Improving Gas Storage Development Planning Through Simulation-Optimization

Abstract

This is the first of two papers describing the application of simulator-optimization methods to a natural gas storage field development planning problem. The results presented here illustrate the large gains in cost-effectiveness that can be made by employing the reservoir simulator as the foundation for a wide-ranging search for solutions to management problems. The current paper illustrates the application of these techniques given a deterministic view of the reservoir. A companion paper will illustrate adaptations needed to accommodate uncertainties regarding reservoir properties. IntroductionAlthough reservoir simulation is a well-established component of reservoir management throughout much of the petroleum industry, little use has been made of reservoir simulation coupled with systematic optimization techniques, i.e. simulation-optimization.The main advantage of applying optimization tools, per se, to decision-making problems is that they are less restricted by human imagination than conventional case-by-case comparisons. As the number of competing engineering, economic, and environmental planning objectives and constraints increases, it becomes difficult for human planners to track complex interactions and select a manageable set of promising development strategies for examination. Using optimization techniques, the search can range over all possible combinations of variables, locating strategies whose effectiveness is not always obvious to planners.The advantage of coupling the reservoir simulator to these optimization tools is that the search for strategies can be based on the simultaneous evaluation of reservoir performance measures and other economic/environmental/policy considerations. It is no longer necessary to treat technical decisions driven by simulator forecasts of reservoir response and these other components of the decision-making process as separate steps.The single biggest obstacle to the application of optimization techniques using a reservoir simulator as the forecasting tool is the computational time required to complete a single simulation. Even the examination of 10 variations on a well-field design becomes cumbersome when a single run requires hours to complete. Extending the use of these simulators into optimization regimes involving hundreds or thousands of runs poses a computational problem bigger than most organizations are willing or able to tackle.The ANN-GA solution to this problem is to train artificial neural networks (ANNs) to predict selected information that the simulator would normally predict. A heuristic technique such as the Genetic Algorithm (GA) then searches for increasingly better strategies (for example, the most productive in-fill drilling pattern), using the trained networks to evaluate the effectiveness of each strategy in place of the

Gas Storage Facility Design Under Uncertainty

“…When there is simultaneous uncertainty in subsurface properties, production and injection performance, capital and operating costs, and demand requirements, the use of complex reservoir models may constrain the scope of uncertainty analysis because of time and/or resource constraints (Johnson et al 2000;Lawal et al 2008). Even with experimental design workflows, there can be some sacrifice in the scope of the uncertainty analysis.…”

“…After this initial work, there appears to be a gap in the research literature. The issue of joint reservoir/facility optimization was taken up again more recently in de Moegen et al (1996), Khodri et al (1997), Johnson et al (2000), McVay and Spivey (2001), and Kuncir et al (2003). However, no study addressed uncertainty in a systematic way.…”

Design and Demonstration of Creating Underground Gas Storage in a Fractured Oil Depleted Carbonate Reservoir

“…Many literatures illustrated design and demonstration of creating underground gas storage [3][4][5][6][7] . However, as far as know, among existing UGS in China, fractured oil depleted carbonate reservoir converted into UGS is unprecedented.…”