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Optimization of well placement and a...

Guyaguler, Baris.

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Optimization of well placement and assessment of uncertainty.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Optimization of well placement and assessment of uncertainty./
Author:	Guyaguler, Baris.
Description:	137 p.
Notes:	Source: Dissertation Abstracts International, Volume: 63-04, Section: B, page: 2036.
Contained By:	Dissertation Abstracts International63-04B.
Subject:	Engineering, Petroleum. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3048536
ISBN:	0493628673

Optimization of well placement and assessment of uncertainty.
Guyaguler, Baris.

Optimization of well placement and assessment of uncertainty. - 137 p.

Source: Dissertation Abstracts International, Volume: 63-04, Section: B, page: 2036.

Thesis (Ph.D.)--Stanford University, 2002.

Determining the best location for new wells is a complex problem that depends on reservoir and fluid properties, well and surface equipment specifications, and economic criteria. Various approaches have been proposed for this problem. Among those, direct optimization using the simulator as the evaluation function, although accurate, is in most cases infeasible due to the number of simulations required. This study proposes a hybrid optimization technique (HGA) based on the genetic algorithm (GA) with helper functions based on the polytope algorithm and the kriging algorithm. Hybridization of the GA with these helper methods introduces hill-climbing into the stochastic search and also makes use of proxies created and calibrated iteratively throughout the run. Performance of the technique was investigated by optimizing placement of injection wells in the Gulf of Mexico Pompano field. It was observed that the number of simulations required to find optimal well configurations was reduced significantly which enabled the use of full-scale simulation. The optimum development plan for another real world reservoir located in the Middle East was investigated. Optimization using the numerical simulator as the evaluation function for the field posed significant challenges since the model has half a million cells. The GA was setup in parallel on four processors to speed up the optimization process. The optimal deployment schedule of 13 predrilled wells that would meet the production target specified by the operating company was sought. The problem was formulated as a traveling salesman problem and the order of wells in the drilling queue was optimized. Ways to assess the uncertainty in the proposed reservoir development plan were also investigated. An approach that can translate the uncertainty in the data to uncertainty in terms of monetary value was developed. In this study the uncertainties associated with well placement were addressed within the utility theory framework using numerical simulation as the evaluation tool. The methodology was evaluated using the PUNQ-S3 model. Experiments were carried on 23 history-matched realizations and a truth case was also available. Utility theory not only offered the framework to quantify the influence of uncertainties in the reservoir description in terms of monetary value but also provided the tools to quantify the otherwise arbitrary notion of risk attitude.

ISBN: 0493628673Subjects--Topical Terms:

1018448
Engineering, Petroleum.

Optimization of well placement and assessment of uncertainty.
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100 1 $a Guyaguler, Baris. $3 1900234
245 1 0 $a Optimization of well placement and assessment of uncertainty.
300 $a 137 p.
500 $a Source: Dissertation Abstracts International, Volume: 63-04, Section: B, page: 2036.
500 $a Adviser: Roland N. Horne.
502 $a Thesis (Ph.D.)--Stanford University, 2002.
520 $a Determining the best location for new wells is a complex problem that depends on reservoir and fluid properties, well and surface equipment specifications, and economic criteria. Various approaches have been proposed for this problem. Among those, direct optimization using the simulator as the evaluation function, although accurate, is in most cases infeasible due to the number of simulations required. This study proposes a hybrid optimization technique (HGA) based on the genetic algorithm (GA) with helper functions based on the polytope algorithm and the kriging algorithm. Hybridization of the GA with these helper methods introduces hill-climbing into the stochastic search and also makes use of proxies created and calibrated iteratively throughout the run. Performance of the technique was investigated by optimizing placement of injection wells in the Gulf of Mexico Pompano field. It was observed that the number of simulations required to find optimal well configurations was reduced significantly which enabled the use of full-scale simulation. The optimum development plan for another real world reservoir located in the Middle East was investigated. Optimization using the numerical simulator as the evaluation function for the field posed significant challenges since the model has half a million cells. The GA was setup in parallel on four processors to speed up the optimization process. The optimal deployment schedule of 13 predrilled wells that would meet the production target specified by the operating company was sought. The problem was formulated as a traveling salesman problem and the order of wells in the drilling queue was optimized. Ways to assess the uncertainty in the proposed reservoir development plan were also investigated. An approach that can translate the uncertainty in the data to uncertainty in terms of monetary value was developed. In this study the uncertainties associated with well placement were addressed within the utility theory framework using numerical simulation as the evaluation tool. The methodology was evaluated using the PUNQ-S3 model. Experiments were carried on 23 history-matched realizations and a truth case was also available. Utility theory not only offered the framework to quantify the influence of uncertainties in the reservoir description in terms of monetary value but also provided the tools to quantify the otherwise arbitrary notion of risk attitude.
590 $a School code: 0212.
650 4 $a Engineering, Petroleum. $3 1018448
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790 1 0 $a Horne, Roland N., $e advisor
790 $a 0212
791 $a Ph.D.
792 $a 2002
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