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Optimum deployment of nonconventiona...

Yeten, Burak.

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Optimum deployment of nonconventional wells.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Optimum deployment of nonconventional wells./
作者:	Yeten, Burak.
面頁冊數:	160 p.
附註:	Source: Dissertation Abstracts International, Volume: 64-05, Section: B, page: 2367.
Contained By:	Dissertation Abstracts International64-05B.
標題:	Engineering, Petroleum. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3090710

Optimum deployment of nonconventional wells.
Yeten, Burak.

Optimum deployment of nonconventional wells. - 160 p.

Source: Dissertation Abstracts International, Volume: 64-05, Section: B, page: 2367.

Thesis (Ph.D.)--Stanford University, 2003.

Nonconventional wells 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. To maximize reservoir performance, 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.Subjects--Topical Terms:

1018448
Engineering, Petroleum.

Optimum deployment of nonconventional wells.
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500 $a Source: Dissertation Abstracts International, Volume: 64-05, Section: B, page: 2367.
500 $a Advisers: Khalid Aziz; Louis J. Durlofsky.
502 $a Thesis (Ph.D.)--Stanford University, 2003.
520 $a Nonconventional wells 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. To maximize reservoir performance, 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.
520 $a We apply a genetic algorithm as our master engine for the optimization of well type, location and trajectory. This engine is accompanied by an artificial neural network which acts as a proxy to the reservoir simulations, 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 to reduce the number of simulations required to quantify the effects of the multiple uncertain parameters during this optimization process.
520 $a We also developed an optimization tool based on a nonlinear conjugate gradient algorithm that enables decisions regarding the deployment of smart completion technology. With this strategy, reservoirs can be screened for smart well technology. Reservoir uncertainty can also be accounted for within this framework.
520 $a 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. 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.
520 $a We next screen various types of reservoirs and wells with our control optimization and quantify the benefits of deploying this technology. Improvement in predicted performance using inflow control devices is demonstrated for all of the examples considered.
520 $a Finally we apply all the tools we have developed to a portion of an oil field located in Saudi Arabia. We demonstrate the potential benefits of deploying optimized multilateral wells and smart completions.
590 $a School code: 0212.
650 4 $a Engineering, Petroleum. $3 1018448
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790 1 0 $a Durlofsky, Louis J., $e advisor
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791 $a Ph.D.
792 $a 2003
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