A unified MILP model for topological structure of production well gathering pipeline network

Zhang, Haoran; Zhang, Wan; Wang, Bohong; Yan, Xinping; Liao, Qi

doi:10.1016/j.petrol.2017.03.016

Cited by 58 publications

(10 citation statements)

References 17 publications

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“…The solution to this model provides the number and location of production platforms, wells and manifolds, the capacities of the production platforms, the interconnections between platforms, manifolds and wells, and which sections of each well should be vertical or horizontal. Zhang et al (2017) developed a unified MILP formulation for obtaining the best possible topological structure in a production gathering network consisting of wells and pipelines under operational constraints. Their model (solved using MINLP solvers in GUROBI) facilitates the development of field planning schedules.…”

Section: Relevant Literaturementioning

confidence: 99%

A computational performance comparison of MILP vs. MINLP formulations for oil production optimisation

Epelle

Gerogiorgis

2020

Computers & Chemical Engineering

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Model-based oil production systems optimisation under pressure and facility routing constraints is a testing challenge, especially in presence of complex downhole wellbore phenomena (water coning, slugging, phase separation). Nonlinearities and nonconvexities from underlying physics and binary decisions exacerbate model complexity, yielding Mixed Integer Nonlinear Programs (MINLP). To guarantee solvability of optimisation formulations and reduce MINLP complexity, piecewise linearisation techniques based on Special Ordered Sets of type 2 (SOS2) constraints are developed towards approximating nonlinear functions and transforming models to Mixed Integer Linear Programs (MILP). Nevertheless, computational analyses of MILP vs. MINLP formulations for oil production optimisation are scarce. This study explores the benefits of an MILP reformulation applied to three case studies of varying complexity. We compare MILP model results to original MINLP formulation solutions with multiple solvers, evaluating the impact of the number of linearisation breakpoints used on solution time, accuracy, robustness, model development effort and ease of automation.

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Section: Relevant Literaturementioning

confidence: 99%

A computational performance comparison of MILP vs. MINLP formulations for oil production optimisation

Epelle

Gerogiorgis

2020

Computers & Chemical Engineering

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“…Duan et al [12] used the genetic algorithm and ant colony algorithm to optimize the topology of four gas field gathering pipeline layout structures. Zhang et al [13] established a unified mixed integer linear programming model considering the terrain and obstacle conditions to optimize three pipe layout structures. The above studies show that the cost of various pipe layout structures differ greatly.…”

Section: Introductionmentioning

confidence: 99%

Integral Layout Optimization of Subsea Production Control System Considering Three-Dimensional Space Constraint

2021

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The subsea production control system, characterized by a complex and diverse structure and high cost, is one of the essential parts of a subsea production system. The rational layout of the subsea production control system is essential to reduce development costs and ensure safe production in offshore fields. Most previous studies on layout design in offshore fields have focused on the oil- and gas-gathering system. However, the layout of the subsea production control system has not thoroughly been researched to date and the seabed terrain and integral optimization have rarely been discussed. This paper focuses on the multi-layer star structure and multi-layer star-tree structure, two common layout structures of subsea production control systems, and establishes the corresponding model with obstacle and seabed terrain conditions. Obtaining the lowest possible total cost was the aim of the model. A hybrid algorithm combining the adaptive mutation particle swarm algorithm and the A-star algorithm was applied to integrally optimize the subsea distribution unit and umbilical touch down point positions, the pipe connection topology and pipe routes. The practicality of this approach is demonstrated by designing a layout with one FPSO and 22 subsea control modules. The results indicate that the multi-layer star-tree layout structure has a lower total cost compared to that of the multi-layer star layout structure. In addition, the results were compared with a case that ignores the seabed terrain, indicating differences in the total construction cost. This method provides engineers with quantitative references and reliable cost estimates to make decisions regarding the layout of the subsea production control system.

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“…Besides pipe length and diameter, elevation differences play a role in gas distribution, particularly in terrains where the pipeline goes through landscapes with large altitude differences. Zhang et al [28] proposed a model taking into account terrain elevation and other obstacles, optimizing a pipeline connecting production wells.…”

Section: Introductionmentioning

confidence: 99%

A Model of Optimal Gas Supply to a Set of Distributed Consumers

et al. 2019

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A better design of gas supply chains may lead to a more efficient use of locally available resources, cost savings, higher energy efficiency and lower impact on the environment. In optimizing the supply chain of liquefied natural gas (LNG), compressed natural gas (CNG) or biogas for smaller regions, the task is to find the best supplier and the most efficient way to transport the gas to the customers to cover their demands, including the design of pipeline networks, truck transportation and storage systems. The analysis also has to consider supporting facilities, such as gasification units, truck loading lines and CNG tanking and filling stations. In this work a mathematical model of a gas supply chain is developed, where gas may be supplied by pipeline, as compressed gas in containers or as LNG by tank trucks, with the goal to find the solution that corresponds to lowest overall costs. In order to efficiently solve the combinatorial optimization problem, it is linearized and tacked by mixed integer linear programming. The resulting model is flexible and can easily be adapted to tackle local supply chain problems with multiple gas sources and distributed consumers of very different energy demands. The model is illustrated by applying it on a local gas distribution problem in western Finland. The dependence of the optimal supply chain on the conditions is demonstrated by a sensitivity analysis, which reveals how the model can be used to evaluate different aspects of the resulting supply chains.

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A unified MILP model for topological structure of production well gathering pipeline network

Cited by 58 publications

References 17 publications

A computational performance comparison of MILP vs. MINLP formulations for oil production optimisation

A computational performance comparison of MILP vs. MINLP formulations for oil production optimisation

Integral Layout Optimization of Subsea Production Control System Considering Three-Dimensional Space Constraint

A Model of Optimal Gas Supply to a Set of Distributed Consumers

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