Study of Gas/Condensate Reservoir Exploitation Using Neurosimulation
- Luis Felipe Ayala (Pennsylvania State U.) | Turgay Ertekin (Pennsylvania State U.) | Michael A. Adewumi (Pennsylvania State U.)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- April 2007
- Document Type
- Journal Paper
- 140 - 149
- 2007. Society of Petroleum Engineers
- 4.6 Natural Gas, 5.2.1 Phase Behavior and PVT Measurements, 5.2 Reservoir Fluid Dynamics, 7.6.6 Artificial Intelligence, 5.3.2 Multiphase Flow, 4.2 Pipelines, Flowlines and Risers, 5.2.2 Fluid Modeling, Equations of State, 5.1 Reservoir Characterisation, 6.1.5 Human Resources, Competence and Training, 5.5 Reservoir Simulation, 4.3.4 Scale, 5.6.4 Drillstem/Well Testing, 5.8.8 Gas-condensate reservoirs, 5.4.3 Gas Cycling
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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.
In a gas/condensate reservoir, initial reservoir conditions are located between the critical point and cricondentherm of the reservoir fluid, as shown in Fig. 1. In general, in a gas/condensate reservoir, the initial fluid is found in an all-vapor condition. A liquid phase is later developed once the dewpoint of the system has been reached as a result of isothermal depletion. Appearance of a liquid phase upon vapor expansion (under isothermal conditions) is not possible for pure substances; thus, this behavior is categorized as "retrograde?? for this particular type of mixture. The retrograde liquid may even revaporize if depletion continues. The major concerns while producing a gas/condensate reservoir have to do with the loss of this valuable liquid to the reservoir and the associated impairment in gas productivity. Accordingly, the study of a gas/condensate reservoir usually entails the use of the most sophisticated phase-behavior and fluid-flow tools at the disposal of the reservoir engineer.
The type of application of neural-network technology used in this work is described as neurosimulation. In neurosimulation, hard-computing techniques (such as numerical computations) are coupled with soft-computing techniques (such as ANNs) for the development of a powerful expert system. Numerical models provide a precise and formal "expertise??—at a significant computational expense—that can be taught to a soft-computing tool that, once trained, can exploit and apply the learned expertise through much-less-intense computational work.
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