Optimization of Well Placement Under Time-Dependent Uncertainty
- Umut Ozdogan (Chevron Energy Technology Co.) | Roland N. Horne (Stanford University)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- April 2006
- Document Type
- Journal Paper
- 135 - 145
- 2006. Society of Petroleum Engineers
- 4.6 Natural Gas, 2.3.4 Real-time Optimization, 1.6 Drilling Operations, 5.1.5 Geologic Modeling, 5.6.4 Drillstem/Well Testing, 5.5 Reservoir Simulation, 5.5.8 History Matching
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Well-placement decisions made during the early stages of exploration and development activities have the capability to improve later placement decisions by providing more information (greater certainty). Therefore, recovery and efficient use of information may add value beyond the amount of oil recovered. This study proposes an approach that emphasizes the value of time-dependent information to achieve better decisions in terms of reduced uncertainty and increased probable net present value (NPV). Unlike previous approaches, well-placement optimization is coupled with recursive probabilistic history-matching steps through the use of the pseudohistory concept. The pseudohistory is defined as the probable (future) response of the reservoir that is generated by a probabilistic forecasting model. To test the results of the proposed approach, an example reservoir was investigated with multiple realizations, all of which match the same production history. The results of this study showed that subsequent well-placement decisions can be improved when probabilistic production profiles obtained from the wells, as they are drilled, are incorporated in the optimization scheme..
Well placement is one of the important decisions made during the exploration and development phase of projects. Most of the time, the large number of possibilities, constraints on computational resources, and the size of the simulation models limit the number of possible scenarios that may be considered. In these cases, optimization algorithms become extremely valuable in searching for the optimum development scenario.
Various approaches have been proposed for production optimization. Bittencourt (1994) optimized the scheduling of a field using the polytope algorithm. Beckner and Song (1995) applied the traveling salesman framework on a well-placement problem, using simulated annealing (SA) to find the optimum locations of the wells. Bittencourt and Horne (1997) hybridized genetic algorithms (GA) with the polytope algorithm and tabu search and referred to this hybrid optimization technique as HGA. HGA was observed to improve the economic forecasts and CPU effort during optimization. Pan and Horne (1998) used kriging as a proxy to the reservoir simulator to decrease the number of simulations. Guyaguler et al. (2000) showed that the number of simulations required to optimize the injector well locations decrease when an HGA was coupled with a kriging proxy. Yeten et al. (2002) coupled GA with hill-climbing methods and an artificial neural network (ANN) proxy to optimize the type, location, and trajectory of nonconventional wells. Guyaguler and Horne (2001) assessed the uncertainty of the well-placement results using utility theory together with multiple realizations of the reservoir.
All these approaches considered only the information that was available at the beginning of the optimization process. Data that would become available as the reservoir developed in time was not taken into account.
|File Size||3 MB||Number of Pages||11|
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