Abstract:

Nonconventional wells (i.e., wells with an arbitrary trajectory or multiple branches) offer great potential for the recovery of petroleum resources. Wells of this type are underutilized in practice, however, in part because it is difficult to optimize their deployment. In this dissertation, we focus on the reservoir engineering aspects of the optimum deployment of nonconventional wells. The effects of uncertain geological and engineering parameters are included in this optimization. To maximize reservoir performance (recovery or net present value), we optimize the number of producers and injectors, their types (e.g., vertical, horizontal or multilateral), locations and trajectories, as well as their control strategy via smart (intelligent) completions.

We apply a genetic algorithm (GA) as our master engine for the optimization of well type, location and trajectory. This engine is accompanied by an artificial neural network (ANN) which acts as a proxy to the reservoir simulations (objective function evaluations), a hill climber, which searches the local neighborhood of the current solution, and a near wellbore upscaling, which allows the incorporation of near wellbore heterogeneity from detailed reservoir descriptions into coarse simulation models. In addition, we introduce an experimental design methodology (ED) to reduce the number of simulations required to quantify the effects of the multiple uncertain parameters during this optimization process. Within this framework we can account for the control of the wells through a ``reactive'' control strategy. Using such a strategy, downhole control devices can open or close depending on the fluids produced from different segments of the well.

We also developed an optimization tool based on a nonlinear conjugate gradient algorithm that enables decisions regarding the deployment of smart completion technology. This tool is independent of the well type, location and trajectory optimization. It allows us to implement a ``defensive'' control strategy; i.e., the control devices are opened or closed based on a well control optimization. With this strategy, reservoirs can be screened for smart well technology. Reservoir uncertainty can also be accounted for within this framework.

We present single and multiple well deployment examples for different synthetic reservoir models. In these examples, well type, location and trajectory are optimized. The effects of uncertainty are included in several of the examples. We determine sensible single and multiple well deployment plans with the algorithms developed. We show that the objective function (cumulative oil produced or net present value of the project) is always increased relative to its value in the first generation of the optimization, in some cases by 30% or more. The optimal well type is found to vary depending on the reservoir model and objective function. We also show that the optimal type of well can differ depending on whether single or multiple realizations of the reservoir geology are considered.

We next screen various types of reservoirs and wells with our defensive control optimization and quantify the benefits of deploying this technology. Improvement in predicted performance using inflow control devices, which is as high as 65% in one case, is demonstrated for all of the examples considered. There is, however, significant variation in the level of improvement attainable using these devices, so sophisticated decision making techniques may be required when considering their use in practice.

Finally we apply all the tools we have developed to a portion of a giant oil field located in Saudi Arabia. We demonstrate the potential benefits of deploying optimized multilateral wells and smart completions. The complex geological features in this field illustrate the advantages of this technology in a practical setting.

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Copyright 2003, Burak Yeten: Please note that the reports and theses are copyright to their original authors. Authors have given written permission for their work to be made available here. Readers who download reports from this site should honor the copyright of the original authors and may not copy or distribute the work further without the permission of the author, Burak Yeten.