Browsing by Subject "Phase behavior modeling"
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Item Equation of State Phase Behavior Modeling for Compositional Simulation(1988-12) Perschke, Douglas Roger; Pope, Gary A.; Sepehrnoori, KamyA phase behavior algorithm has been developed for use in compositional simulation which is capable of determining the number and composition of the phases that should be present at equilibrium. The algorithm can determine equilibrium behavior for mixtures forming up to three phases, with the intent to model the low temperature phase behavior of carbon dioxide/hydrocarbon mixtures. An equation of state was used to model the nonaqueous fluid behavior. In the phase behavior algorithm, a sequential procedure was used which combined a phase stability analysis calculation with a phase composition calculation to determine the number and composition of the equilibrium phases. Different methods were implemented and compared for each calculation for twoand three-phase mixtures. The phase behavior algorithm was implemented into a compositional simulator. A method to consistently label phases during simulation was developed and implemented. This method for phase identification was shown to be successful in the single-phase region as well as in the two- and three-phase regions. Phases were also identified consistently during simulation examples where phase mass density inversions occurred. Several simulation examples were presented showing results for mixtures exhibiting complex phase behavior, including mixtures traversing the three-phase region. Results were presented showing an example where the oil/gas/second liquid region had a significant effect and was poorly approximated by two-phase equilibrium calculations. Other simulation results were presented, however, in which the three-phase region had no effect at all. The phase behavior algorithm was evaluated under simulation conditions and problems encountered during phase behavior calculations were presented and discussed.Item Improvements in phase behavior modeling for compositional simulation(2015-05) Rezaveisi, Mohsen; Sepehrnoori, Kamy, 1951-; Johns, Russell T.; Pope, Gary A; Delshad, Mojdeh; Mohanty, Kishore KAccurate and reliable phase equilibrium calculations are among the most important issues in compositional reservoir simulation of enhanced oil recovery (EOR) processes especially miscible gas floods. The important challenges in equation of state (EOS)-based compositional simulators are the time-consuming nature of the phase equilibrium calculations, e.g. 30%-50% of the total computational time in the UTCOMP simulator (Chang, 1990), and accuracy as well as robustness of these calculations. Thus, increasing the computational speed and robustness of the phase equilibrium calculations is of utmost importance in IMPEC-type and fully implicit reservoir simulators. Furthermore, most current compositional reservoir simulators ignore the effect of capillary pressure in porous media on the fluid’s phase behavior. This assumption may lead to significant errors in performance prediction of tight oil and shale gas reservoirs where the small pore sizes result in very large capillary pressure values. The “tie-simplex-based (TSB) phase behavior modeling” techniques attempt to speed up phase behavior calculations by skipping stability analysis and preconditioning phase-split calculations. We implemented the compositional space adaptive tabulation (CSAT), a TSB phase behavior modeling method, in UTCOMP and compared the computational performance of CSAT when used for skipping stability analysis and generating initial estimates for flash calculations, against the standard phase behavior modeling methods in UTCOMP. The results show that the CSAT method as well as a simple heuristic technique, where stability analysis is skipped for single-phase gridblocks surrounded by single phase neighbors, can improve the total computational time by up to 30% compared to the original UTCOMP. In order to avoid the negative-flash calculations required for adaptive tie-line tabulation during the simulation, a prior set of tie-line tables can be used. We demonstrate that the tie lines from the multiple-mixing-cell (MMC) method are very close to the actual compositional simulation tie lines. Thus, the MMC tie lines were used as prior tieline tables in three tie-line-based K-value simulation methods in order to improve speed and robustness of compositional simulation. Several simulation case studies were performed to compare the computational efficiency of the three MMC-based methods, an extended CSAT method (adaptive K-value simulation) and a method based on pure heuristic techniques against the original UTCOMP formulation. The results show that the MMC-based methods and the extended CSAT method can improve the total computational time by up to 50% with acceptable accuracy for the cases studied. The MMC-based methods, the CSAT method and the heuristic methods were implemented in the natural variable formulation in the fully-implicit General Purpose Adaptive Simulator (GPAS) for speeding up the phase equilibrium calculations. The computational efficiency results for several cases that we studied show that the CSAT method and the MMC-based method improve the computational time of the phase equilibrium calculations by up to 78% in the multi-contact-miscible gas injection cases studied. Finally, we present a Gibbs free energy analysis of capillary equilibrium and demonstrate that there is a limiting maximum capillary pressure (P[subscript cmax]) where gas/oil capillary equilibrium is possible and formulate the P [subscript cmax] limit using the spinodal condition of the phase of smaller pressure in capillary equilibrium. The effect of capillary pressure on phase behavior was implemented in the UTCOMP simulator and several simulation case studies in shale gas and tight oil reservoirs were performed. The simulation results illustrate the effect of capillary pressure on production behavior in shale gas and tight oil reservoirs.Item Modeling Gas Condensate Reservoirs and Development of a New Hybrid Well Model(2003-05) Sharma, Ravi; Pope, Gary A.; Sepehrnoori, KamyGas condensate reservoirs have been receiving a lot of attention in the past few years. Main reasons for the growing interest is the increasing demand for natural gas as an energy source and the discovery of many new gas fields every year. Natural gas is also cheaper and has environmental advantages over other sources of energy such as coal and oil. It is the fastest-growing primary energy source in the world. Most of the gas reserves have severe temperature and pressure conditions which increases the cost and risk involved in the development of the fields. Gas condensate reservoirs exhibit even more complex behavior because of the retrograde condensation. During the depletion of gas condensate reservoirs, retrograde condensation occurs when the bottom hole pressure falls below the dew point pressure. The productivity of many gas condensate wells is reduced by the formation of a condensate bank in the near well region. Formation of the condensate bank reduces the relative permeability to gas which leads to the loss of productivity. One important step in the study of gas condensate fields is the phase behavior modeling of the gas condensate mixture. This is essential for accurately predicting the amount of liquid dropout and phase equilibrium during a compositional simulation. To study the gas condensate fields, it is essential to capture the near well effects such as condensate bank formation, changes in gas relative permeability at high trapping number and high velocity phenomena such as non-Darcy flow. Fine grid simulation is necessary to have adequate resolution in these regions of intense activity. However, in the conventional fine grid method, a large number of superfluous cells are generated. This is because the grid lines cannot terminate inside the well grid block and are extended to the model boundaries. Increase in the number of cells in the model also increases the memory requirement and the computational time. This in turn limits the size of the model that can be simulated. One alternative to the conventional fine grid simulation is local grid refinement. In local grid refinement, only the area of interest is refined and rest of the field is modeled with coarse grids. Local grid refinement has been a subject of study for many years and there have been great advances in this field. But local grid refinement has its limitations, as it is much more complex and difficult to implement.