1. Paper title:
Generalized field-development optimization with well control zonation.
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2. Author contact details:
Abeeb A. Awotunde
Department of Petroleum Engineering,
King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
awotunde@kfupm.edu.sa
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3. Corresponding author:
Abeeb A. Awotunde
awotunde@kfupm.edu.sa
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4. Paper abstract:
Of concern in the development of oil fields, is the problem of determining the optimal locations of wells and the optimal controls to place on the wells. Extraction of hydrocarbon resources from petroleum reservoirs in a cost effective manner requires that the producers and injectors be placed at optimal locations and optimal controls be imposed on the wells. While the optimization of well locations and well controls plays an important role in ensuring that the net present value of the project is maximized, optimization of other factors such as well type and number of wells also plays important roles in increasing the profitability of investments. Until very recently, improving the net worth of hydrocarbon assets has been focused primarily on optimizing the well locations or well controls, mostly manually. In recent times, automatic optimization using either gradient-based algorithms or stochastic (global) optimization algorithms has become increasingly popular.
A well-control zonation (WCZ) approach to estimating optimal well locations, well rates, well type and well number is proposed. Our approach uses a set of well coordinates and a set of well-control variables as the optimization parameters. However, one of the well-control variables has its search range extended to cover three parts, one part denoting the region where the well is an injector, a second part denoting the region where there is no well, and a third part denoting the region where the well is a producer. By this, the optimization algorithm is able to match every member in the set of well coordinates to three possibilities within the search space of well controls: an injector, a no-well situation or a producer. The optimization was performed using differential evolution and two sample applications were presented to show the effectiveness of the method. Results obtained show that the methods are able to reduce the number of optimization variables needed and also to identify simultaneously, optimal well locations, optimal well controls, optimal well type and the optimum number of wells. Also, comparison of results with the mixed integer nonlinear linear programming (MINLP) approach shows that the WCZ approach mostly outperformed the MINLP approach.
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5. Relevant criteria:
A, B, D, E, G
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6. Statement:
A: There have been patents pertaining to the optimization of well placement and well rates but none of these considers the efficient determination of the number and type (injector or producer) of wells. This paper implicitly determines the well type and the number of wells from the well control variable and is a significant improvement over any existing patented field-development optimization algorithm. Thus, it would qualify today as a patentable new invention
B: The closest publication to this work is that of Isebor et al. (2014) which equally determines the number and type of wells but uses an additional variable (integer variable) per well for well-type estimation. This work is again a significant improvement over Isebor et al. (2014) because it makes the use of the additional variable (per each well declared) unnecessary, thus reducing the problem dimension. This work also coverts the problem from a mixed integer nonlinear optimization programming (MINLP) problem to a continuous optimization that is much efficient to solve using any global/stochastic optimization algorithm.
D: This work is publishable in its own right and has just been published by one of the most reputable journals in computational/mathematical geosciences.
E: The problem of oilfield development optimization is a long-standing one with incremental solutions over the years. Only recently, have oil and gas service companies included automated optimization of field development in their commercial simulators. Even then, the existing implementations in commercial software are still crude and much behind what is presented in my paper. We successfully used DE to optimize the well control, well locations as well as the number and type of wells in a continuous optimization setting. Thus the paper satisfies Criterion E.
G: The problem of field development optimization is quite a challenging one that involves several optimization variables. The number of wells needed to tap the oil and gas resources in a hydrocarbon reservoir increases with the size of the oil field to be developed. An increase in the number of wells also means an increase in the number of optimization variables with total number of variables running into hundreds. Also, some variables are continuous while at least one variable (well-type) is discrete making the problem a challenging one.
References
Isebor, O.J., Ciaurri, D.E. and Durlofsky, L. J. (2014): Generalized field-development optimization with derivative-free procedures
SPE J., 19(5): 891-908.
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7. Citation:
Awotunde, A. A. (2016): Generalized field-development optimization with well-control zonation
Computational Geosciences, 20(1): 213-230.
http://link.springer.com/article/10.1007%2Fs10596-016-9559-2
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8. Any prize money, if any, is to be awarded to Abeeb A. Awotunde (the sole contributor to this work)
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9. "Best" statement:
The paper is a significant contribution to the economic extraction of oil and gas resources at a time when a decline in oil and gas prices is sending companies out of business. It helps oil and gas companies make critical decisions on the number of production and injection wells to drill, the rate at which to produce oil or inject water to drive the oil towards the production well and the position in the reservoir where to place each injector or producer. The optimization procedure reduces the costs of production while increasing the amount of oil recovered from the oilfield, thus increasing the net present value of investment.
More importantly, the procedure presented helps to simplify the optimization problem by reducing the number of optimization variables involved and converting the problem from a mixed-integer nonlinear programming to a continuous optimization problem so that highest net present value can be obtained quickly within the limited computational resources available.
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10. Method used:
Differential Evolution (DE).