Phase-equilibrium calculations become computationally intensive in compositional simulation as the number of components and phases increases. Reduced methods were developed to address this problem, where the binary-interaction-parameter (BIP) matrix is approximated either by spectral decomposition (SD), as performed by Hendriks and van Bergen (1992), or with the twoparameter BIP formula of Li and Johns (2006). Several authors have recently stated that the SD method-and by reference all reduced methods-is not as fast as previously reported in the literature. In this paper we present the first study that compares all eight reduced and conventional methods published to date by use of optimized code and compilers.The results show that the SD method and its variants are not as fast as other reduced methods, and can be slower than the conventional approach when fewer than 10 components are used. These conclusions confirm the findings of recently published papers. The reason for the slow speed is the requirement that the code must allow for a variable number of eigenvalues. We show that the reduced method of Li and Johns (2006) and its variants, however, are faster because the number of reduced parameters is fixed to six, which is independent of the number of components. Speed up in flash calculations for their formula is achieved for all fluids studied when more than six components are used. For example, for 10-component fluids, a speed up of 2-3 in the computational time for Newton-Raphson (NR) iterations is obtained compared with the conventional method modeled after minimization of Gibbs energy. The reduced method modeled after the linearized approach of Nichita and Graciaa (2011), which uses the twoparameter BIP formula of Li and Johns (2006), is also demonstrated to have a significantly larger radius of convergence than other reduced and conventional methods for five fluids studied. IntroductionGas injection achieves enhanced oil recovery (EOR) primarily by developing miscibility in situ. Compositional simulation of gasfloods is an important tool for recovery estimation, but a significant drawback is that compositional simulation is costly when the number of components and phases is large. One approach is to lump components into several pseudocomponents and to limit the number of phases that can form. Reducing the number of components, however, decreases the accuracy of the phase-behavior calculation in compositional simulation, and also the detailed type of compositional information required for better surface-facility designs. Fewer components also means that heavier components are not adequately represented and evaporation fronts will not be captured properly in simulation, necessitating the use of an unphysical parameter called the miscible residual oil saturation to force oil to be left behind. Thus, it is highly advantageous to use more components, not fewer, if methods for faster flash calculations are available.

No abstract

No abstract

No abstract

The exploitation of gas from tight gas reservoirs has been increasing due to the advances in unconventional gas technologies and depletion of conventional gas resources. Drilling horizontal wells and placing transverse hydraulic fractures are needed to produce economically viable amounts of gas from tight sands. In this research, optimization of the design of hydraulically fractured horizontal well placed in naturally fractured tight gas sand reservoir systems was studied. A commercial reservoir simulator is coupled with artificial neural network (ANN) to create an expert system that can be used to design an efficient stimulation strategy. Reservoir simulation was used to generate production profiles of the reservoir system and, then, used to train the artificial neural network system. The ANN tools developed in this project consists of two parts: forward and inverse processes. In the forward process, an input data set that includes well and completion design parameters and reservoir properties is used to create an ANN toolbox to predict the production profile. In the inverse process, several design parameters and the desired production profiles were introduced into the input data to create an ANN toolbox to estimate the optimum design parameters. It is found that in both expert systems, functional links play a significant role in the success of the ANN tool. The results estimated by this developed toolbox can be used as preliminary information about the expected production profiles or the required wellbore and fracture design parameters for different cases. This study shows that natural fracture permeability is the principal factor that determines the magnitude of the production. In addition, overall conductivities of the natural fractures and the hydraulic fractures are important factors affecting production.