Due to the presence of asphaltene, the flow assurance of high viscosity crude oil becomes more challenging and costly to produce in wellbores and pipelines. One of the most effective ways to reduce viscosity is to blend heavy oil with light oil. However, the viscosity measurement of diluted heavy crude is either time-consuming or inaccurate. This work aims to develop a more accurate viscosity model of diluted heavy crude based on machine learning techniques. A multilayer neural network is used to predict the viscosity of heavy oil diluted with lighter oil. The input data used in the training include temperature, light oil viscosity, heavy oil viscosity, and dilution ratio. In this modeling process, 156 datasets were retrieved from the available iterature of various heavy-oil fields in China. Part of the data (80%) is used to train the developed models using Adam optimizer algorithms, while the other part of the data (20%) is used to predict the viscosity of heavy oil diluted with lighter. The performance and accuracy of the machine learning models were tested and compared with the existing viscosity models. It was found that the new model can predict the viscosity of diluted heavy oil with higher accuracy, and it performs better than other models. The absolute average relative error is 10.44%, the standard deviation of the relative error is 8.45%, and the coefficient of determination is R² = 0.95. The viscosity predicted by the neural network outperformed existing correlations by the statistical analysis used for the datasets available in the literature. Therefore, the method proposed in this paper can better estimate the viscosity of diluted heavy crude oil and has important promotion value.

Permeability is one of the key factors involved in the optimization of oil and gas production in fractured porous media. Understanding the loss in permeability influenced by the fracture system due to the increasing effective stress aids to improve recovery in tight reservoirs. Specifically, the impacts on permeability loss caused by different fracture parameters are not yet clearly understood. The principal aim of this paper is to develop a reasonable and meaningful quantitative model that manifests the controls on the permeability of fracture systems with different extents of fracture penetration. The stress-dependent permeability of a fracture system was studied through physical tests and numerical simulation with the finite element method (FEM). In addition, to extend capability beyond the existing model, a theoretical stress-dependent permeability model is proposed with fracture penetration extent as an influencing factor. The results presented include (1) a friendly agreement between the predicted permeability reduction under different stress conditions and the practical experimental data; (2) rock permeability of cores with fractures first reduces dramatically due to the closure of the fractures, then the permeability decreases gradually with the increase in effective stress; and (3) fracture penetration extent is one of the main factors in permeability stress sensitivity. The sensitivity is more influenced by fracture systems with a larger fracture penetration extent, whereas matrix compaction is the leading influencing factor in permeability stress sensitivity for fracture systems with smaller fracture penetration extents.

Asphaltene deposition in oil wells greatly causes production and associated monetary losses. In this work, the virtual mass force and pressure gradient force are considered in the momentum equation of asphaltene aggregates, since the rate of asphaltene particle density (1100 kg/m³) to crude oil density (886 kg/m³) is close to 1. Accordingly, the flow of oil-asphaltene particles was simulated within the Eulerian–Lagrangian framework in a production tubing. Subsequently, the shear stress turbulence model and discrete random walk model are applied to predict the deposition rate of asphaltene aggregates on production tubing. The simulation results show that the deposition rate considering these two forces agrees well with the experimental data. Moreover, the effect of the saffman lift force, virtual mass force, pressure gradient force, oil viscosity, particle density, wall roughness as well as crude oil density are investigated on the deposition rate of asphaltene aggregates on production tubing. The findings of this work can favor a better understand of the law of asphaltene deposition in oil wells.

After single-gas (SG) injection operations in tight oil reservoirs, a significant amount of oil is still unrecovered. To increase productivity, several sequencing gas injection techniques have been utilized. Given the scarcity of research on multiple-gas alternating injection schemes, this study propose an optimized triple-alternating-gas (TAG) injection for improved oil recovery. The performance of the TAG process was demonstrated through numerical simulations and comparative analysis. First, a reservoir compositional model is developed to establish the properties and composition of the tight oil reservoir; then, a suitable combination for the SG, double alternating gas (DAG), and TAG was selected via a comparative simulation process. Second, the TAG process was optimized and the best case parameters were derived. Finally, based on the oil recovery factors and sweep efficiencies, a comparative simulation for SG, DAG, and TAG was performed and the mechanisms explained. The following findings were made: (1) The DAG and TAG provided a higher recovery factor than the SG injection and based on recovery factor and economic advantages, CO₂ + CH₄ + H₂S was the best choice for the TAG process. (2) The results of the sensitivity analysis showed that the critical optimization factors for a TAG injection scheme are the injection and the production pressures. (3) After optimization, the recovery factor and sweep efficiency of the TAG injection scheme were the best. This study promotes the understanding of multiple-gas injection enhanced oil recovery (EOR) and serves as a guide to field design of gas EOR techniques.

CO2 flooding disrupts asphaltene balance, causing deposition and reservoir damage. Most models lack core verification, resulting in inaccurate predictions of asphaltene deposition and reservoir performance. The Discrete Phase Model (DPM) of Volume model for characterizing asphaltene deposition during CO2 flooding was established by coupling Volume of Fluid with DPM, which simulates the complex interaction between CO2-flooded crude oil and asphaltene flow deposition dynamics. Key factors such as CO2 mole fraction, displacement velocity, crude oil viscosity, asphaltene density, and particle size on asphaltene deposition, as well as the impact on reservoir porosity and permeability degradation, were comprehensively examined. The findings show that increasing CO2 mole fraction from 0.2 to 1 enhances asphaltene deposition rate by 53.6% and damages porosity and permeability by factors of 2.4 and 5.78, respectively. Raising CO2 injection velocity rises from 0.006 to 0.016 m/s reduces the deposition rate by 17.97%, the porosity damage factor drops from 1.981 to 1.075 and the permeability damage factor decreases from 3.926 to 1.556. Higher crude oil viscosity significantly reduces both deposition rate and related damage, while increasing particle density and diameter correlate with greater deposition and damage rates. Asphaltene particles accumulate within the throats of porous media as dispersed particulates, while they densely pack within the pores. Gray correlation analysis reveals that CO2 mole fraction exhibits the highest relationship with both the rate of asphaltene particle deposition and the extent of pore permeability impairment. This study enhances understanding of asphaltene deposition dynamics and reservoir damage mechanisms in CO2-flooded.

Shale gas is an important unconventional natural gas resource. Studying the microstructure of shale and the gas transport law is of great significance for the development of shale gas. This paper uses the field emission scanning electron microscope to observe shale samples of the BC shale gas reservoirs in southern China. It is found that there are three types of storage spaces on the micro–nano-scale of shale samples. The storage space can be distributed either in pure organic matter or pure inorganic matter or in both organic matter and inorganic matter. They are called organic storage space, inorganic storage space, and mixed storage space of organic matter and inorganic matter, respectively. According to these types of storage spaces, an ideal conceptual model that reflects various types of storage spaces has been researched and established on the micro–nano-scale. At the same time, the transport mechanisms of slip, diffusion, adsorption, and coupling have been considered, and shale mixed storage space has also been considered in particular. On this basis, a comprehensive equation that can simulate the transport of shale gas in various types of storage spaces is derived. The equation also introduces the proportional parameters of the organic part, fractal characteristics, and water film of the inorganic part in the mixed storage space. Researchers can adjust this parameter to simulate shale gas transportation in different types of storage spaces and then use the finite element method to solve it numerically. This paper analyzes the influence of shale reservoir space types on shale gas transport. The larger the proportion of organic components in the mixed pores, the better the gas transport. The rough fractal dimension of the pores also affects the gas transport. However, when the pore diameter is less than 300 nm, the rough fractal dimension of the pores has a negligible influence on gas transport. For the water film on the inorganic wall surface of mixed pores, the gas transport of the macropore is more sensitive to the change in water film thickness.