Accelerated Compositional Simulation of Tight Oil and Shale Gas Reservoirs Using Proxy Flash Calculation

Wang, Shihao; Sobecki, Nicolas; Ding, Didier Yu; Wu, Yu‐Shu; Zhu, Lingchen

doi:10.2118/193878-ms

Cited by 4 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the deepening of research, some scholars − have proposed four-phase and multiphase flash calculation methods. Under formation conditions, Neshat et al, Achour et al, Wang et al, and Sandoval et al carried outgas–liquid flash calculations through considering the influence of the capillary force. The modified method greatly improves the accuracy of the calculation considering the capillarity effect .…”

Section: Introductionmentioning

confidence: 99%

Study on the Phase Behavior Simulation Method of High-Salinity Reservoirs

Ren,

Li,

Yuan

et al. 2024

ACS Omega

View full text Add to dashboard Cite

The presence of salinity affects the accuracy of existing correlations used in the equation of state. Moreover, the variation of salinity is often ignored in the systematic analysis of the phase diagram, resulting in a large error in the final calculation result. It is obvious that the conventional phase equilibrium calculation is not applicable in a high-salinity reservoir. By introducing the hydrocarbon−brine binary interaction coefficient and α-function, combined with the definition of salinity, and considering the variation of salinity under different pressure and temperature conditions, a more perfect phase equilibrium calculation model was established. The complete phase diagram was drawn, and the calculation results of salinity distribution are obtained. The effect of the mole percentage of water and salt content on the phase behavior was simulated. Finally, the phase distribution simulation is carried out based on the measured data. The phase state and salinity variation law of a high-salinity reservoir are obtained. According to the fluid composition of different periods, the real phase state of the high-salinity reservoir can be monitored in real time. It can provide a theoretical basis for the gas reservoir development and the dynamic evaluation of gas storage injection and production with a hydrocarbon−brine two-phase system.

show abstract

Section: Introductionmentioning

confidence: 99%

Study on the Phase Behavior Simulation Method of High-Salinity Reservoirs

Ren,

Li,

Yuan

et al. 2024

ACS Omega

View full text Add to dashboard Cite

show abstract

“…Therefore, an accurate modeling of the phase distribution and changing rules in the subsurface unconventional reservoirs with capillary effect is of significant importance with respect to both the theoretical studies and engineering applications. Moreover, thermodynamic studies on phase behaviors can further suggest the formulation of governing equations for modeling multicomponent multiphase flow and transport to ensure the thermodynamic consistency and mass conservation properties [9].…”

Section: Introductionmentioning

confidence: 99%

Thermodynamics-Informed Neural Network (TINN) for Phase Equilibrium Calculations Considering Capillary Pressure

Tao

Sun

2021

Energies

View full text Add to dashboard Cite

The thermodynamic properties of fluid mixtures play a crucial role in designing physically meaningful models and robust algorithms for simulating multi-component multi-phase flow in subsurface, which is needed for many subsurface applications. In this context, the equation-of-state-based flash calculation used to predict the equilibrium properties of each phase for a given fluid mixture going through phase splitting is a crucial component, and often a bottleneck, of multi-phase flow simulations. In this paper, a capillarity-wise Thermodynamics-Informed Neural Network is developed for the first time to propose a fast, accurate and robust approach calculating phase equilibrium properties for unconventional reservoirs. The trained model performs well in both phase stability tests and phase splitting calculations in a large range of reservoir conditions, which enables further multi-component multi-phase flow simulations with a strong thermodynamic basis.

show abstract

Fast History Matching and Robust Optimization Using a Novel Physics-Based Data-Driven Flow Network Model: An Application to a Steamflood Sector Model

et al. 2022

View full text Add to dashboard Cite

Summary Full-fidelity models can be computationally expensive during history matching (HM) and robust optimization as these problems typically require hundreds of simulations. Previously, we have implemented a physics-based data-driven flow network model, general purpose simulator-powered network model (GPSNet), that serves as a surrogate without the need to build the 3D full-fidelity model. In this paper, GPSNet is enhanced to GPSNet-2D to better capture thermal processes, especially gravity drainage. This enhancement successfully enables a rapid HM and robust optimization for a steamflood sector model. In GPSNet-2D, the reservoir is discretized into a series of 2D connections (x-z planes) between well completions. These connections capture the main areal/vertical flow paths, and the grid properties of each connection are interpreted with historical data. Steam segregation and heat loss are included to better represent the subsurface physics. When simulating GPSNet-2D using a commercial simulator, an equivalent 3D Cartesian model is designed where vertical slices correspond to the interwell connection planes. Thereafter, an iterative HM is conducted using the ensemble smoother with multiple data assimilation. The best-matched model is then used for steam injector control optimization. The GPSNet-2D model is first validated through a synthetic steamflood case. HM result shows that the GPSNet-2D model not only aligns closely to field-scale volumetric data but also yields good well-level matches, including bottomhole pressure (BHP)/bottomhole temperature (BHT) and phase rates. Then, GPSNet-2D is successfully applied to steamflood HM and robust optimization for a 36-well sector of a heavy oil field in the San Joaquin Valley in North America. Again, the calibrated GPSNet-2D model demonstrates its capability and reliability by generating accurate field-scale match results and reasonable matches for most of the wells. For robust well control optimization, we select nine realizations from the posterior ensemble and maximize NPV under well constraints simultaneously. The optimal case improves the average NPV by 27% over the reference case. The integration with a commercial simulator in GPSNet-2D provides flexibility to account for complex physics in the thermal recovery processes, such as steam segregation near injectors, fast steam breakthroughs at producers, and heat loss to the overburden/underburden formations. Unlike traditional simulation that relies on a detailed characterization of geological models, the GPSNet-2D model only requires well volumetric production/injection data and its approximate trajectory and can be generated and updated rapidly. In addition, GPSNet-2D also runs much faster (minutes) than a full-fidelity thermal model due to having much fewer gridblocks in the model. To our knowledge, this is the first time a physics-based data-driven network model integrated with a commercial simulator is demonstrated via a field steamflood case. Unlike approaches developed with analytic/empirical solutions or research simulators, the use of a commercial simulator makes it possible to extend flow network modeling to simulate enhanced oil recovery processes more realistically. It serves as an ideal surrogate model for both fast and reliable decision-making in reservoir management.

show abstract

Accelerated Compositional Simulation of Tight Oil and Shale Gas Reservoirs Using Proxy Flash Calculation

Cited by 4 publications

References 25 publications

Study on the Phase Behavior Simulation Method of High-Salinity Reservoirs

Study on the Phase Behavior Simulation Method of High-Salinity Reservoirs

Thermodynamics-Informed Neural Network (TINN) for Phase Equilibrium Calculations Considering Capillary Pressure

Fast History Matching and Robust Optimization Using a Novel Physics-Based Data-Driven Flow Network Model: An Application to a Steamflood Sector Model

Contact Info

Product

Resources

About