacity is ranked as surface < microfracture < micropore. • The number of adsorption layers is quantitatively simulated and analyzed. [ABSTRACT FROM AUTHOR]

Solvent-assisted steam flooding has gained popularity in heavy oil reservoir during recent years. Calculating asphaltene deposition is a key issue in developing heavy oil reservoirs, because asphaltene deposition can significantly reduce permeability by blocking pore throats. In this work, a comprehensive model of solvent assisted steam flooding for thin heavy oil reservoirs considering the influence of asphaltene deposition was proposed. The model is solved by IMPES (Implicit Pressure and Explicit Saturations) method. The amount of deposition is calculated through three mechanisms of entrainment, plugging deposition and surface deposition. Then, the effects of asphaltene deposition on well performance and permeability reduction were analyzed. Furthermore, the solubility parameter is used to investigate the effect of different solvents on alleviating the damage of asphaltene deposition for reservoir. The damage of asphaltene deposition for reservoir can decrease with the increase of solvent concentration. Finally, based on the analysis of oil production, it is found that butane assisted steam flooding is the most effective thermal method to improve the oil recovery. And injection parameters of butane assisted steam are also determined for an oil field by analysis of oil production. Based on the results in this paper, it may be extensive applicability and practical significance for determining the best solvent and reasonable injection parameters during the development of heavy oil reservoirs considering asphaltene deposition to enhance oil recovery. • A mathematical model has been developed to evaluate the performance of solvent assisted steam flooding considering asphaltene deposition. • The effects of asphaltene deposition on well performance and permeability reduction were analyzed. • The solubility parameter is used to investigate the effect of different solvents on alleviating the damage of asphaltene deposition. • The production performance by adding different solvents is compared. [ABSTRACT FROM AUTHOR]

The purpose of this study is to develop a data-driven proxy model for forecasting of cumulative oil (Cum-oil) production during the steam-assisted gravity drainage process. During the model building process, an artificial neural network (ANN) is used to offer a complementary and computationally efficient tool for the physics-driven model, and the von Bertalanffy performance indicator is used to bridge the physics-driven model with the ANN. After that, the accuracy of the model is validated by blind-testing cases. Average absolute percentage error of related parameters of the performance indicator in the testing data set is 0.77%, and the error of Cum-oil production after 20 years is 0.52%. The results illustrate that the integration of performance indicator and ANN makes it possible to solve time series problems in an efficient way. Besides, the data-driven proxy model could be applied to fast parametric studies, quick uncertainty analysis with the Monte Carlo method, and average daily oil production prediction. The findings of this study could help for better understanding of combination of physics-driven model and data-driven model and illustrate the potential for application of the data-driven proxy model to help reservoir engineers, making better use of this significant thermal recovery technology for oil sands or heavy oil reservoirs., Competing Interests: The authors declare no competing financial interest., (© 2021 The Authors. Published by American Chemical Society.)