A Comparison between Evolutionary Metaheuristics and Mathematical Optimization to Solve the Wells Placement Problem

AlQahtani, Ghazi; Alzahabi, Ahmed; Kozyreff, E.; Farias, Ismael R. de; Soliman, Mohamed Y.

doi:10.4236/aces.2013.34a1005

Cited by 7 publications

(7 citation statements)

References 12 publications

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“…Genetic algorithm is used for solving the pump scheduling in water supply in [6], [7]. A comparison between genetic algorithm and mathematical optimization for solving wells placement problem shows that the mathematical optimization provides better solutions and within less computational time [8].…”

Section: Introductionmentioning

confidence: 99%

Mixed Integer Nonlinear Optimization for ESP Lifted Oil Field and Improved Operation through Production Valve Choking

Sharma¹,

Glemmestad²

2014

IJMO

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Abstract-The demand for the total production of oil from an Electric Submersible Pump (ESP) lifted oil field may vary with time. It may not always be economically profitable to use all of the available oil wells for varying production rates. Depending on the amount of oil to be produced from the oil field, some of the oil wells may have to be shut down. The number of oil wells to be used should be properly selected. Not only the number of oil wells should be known, but which wells to use should also be exactly identified. The operating speed of the ESP and the production choke valve opening of each selected oil well should be optimally chosen so that the profit from the oil field is maximized by keeping the operational expenses at the minimum. The oil well scheduling problem is formulated as a Mixed Integer Nonlinear Programming (MINLP) problem. The cost of the electrical power consumed by the pumps, the operational cost of separation process and the income from the oil production are considered for the MINLP formulation. In the first part of the paper, the production choke valves are always fully opened and the number of wells and their respective pump speeds are calculated for varying total production flow rates. However, in the second half of the paper, it is shown that the operation of the oil field can be improved in terms of power consumption through production valve choking.Index Terms-Mixed integer nonlinear programming, ESP lifted oil field, well scheduling, optimal operation, production valve choking. I. INTRODUCTIONIn an ESP lifted oil field, multiple oil wells are connected in parallel to a common gathering manifold. The oil produced from each of the wells is collected by the gathering manifold and then transported to the separator through the transportation lines (see Fig. 4). Each oil well is fitted with a multistage centrifugal pump. The main operational expense in the oil field is the total cost of power consumed by the pumps and the cost of operating the separator. The income is the production of crude oil from the field. The energy costs associated with the operation of the pumps in an ESP lifted oil field is of great significance when multiple oils wells are considered. The cost of operating the separator is less significant compared to the energy costs of the pumps.The total amount of oil to be produced from the field may vary. For higher production rates from the oil field, all of the available oil wells may be used to meet the demand. On the contrary for lower production rates, some of the oil wells of the oil field can be shut down to meet the lower production demand. The lower the pump speed, the lower is the fluid flow rate through the well. However, running all of the available oil wells at lower pump speeds for fulfilling lower production demands might not be beneficial. The wells might be consuming more power than what is actually needed. Shutting down an oil well reduces the consumption of electric power as the ESP for the well is turned off. So, the balance between fulfilling the to...

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Section: Introductionmentioning

confidence: 99%

Mixed Integer Nonlinear Optimization for ESP Lifted Oil Field and Improved Operation through Production Valve Choking

Sharma¹,

Glemmestad²

2014

IJMO

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“…Previous work by the authors that applied a genetic algorithm included modules for mutation, cross-over, local search, population intrusion, and selection [12]. Of these types of operations, the genetic algorithm applied in the current work used only the mutation and selection modules, in order to focus on achieving high performance from the mutation operation.…”

Section: Genetic Algorithm Designmentioning

confidence: 99%

“…It is common practice, in IP, to formulate problems by defining an objective function to be maximized (or minimized), subject to constraints that define the problem in question. Here, the formulation of the wells placement problem was presented in [12].…”

Section: Optimization Via Mathematical Formulationsmentioning

confidence: 99%

“…For tests using IP, the formulations generated were written in MATLAB and the optimization models were resolved using Gurobi 5.6 solver with default settings which were set to Branch-and-Cut solution strategy and the absolute gap tolerances were set to zero. The other controls used are the same as described in [12].…”

Section: Optimization Computationsmentioning

confidence: 99%

“…An earlier work compared the use of genetic algorithm and integer programming to solve this problem [12]. For each case studied, it was found that IP was capable of finding solutions at least as good as those obtained from genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Computational Comparison between Optimization Techniques for Wells Placement Problem: Mathematical Formulations, Genetic Algorithms and Very Fast Simulated Annealing

Dhahran¹,

Lubbock²,

.³

2014

MSCE

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This study considers several computational techniques for solving one formulation of the wells placement problem (WPP). Usually the wells placement problem is tackled through the combined efforts of many teams using conventional approaches, which include gathering seismic data, conducting real-time surveys, and performing production interpretations in order to define the sweet spots. This work considers one formulation of the wells placement problem in heterogeneous reservoirs with constraints on inter-well spacing. The performance of three different types of algorithms for optimizing the well placement problem is compared. These three techniques are: genetic algorithm, simulated annealing, and mixed integer programming (IP). Example case studies show that integer programming is the best approach in terms of reaching the global optimum. However, in many cases, the other approaches can often reach a close to optimal solution with much more computational efficiency.

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Multigrid Fracture Stimulated Reservoir Volume Mapping Coupled with a Novel Mathematical Optimization Approach to Shale Reservoir Well and Fracture Design

Alzahabi¹,

Berlow²,

Soliman³

et al. 2019

Hydraulic Fracturing and Well Stimulation

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A Comparison between Evolutionary Metaheuristics and Mathematical Optimization to Solve the Wells Placement Problem

Cited by 7 publications

References 12 publications

Mixed Integer Nonlinear Optimization for ESP Lifted Oil Field and Improved Operation through Production Valve Choking

Mixed Integer Nonlinear Optimization for ESP Lifted Oil Field and Improved Operation through Production Valve Choking

A Computational Comparison between Optimization Techniques for Wells Placement Problem: Mathematical Formulations, Genetic Algorithms and Very Fast Simulated Annealing

Multigrid Fracture Stimulated Reservoir Volume Mapping Coupled with a Novel Mathematical Optimization Approach to Shale Reservoir Well and Fracture Design

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