Optimization of Well Placement Under Time-Dependent Uncertainty


Umut Ozdogan







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Determining the optimum location of the wells is a crucial decision to be made during afield development plan. The quality of the decision is strongly dependent upon theamount of the information available to the decision-maker at the time the decision ismade. Knowing that the development phase of a reservoir is a dynamic period in whichdifferent categories of information are added to system from distinct sources, one shouldmake the well placement decisions considering these time-dependent contributions ofinformation. This study proposes an approach that addresses the value of time-dependentinformation to achieve better decisions in terms of reduced uncertainty and increasedprobable Net Present Value (NPV). A Hybrid Genetic Algorithm (HGA) was used as theoptimization method to find the best locations of the wells. In order to find the optimumdecisions for different risk attitudes, a utility framework, that enables the assessment ofthe uncertainty of the well-placement decisions, was used. Through this new approach, production history data obtained from the wells, as they are drilled, are integrated into thewell placement decisions. Unlike previous approaches, well placement optimization iscoupled with recursive history matching steps. To test the results of the proposedapproach, an example reservoir and its realizations, all of which match the historyresponse of the example reservoir, were investigated. At each step of optimization, areduction in the uncertainty of the multiple realizations was observed, as productionhistory became available.

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