Gas/condensate reservoirs have been the subject of intensive research throughout the years because they represent an important class of the world's hydrocarbon reserves. Their exploitation for maximum hydrocarbon recovery involves additional complexities that cast them as a different class of reservoirs, apart from dry-gas, wet-gas, and oil reservoirs. Gas/condensate reservoirs are good candidates for compositional-simulation studies because their depletion performance is highly influenced by changes in fluid composition. Often, highly sophisticated and computationally intensive compositional simulations are needed for the accurate modeling of their performance, phase behavior, and fluid-flow characteristics. The desired outcome of a simulation study for gas/condensate reservoirs is the identification and development of the best operational production schemes that maximize hydrocarbon recovery with a minimum loss of condensate at reservoir conditions. However, compositional simulations are demanding in terms of computational overhead, manpower, and software and hardware requirements. Artificial-neural-network (ANN) technology (soft-computing) has proved instrumental in establishing expert systems capable of learning the existing vaguely understood relationships between the input parameters and output responses of highly sophisticated hard-computing protocols such as compositional simulation of gas/condensate reservoirs. In this study, we conduct parametric studies that identify the most influential reservoir and fluid characteristics in the establishment of optimum production protocols for the exploitation of gas/condensate reservoirs. During the training phase of the ANN, an internal mapping is created that accurately estimates the corresponding output for a range of input parameters. In this paper, a powerful screening and optimization tool for the production of gas/ condensate reservoirs is presented. This tool is capable of screening the eligibility of different gas/condensate reservoirs for exploitation as well as assisting in designing the optimized exploitation scheme for a particular reservoir under consideration for development.
All Science Journal Classification (ASJC) codes
- Fuel Technology
- Energy Engineering and Power Technology