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Integration of oilfield planning problems: infrastructure design, development planning and production scheduling

JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2017年 158卷 585-602页

作者： Redutskiy, Yury Molde Univ Coll POB 2110 N-6402 Molde Norway

The oil and gas industry faces considerable technological challenges nowadays, mainly associated with nonconventional reserves and offshore environments. To ensure the economic efficiency of operations under such complicated conditions, industrial solutions called "smart fields" were introduced. A vital part of these solutions is strategic planning which aims to coordinate field development and hydrocarbons production operations to optimize the entire field lifecycle, given the chosen planning criterion (e.g., net present value of the project). Still, in many cases engineering design is conducted with a conservative approach of solving one problem at a time. In this study, a multi-period mixed integer nonlinear problem (MINLP) covering the relevant issues of both development and production phases, is introduced. The contribution of the proposed model to the area of oilfield development planning is related to the model's simultaneously incorporating technological details regarding the infrastructure layout, pressures and flows in the gathering system, and artificial lift performance, all of which ultimately aims to provide realistic production scheduling results and cost evaluation. The composed model is used to study the benefits of joint development and production planning in comparison to planning these two phases of the project separately. This research is relevant to the engineering design departments and contractors for the preliminary design stage of the engineering project when the infrastructures layout and capacities are chosen, and rough estimates of costs and revenues are calculated.

关键词： Development and production planning Electrical submersible pump Integrated decision-making mixed integer nonlinear programming Oilfield infrastructure design Operational efficiency Production scheduling

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REACTIVE OPERATING THEATRE SCHEDULING

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Bulletin of the Australian Mathematical Society 2018年第3期98卷 520-521页

作者： BELINDA SPRATT School of Mathematical Sciences Queensland University of Technology 2 George St Brisbane 4000 Australia email b.spratt@qut.edu.au

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Practical Inner Codes for Batched Sparse Codes

Practical Inner Codes for Batched Sparse Codes

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IEEE Global Telecommunications Conference (GLOBECOM)

作者： Zhou, Zhiheng Li, Congduan Guang, Xuan Univ Elect Sci & Technol China Natl Key Lab Sci & Technol Commun Chengdu Sichuan Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China Chinese Univ Hong Kong Inst Network Coding Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781509050192

Batched sparse (BATS) code is a promising technology for reliable end-to-end transmission in multi-hop wireless networks. One main research topic for BATS code is how to design an optimal inner code that is typically random linear network code. In this paper, this issue is focus on the number of transmissions from an end-to-end perspective. The problem is formulated as a mixed integer nonlinear programming (MINLP) problem with the objective of minimizing the total number of transmissions from source to destination. Subsequently, the inherent properties of inner codes are exploited to relax the integer restrictions by the means of the regularized incomplete beta function. As a result, a new nonlinear programming (NLP) problem is constructed. Solving the NLP problem provides a valid lower bound on the optimal solution, and, hence, is used as the performance measure for our heuristic. Furthermore, a centralized approximation approach is developed to solve our MINLP problem efficiently. The numerical results demonstrate that all solutions developed in the paper are near-optimal with a guaranteed performance bound.

关键词： Network coding Batched sparse codes mixed integer nonlinear programming

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Model-based optimal load control of inverter-driven air conditioners responding to dynamic electricity pricing 9th

Model-based optimal load control of inverter-driven air cond...

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9th International Conference on Applied Energy (ICAE)

作者： Hu, Maomao Xiao, Fu Hong Kong Polytech Univ Dept Bldg Serv Engn Kowloon Hong Kong Peoples R China

Dynamic electricity pricing provides a great opportunity for residential consumers to participate into demand response (DR) programs to reduce the electricity bills. The lack of automatic response to time-varying electricity prices is one of the challenges faced by the residential electric appliances. Most of the existing studies on DR of residential air conditioner (ACs) focus on the single-speed ACs, rather than the inverter-driven ACs which are more energy efficient and extensively installed in today's residential buildings. This paper presents a novel model-based optimal load scheduling method for residential inverter-driven ACs to realize automatic DR to the day-ahead dynamic electricity prices. The models of the inverter-driven ACs and room thermal dynamics are firstly developed, identified and integrated for the development of the model-based scheduling. The tradeoff problem between the electricity costs, resident's comfort and peak power reductions is formulated as a mixed-integer nonlinear programming problem with adjustable weightings. The optimal solution of the nonlinear programming problem is searched by the genetic algorithm (GA). Simulation results show that multiple goals can be achieved via GA optimization and the regulations of the weights in the objective function. The developed framework can be implemented in the programmable communicating thermostats (PCTs) or the smart home energy manage systems (HEMSs) to enable residential inverter-driven ACs automatically respond to day-ahead dynamic electricity pricing. (C) 2017 The Authors. Published by Elsevier Ltd.

关键词： Inverter-driven air conditioners Demand response Dynamic electricity pricing Genetic algorithm mixed integer nonlinear programming

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Optimal Placement of SVC Based on Line Flow Base Equation Using mixed integer nonlinear programming

Optimal Placement of SVC Based on Line Flow Base Equation Us...

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Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Etemad, Reza Navabi, Reza Shayanfar, Heidar Ali Iran Univ Sci & Technol Dept Elect Engn Tehran Iran Iran Univ Sci & Technol Ctr Excellence Power Syst Automat & Operat Dept Elect Engn Tehran Iran

ISBN: (纸本)9781424448135

In this paper a novel approach to determine optimal location, number and initial compensation level of the SVC in a power system is presented. mixed integer nonlinear programming (MINLP) is used for this purpose as a useful technique for combinatorial optimization over integers and variables. This technique can provide a fast approach as well as high computational efficiency even in solving complex optimization problems. Previous works used DC power flow that has well known limitations such as reactive power balance and reactive power loss ignorance. This paper uses novel Line Flow Based (LFB) equation that employs line power flow (instead of line current) and square of the bus voltages as variables. Since control variables such as line active and reactive power flow figure directly in formulation, handling the control action of the Flexible Alternating Current Transmission Systems (FACTS) devices is directly and easily implemented. The objectives of this paper are increasing system loadability and decreasing power losses and VAr generation cost by finding the optimal location, number and initial setting of the SVCs.

关键词： FACTS allocation SVC mixed integer nonlinear programming Line Flow Based Equations

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Model-based optimal load control of inverter-driven air conditioners responding to dynamic electricity pricing

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Energy Procedia 2017年 142卷 1953-1959页

作者： Maomao Hu Fu Xiao Department of Building Services Engineering The Hong Kong Polytechnic University Kowloon Hong Kong

Dynamic electricity pricing provides a great opportunity for residential consumers to participate into demand response (DR) programs to reduce the electricity bills. The lack of automatic response to time-varying electricity prices is one of the challenges faced by the residential electric appliances. Most of the existing studies on DR of residential air conditioner (ACs) focus on the single-speed ACs, rather than the inverter-driven ACs which are more energy efficient and extensively installed in today’s residential buildings. This paper presents a novel model-based optimal load scheduling method for residential inverter-driven ACs to realize automatic DR to the day-ahead dynamic electricity prices. The models of the inverter-driven ACs and room thermal dynamics are firstly developed, identified and integrated for the development of the model-based scheduling. The tradeoff problem between the electricity costs, resident’s comfort and peak power reductions is formulated as a mixed-integer nonlinear programming problem with adjustable weightings. The optimal solution of the nonlinear programming problem is searched by the genetic algorithm (GA). Simulation results show that multiple goals can be achieved via GA optimization and the regulations of the weights in the objective function. The developed framework can be implemented in the programmable communicating thermostats (PCTs) or the smart home energy manage systems (HEMSs) to enable residential inverter-driven ACs automatically respond to day-ahead dynamic electricity pricing.

关键词： Inverter-driven air conditioners Demand response Dynamic electricity pricing Genetic algorithm mixed integer nonlinear programming

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Resource allocation in uplink wireless multi-cell OFDMA networks

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COMPUTER STANDARDS & INTERFACES 2016年 44卷 274-289页

作者： Adasme, Pablo Leung, Janny Lisser, Abdel Univ Santiago Chile Dept Ingn Elect Santiago Chile Chinese Univ Hong Kong Dept Syst Engn & Engn Management Shatin Hong Kong Peoples R China Univ Paris 11 Lab Rech Informat F-91190 Orsay France

We propose resource allocation models for wireless multi-cell orthogonal frequency division multiple access (OFDMA) networks. The models-maximize signal to interference noise ratio (SINR) and capacity with SINR produced in an OFDMA network subject to user power and subcarrier assignment constraints. We derive mixed integer programming formulations for the case when maximizing SINR and piecewise linear approximations for the capacity objective. A variable neighborhood search (VNS) metaheuristic is proposed to compute near optimal solutions. Our numerical results indicate that VNS provides near optimal solutions and better feasible solutions than CPLEX and DICOPT solvers in significantly less computational cost. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Wireless multi-cell OFDMA networks mixed integer linear programming mixed integer nonlinear programming Resource allocation Variable neighborhood search

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Solving Multistage mixed nonlinear Convex Stochastic Problems

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INFORMATICA 2016年第4期27卷 799-818页

作者： Mijangos, Eugenio Univ Basque Country UPV EHU Dept Appl Math & Stat & Operat Res Math POB 644 Leioa Bizkaia Spain

We present an algorithm to solve multistage stochastic convex problems, whose objective function and constraints are nonlinear. It is based on the twin-node-family concept involved in the Branch-and-Fix Coordination method. These problems have 0-1 mixed-integer and continuous variables in all the stages. The non-anticipativity constraints are satisfied by means of the twin-node family strategy. In this work to solve each nonlinear convex subproblem at each node we propose the solution of sequences of quadratic subproblems. Due to the convexity of the constraints we can approximate them by means of outer approximations. These methods have been implemented in C++ with the help of CPLEX 12.1, which only solves the quadratic approximations. The test problems have been randomly generated by using a C++ code developed by this author. Numerical experiments have been performed and its efficiency has been compared with that of a well-known code. Key words: stochastic programming, convex programming, branch and fix coordination, mixed integer nonlinear programming, quadratic programming, outer approximation.

关键词： stochastic programming convex programming branch and fix coordination mixed integer nonlinear programming quadratic programming outer approximation

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A hybrid imperialist competitive algorithm for solving economic lot and delivery scheduling problem in a four-lestage supply chain

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ADVANCES IN MECHANICAL ENGINEERING 2017年第2期9卷

作者： Kia, Hamidreza Ghodsypour, Seyed Hassan Davoudpour, Hamid Amirkabir Univ Technol Dept Ind Engn & Management Syst Tehran Iran

In this article, we study the economic lot and delivery scheduling problem for a four-stage supply chain that includes suppliers, fabricators, assemblers, and retailers. All of the parameters such as demand rate are deterministic and production setup times are sequence-dependent. The common cycle time and integer multipliers policies are adapted as replenishment policies for synchronization throughout the supply chain. A new mixed integer nonlinear programming model is developed for both policies, the objective of which is the minimization of inventory, transportation, and production setup costs. We propose a new hybrid algorithm including a modified imperialist competitive algorithm which is purposed to the assimilation policy of imperialist competitive algorithm and teaching learning-based optimization which is added to improve local search. A hybrid modified imperialist competitive algorithm and teaching learning-based optimization is applied to find a near-optimum solution of mixed integer nonlinear programming in large-sized problems. The results denoted that our proposed algorithm can solve different size of problem in reasonable time. This procedure showed its efficiency in medium-and large-sized problems as compared to imperialist competitive algorithm, modified imperialist competitive algorithm, and other methods reported in the literature.

关键词： Supply chain management mixed integer nonlinear programming scheduling coordination imperialist competitive algorithm teaching learning-based optimization

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Optimization of an orbital long-duration rendezvous mission

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AEROSPACE SCIENCE AND TECHNOLOGY 2016年 58卷 482-489页

作者： Zhang, Jin Tang, Guo-jin Luo, Ya-zhong Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha 410073 Hunan Peoples R China

The phasing segment of the rendezvous mission between a cargo spacecraft and a space station usually lasts for several weeks, and actually presents an orbital long-duration problem. In this study, this orbital long-duration problem is formulated as a mixed integer nonlinear programming (MINLP) problem in which the maneuver revolution numbers (integers), maneuver arguments of latitude and impulse magnitude are used as design variables at the same time. A hybrid approach is then proposed to solve this MINLP problem. First, a linear dynamics model considering the J(2) term of the Earth non-spherical gravity is employed to formulate an approximate phasing problem, which is optimized using a genetic algorithm. Second, a shooting iteration process considering the coupling effect between the in-plane and out-of-plane maneuvers is proposed to improve the approximate solution to satisfy the terminal conditions of the high-precision problem. The proposed approach is demonstrated for a typical two-week rendezvous phasing mission. The results show that the proposed approach can stably obtain the near optimal high-precision solution by integrating the perturbed trajectory only a few times. Furthermore, a long-duration rendezvous phasing plan is compatible with any initial phase angles that the in-plane velocity increment remains almost unchanged when the initial phase angle changes. However, under the same conditions, the out-of-plane velocity increment has considerable variations. Compared with a two-day rendezvous phasing plan, a two-week plan could have several successive coplanar launch opportunities for the chaser by aiming different terminal revolution numbers. (C) 2016 Elsevier Masson SAS. All rights reserved.

关键词： Orbital rendezvous Long-duration maneuvers mixed integer nonlinear programming Engineering optimization

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