Yuan Di

Wireline formation fluid sampling is extensively utilized to acquire downhole fluid samples. Due to mud filtrate invasion, enough time is required to pump out the formation fluid so that an acceptable level of contaminant is reached. Excessive cleanup time would increase costs and the risk of the testing tool becoming stuck within the drilling mud. The challenge lies in deciding what type of formation-tester probe should be used to ensure minimally contaminated measurements for a specific tool configuration and when the withdrawal sample is sufficiently purged of contaminants. A numerical simulator to simulate the virgin formation fluid sampling was developed, and the accuracy of the simulator was validated based on the spherical flow theory. Through running 2515 simulation cases, the effects of various operational and formation conditions on the breakthrough and pumpout times with three different probes (i.e., the standard probe, the elliptical probe, the elongated probe, and their corresponding 3D radial probes) were compared and analyzed quantitatively. We numerically investigated the key factors influencing the breakthrough and pumpout times and delved into the impact of the formation anisotropy. This study reveals the parameters that encompass the first-order effect on the breakthrough and pumpout times, enabling the determination of the probe-type selection and the early predictions of pumpout time. By leveraging these insights, sampling operations can be optimized to enhance sample quality, reduce operational time, and mitigate the risks associated with tool entrapment.

Chemical flooding is important and effective enhanced oil recovery processes are applied to improve the recovery of heavy oil reservoirs. Emulsification occurs during chemical flooding processes, forming an oil-in-water (O/W) emulsion system. In this work, the heavy oil emulsion system is characterized as a three-phase (continuous oil phase, dispersed oil phase, and continuous water phase) system. Based on a capillary tube model, a new relative permeability model is proposed to describe the flow of the emulsion system in porous media quantitatively, considering the physicochemical properties of emulsions and the properties of porous media. A resistance factor is derived in this model to describe the additional resistance to the emulsion flow caused by the interaction between dispersed oil droplets and the pore system. Three dimensionless numbers related to the emulsion porous flow process were proposed and their different effects on the three-phase relative permeability are investigated. To validate the reliability of the proposed model, a one-dimensional O/W emulsion–oil displacement experiment is simulated. The maximum absolute error between the simulated results and experimental data is no more than 10%, and the new model can be used to describe the flow behavior of heavy oil emulsions in porous media.

Carbonate reservoirs are a highly heterogeneous type of reservoir characterized by the presence of a large amount of vugs and pores. During two-phase displacement, the two-phase flow regime in the vugs might be gravity segregated. The distribution pattern of two-phase fluid in the vugs would accelerate the water flow in downward and horizontal directions, meanwhile decelerating in an upward direction, resulting in a different oil recovery ratio. This gives rise to the question of whether the relative permeability should be modeled as a directional dependent in a vugular porous medium since it is usually treated as an isotropic quantity. In this study, via both experiment and numerical simulation, we demonstrate that the relative permeability of vugular porous medium is dependent on the angle between the flow direction and the horizontal plane and should be considered for oil recovery estimation for carbonate reservoirs. Using the transmissibility-weighted upscaling method and a single-vug model, the relative permeability curves for different flow directions are obtained by numerical simulation. A directional relative permeability model for a vugular porous medium is also proposed.

Improving the robustness and efficiency of flash calculations in phase equilibrium is crucial for reservoir simulation. DL-KF (Deep Learning for K-values and Fugacity Calculation) modeling is proposed to accelerate phase equilibrium calculation using deep learning methods, in which the three-steps neural networks are included: ANN-STAB (Artificial Neural Network for Stability Test) model, ANN-KV (Artificial Neural Network for K-values Calculation) model and ANN-FUG (Artificial Neural Network for Fugacity Calculation) model respectively. The ANN-STAB model is generated to test phase stability. When temperature, pressure and feed composition are given, the phase classification is obtained directly with very low computation cost. In the ANN-KV model, initial K-values are determined by trained networks instead of employing Wilson equation employed in traditional flash calculation. Its initial estimations of K-values significantly reduce the number of iterations and avoid converging to incorrect or unphysical solutions. The ANN-FUG model is built to replace the fugacity coefficient calculation in traditional flash calculation algorithms, and simplifies the nonlinear calculation of internal iterative calculation. These three artificial neural network models are embedded into the traditional algorithms to accelerate flash calculations. The framework considers the complete physical process of the algorithms of flash calculations in phase equilibrium calculations using deep learning methods, and it can also guarantee the conservation of component mass, which is crucial for phase equilibrium calculations and reservoir simulation. The proposed DL-KF modeling is validated and compared with the original equation of state modeling and three other deep learning methods using two typical hydrocarbon fluid cases. A sample of C₃H₈-CO₂-heavy oil systems from Huabei oilfield and a PVT experiment in Tahe oilfield are used to examine the DL-KF modeling. The physical properties of oil sample of Bakken reservoir with CO₂ injection are also investigated. These results reveal that the DL-KF methoding is accurate and efficient for accelerating phase equilibrium calculations of reservoir fluids.

The embedded discrete fracture model (EDFM) has been popular for the modeling of fractured reservoirs due to its flexibility and efficiency while maintaining the complex geometry of fracture networks. Though the EDFM has been validated for single-phase flow simulations, some recent cases show that the EDFM might result in apparent errors in multiphase flow situations. The projection-based embedded discrete fracture model (pEDFM) and the integrally embedded discrete fracture model (IEDFM) are two recently developed methods, which intend to improve the accuracy of the EDFM. In this study, a projection-based integrally embedded discrete fracture model (pIEDFM) is proposed, which combines the advantages of the pEDFM and the IEDFM. Similar to the pEDFM, the pIEDFM uses a kind of additional connections between fracture and nonneighboring matrix cells to obtain more physically authentic velocity fields. As a significant improvement, a semi-analytical cone-shaped pressure distribution that follows the IEDFM is adopted in the pIEDFM to capture the sharp pressure change near the fracture surfaces. Comparisons with benchmark results and explicit-fracture fine grid simulation results show that the pIEDFM provides accurate solutions using a moderate amount of grids. The proposed pIEDFM is also applied to coupled flow and geomechanical simulation for fractured reservoirs. Comparison of our coupled simulation results with that of the EDFM shows that the pIEDFM is applicable for the coupled simulation, and the different methods for matrix-fracture transmissibility between the pIEDFM and the EDFM may lead to deviations in stress fields predicted by geomechanical modeling, which eventually affects the oil production, water cut, and oil saturation profiles.

The effect of geomechanics is crucial in the modeling of fractured reservoirs since fractures can be more stress-sensitive than the rock matrix. In fractured reservoirs, the microscale fractures are often homogenized into the matrix continuum leading to matrix permeability anisotropy. The embedded discrete fracture model (EDFM) is becoming increasingly popular for numerical simulation of fractured reservoirs and has been successfully employed in the simulation of coupled geomechanics and flow systems. However, the anisotropic permeability of matrix cannot be considered in traditional EDFM. An approach of coupled geomechanics and flow modeling is proposed for fractured reservoirs considering anisotropic matrix permeability. In order to calculate the fluid exchange between fractures and rock matrix in an anisotropic formation, an integrally embedded discrete fracture model (IEDFM) is used. The geomechanics model of the proposed approach uses an equivalent continuum approach, which introduces an equivalent material to represent the overall deformation of the fractured rock under normal and shear stresses. The constitutive relations of the equivalent continuum are derived rigorously from stress-strain analysis, where the stress-dependent moduli of natural fractures are included. The coupled geomechanics and flow system is solved using the fixed-stress split iterative coupling strategy with the dynamic hydraulic parameters of matrix and fractures updated separately. Several examples are performed to demonstrate the applicability of the proposed approach for modeling the coupled geomechanics and flow system in fractured reservoirs considering anisotropic permeability. The effect of anisotropy is investigated, which indicates that the dynamic behavior of a fracture is highly orientation-related due to initial stress anisotropy and matrix permeability anisotropy. Simulations also show that the anisotropic matrix permeability affects the compaction in the reservoir domain, which reflects on the performance of production.

Flash calculation is essential for compositional model to determine the composition of the existing hydrocarbon phases and understand the mechanics of fluids flow in reservoirs. Due to its inherent nonlinearity, flash calculation usually turns out to be unstable and high time-consuming in the determination of phase classification and composition concentrations at each grid-block and each time step in compositional simulation. The efficiency and robustness of phase equilibrium calculation is crucial for reservoir simulation. A DL-KF (Deep Learning for K-values & Fugacity Calculation) modeling is proposed to accelerate phase equilibrium calculation using deep learning methods, in which the three-steps neural networks are concluded: ANN-STAB (Artificial Neural Network for Stability Test) model, ANN-KV (Artificial Neural Network for K-values Calculation) model and ANN-FUG (Artificial Neural Network for Fugacity Calculation) model respectively. The ANN-STAB model is generated to test phase stability. When temperature, pressure and feed composition are given, the phase classification is obtained directly with very low computation cost. In the ANN-KV model, initial K-values are determined by trained networks instead of employing Wilson equation employed in traditional flash calculation. Its initial estimations of K-values significantly reduce the number of iterations and avoid converging to incorrect or unphysical solutions. The ANN-FUG model is built to replace the fugacity coefficient calculation in traditional flash calculation algorithms, and simplifies the nonlinear calculation of internal iterative calculation. The proposed DL-KF modeling is validated and compared with the original EOS modeling and three other deep learning methods using two typical hydrocarbon fluid cases. A sample of C₃H₈CO₂-heavy oil systems from Huabei oilfield and a PVT experiment in Tahe oilfield are used to examine the DL-KF modeling. The physical properties of oil sample of Bakken reservoir with CO₂ injection are also investigated. These results reveal that the DL-KF method is accurate and efficient for phase equilibrium calculations of reservoir fluids.

The pore sizes in tight reservoirs are nanopores, where the phase behavior deviates significantly from that of bulk fluids in conventional reservoirs. The phase behavior for fluids in tight reservoirs is essential for a better understanding of the mechanics of fluid flow. A novel methodology is proposed to investigate the phase behavior of carbon dioxide (CO₂)/hydrocarbons systems considering nanopore confinement. The phase equilibrium calculation is modified by coupling the Peng-Robinson equation of state (PR-EOS) with capillary pressure, fluid-wall interaction, and molecule adsorption. The proposed model has been validated with CMG-Winprop and experimental results with bulk and confined fluids. Subsequently, one case study for the Bakken tight oil reservoir was performed, and the results show that the reduction in the nanopore size causes noticeable difference in the phase envelope and the bubble point pressure is depressed due to nanopore confinement, which is conductive to enhance oil recovery with a higher possibility of achieving miscibility in miscible gas injection. As the pore size decreases, the interfacial tension (IFT) decreases whereas the capillary pressure increases obviously. Finally, the recovery mechanisms for CO₂ injection are investigated in terms of minimum miscibility pressure (MMP), solution gas-oil ratio, oil volume expansion, viscosity reduction, extraction of lighter hydrocarbons, and molecular diffusion. Results indicate that nanopore confinement effect contributes to decrease MMP, which suppresses to 650psi (65.9 smaller) as the pore size decreases to 2nm, resulting in the suppression of the resistance of fluid transport. With the nanopore confinement effect, the CO₂ solution gas-oil ratio and the oil formation volume factor of the oil increase with the decrease of pore size. In turn, the oil viscosity reduces as the pore size decreases. It indicates that considering the nanopore confinement effect, the amount of gas dissolved into crude oil increases, which will lead to the increase of the oil volume expansion and the decrease of the viscosity of crude oil. Besides, considering nanopore confinement effect seems to have a slightly reduced effect on extraction of lighter hydrocarbons. On the contrary, it causes an increase in the CO₂ diffusion coefficient for liquid phase. Generally, the nanopore confinement appears to have a positive effect on the recovery mechanisms for CO₂ injection in tight oil reservoirs. The developed novel model could provide a better understanding of confinement effect on the phase behavior of nanoscale porous media in tight reservoirs. The findings of this study can also help for better understanding of a flow mechanism of tight oil reservoirs especially in the case of CO₂ injection for enhancing oil recovery.