Simultaneous Optimal Placement of Injector and Producer Wells Using Mathematical Programming

Leow, Wei Xiang; Tavallali, M. Sadegh; Karimi, Iftekhar A.; Teo, Kwong Meng

doi:10.1016/b978-0-444-59506-5.50127-9

Cited by 1 publication

(1 citation statement)

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“…Mathematical programming presents as disadvantage, the inaccuracy of the models representing the reservoir response (Leow et al, 2012). By this reason, some studies have used some techniques to overcome this problem, like Tavallali et al (2011), whose work uses rigorous multiphase flow model in their Mixed Integer Non Linear Programming (MINLP) formulation to optimize producer locations and later improved by Leow et al (2012) who obtained as final formulation a dynamic an non-convex MINLP solved by a modified outer approximation method.…”

Section: Strategy Optimizationmentioning

confidence: 99%

Application of Assisted Optimization to Aid Oil Exploitation Strategy Selection for Offshore Fields

Gaspar

Barreto

Mazo

et al. 2014

SPE Latin America and Caribbean Petroleum Engineering Conference

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Decision-making processes for selecting an oil exploitation strategy can be complex due to the high number of variables to be optimized. Many times, it can be unfeasible to search an optimal solution by evaluating a high quantity of variables simultaneously. In this case, assisted methods that involve engineering analyses and mathematical optimization algorithms are an alternative to obtain a good solution. This paper shows the application of an assisted method to optimize a large number of variables of an oil exploitation strategy. The proposed methodology is to order and combine different optimization procedures with practical engineering analysis. The optimization variables include number and position of wells, platform capacities, wells opening schedule and wells shut-in time. The methodology is applied to a reservoir model based on a Brazilian offshore oil field to discuss the results obtained. Results indicate an efficient procedure for evaluating deterministic scenarios, suggesting optimization procedures for each decision variable and enabling the achievement of good quality solutions with a reasonable number of simulation runs. This is useful in many practical cases, mainly those, which require runs with long simulation time.

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Section: Strategy Optimizationmentioning

confidence: 99%