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Book:
Gas Allocation Optimization Methods in Artificial Gas Lift
Author:
Khamehchi Ehsan / Mahdiani Mohammad Reza
Publisher:
Springer International Publishing
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2017
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Cham;Switzerland
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This Brief offers a comprehensive study covering the different aspects of gas allocation optimization in petroleum engineering. It contains different methods of defining the fitness function, dealing with constraints and selecting the optimizer; in each chapter a detailed literature review is included which covers older and important studies as well as recent publications. This book will be of use for production engineers and students interested in gas lift optimization.;Preface; Contents; Abbreviations; 1 An Introduction to Gas Lift; Abstract; 1.1 Introduction; 1.2 Formulation of the Problem; References; 2 The Fitness Function of Gas Allocation Optimization; Abstract; 2.1 Introduction; 2.2 Technical Factors; 2.2.1 Nodal Analysis; 2.2.2 Analytical Models; 2.2.3 Proxy Models; 2.3 Economic Factors; 2.4 Integrated Model; References; 3 Constraint Optimization; Abstract; 3.1 Introduction; 3.2 No Limitation; 3.3 Maximum Amount of Available Lift Gas; 3.4 Injection and Production Limitations; 3.5 Stability; 3.6 Controlling the System; 3.6.1 Smart Methods; References.

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The Author(s) 2017

Ehsan Khamehchi and Mohammad Reza Mahdiani Gas Allocation Optimization Methods in Artificial Gas Lift SpringerBriefs in Petroleum Geoscience & Engineering 10.1007/978-3-319-51451-2_1

1. An Introduction to Gas Lift

Ehsan Khamehchi 1 and Mohammad Reza Mahdiani 1

(1)

Faculty of Petroleum Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

Ehsan Khamehchi

Email:

Abstract

When the reservoir pressure declines, the production oil rate decreases and falls below the economic limit. One of the methods to increase the production rate is the gas lift. In this process, gas is injected to the well from the annulus. Then at the injection point it enters the tubing and is dissolved in the tubings fluid which causes the reduction of the fluid density. Thus, the head pressure of the fluid column decreases, and then the production rate increases. Usually in gas lift projects there is a limited amount of gas that should be allocated between some wells in a way that some limitations such as the amount of fluid production, injection rate and facilities constraints are satisfied. Different wells have their specific properties and thus their respond to the injected gas is different. Finding a method regarding these constraints that maximizes the production is the subject of gas allocation optimization. This method should consider the properties of the wells, reservoir and facilities, as well as its proposed optimum point and should satisfy all constraints. In this chapter the physics of the gas lift and a briefing on how to formulate the gas allocation optimization problems will be given.

Keywords

Constraint optimization Gas injection Gas lift Gas allocation Optimization methods

1.1 Introduction

As the production continues, reservoir pressure declines and causes a reduction in the petroleum production rate. In these cases, using artificial lift methods such as gas lift is inevitable (Shen et al. shows a schematic of this operation.

Fig. 1.1

A schematic of gas lift operation (Ghassemzadeh et al. )

In the formulation of every optimization problem, first a fitness function should be defined. The fitness function catches some inputs (here gas injection rate (qgi) of different wells) and returns the output. Output can be a single value or an array of values (multi objective) (Deb ). The optimizer should find a point which minimizes or maximizes the output while satisfying all constraints.

The increment of the oil depends on many factors such as gas injection rate and reservoir and well properties (Chithra Chakra et al. shows a schematic of the gas allocation process.

Fig. 1.2

A schematic of gas allocation (Monfared and Helalizadeh )

This optimization based on the performance of each well, assigns some gas to each well in which the total profit of all wells is maximized and also some constraints are satisfied (Mahdiani and Khamehchi ).

In this book, different aspects of gas allocation optimization will be discussed. First, the formulation of the problem and different methods (such as the building the proxy models) will be reviewed and the advantages and disadvantages of each method will be surveyed. Afterwards, different constraints that can be considered in this problem will be discussed and finally the optimization algorithms and their efficiency will be reviewed.

1.2 Formulation of the Problem

The problem is that the total production oil rate (QO) which is the sum of individual production of each well (qOi) should be maximized by allocating an optimum arrangement of gas injection rate (qgi) to the wells. In mathematical form it can be expressed as ( ):

11 12 The superscript T denotes the transposition of the - photo 3

(1.1)

Gas Allocation Optimization Methods in Artificial Gas Lift - image 4

(1.2)

The superscript T denotes the transposition of the matrix. Based on the above equation, the gas allocation optimization problem can be expressed as ():

Gas Allocation Optimization Methods in Artificial Gas Lift - image 5

(1.3)

This problem can be exposed to some constraints.

For example ():

14 In addition to maximizing the total oil production the system can have - photo 6

(1.4)

In addition to maximizing the total oil production, the system can have some other objectives to be maximized or minimized such as minimizing the water cut. When the problem has more than one objective, we are dealing with a multi objective optimization problem (Sifuentes et al. ).

References

Bejan, A. (1995). Entropy generation minimization: The method of thermodynamic optimization of finite-size systems and finite-time processes . CRC Press.

Bhatti, M. A. (2000). Practical optimization methods: With mathematica applications: CD-ROM included. Springer Science & Business Media.

Chithra Chakra, N., et al. (2015). An innovative neural forecast of cumulative oil production from a petroleum reservoir employing higher-order neural networks (HONNs). Journal of Petroleum Science and Engineering, (34), 1833.

Deb, K. (2001). Multi-objective optimization using evolutionary algorithms . Wiley.

Ghassemzadeh, S., et al. (2015). Optimization of gas lift allocation for improved oil production under facilities constraints. Geosystem Engineering, (3), 3947.

Jacoud, A., et al. (2015). Modelling and extremum seeking control of gas lifted oil wells. IFAC-PapersOnLine, (2012), 2126.

Khamehchi, E., et al. (2009). Continuous gas lift optimization with a novel genetic algorithm. Australian Journal of Basic and Applied Sciences, (4), 587594.

Mahdiani, M. R., & Khamehchi, E. (2015a). Preventing instability phenomenon in gas-lift optimization. Iranian Journal of Oil & Gas Science and Technology, (1), 4965.

Mahdiani, M. R., & Khamehchi, E. (2015b). Stabilizing gas lift optimization with different amounts of available lift gas. Journal of Natural Gas Science and Engineering, 26, 1827. CrossRef

Monfared, M., & Helalizadeh, A. (2013). Simulation and gas allocation optimization of gas lift system using genetic algorithm method in one of Iranian oil field to sales distribution liquid gathering line gas injection line valves Flow line well 2. Journal of Basic and Applied Scientific Research, 3 (3), 732738.

Neustadt, L. W. (2015). Optimization: a theory of necessary conditions . Princeton University Press.

Newman, T. R. (2008). Multiple objective fitness functions for cognitive radio adaptation . ProQuest.

Nishikiori, N., et al. (1989). An improved method for gas lift allocation optimization. In SPE Annual Technical Conference and Exhibition . Society of Petroleum Engineers.

Osman, M. S., Abo-Sinna, M. A., & Mousa, A. A. (2005). An effective genetic algorithm approach to multiobjective resource allocation problems (MORAPs). Applied Mathematics and Computation, (2), 755768. MathSciNet CrossRef MATH

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