An MILP model and solution technique for the planning of infrastructure in offshore oilfields

Carvalho, M.C.A.; Pinto, José M.

doi:10.1016/j.petrol.2005.11.012

Cited by 44 publications

(29 citation statements)

References 8 publications

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“…[25][26][27][28][29] If the supply chain planning problem includes both strategic and operational decisions, bilevel decomposition 21 can be implemented to iteratively solve an upper level aggregated model and a lower level detailed model. [30][31][32] Hierarchical decomposition 22 is effective for supply chain optimizatoin problems with multiple levels of decision-making. [33][34][35][36] .…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Distribution-Inventory Planning of Industrial Gases. II. MINLP Models and Algorithms for Stochastic Cases

You

Pinto²,

Grossmann

et al. 2011

Ind. Eng. Chem. Res.

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In this paper, we address the optimization of industrial gas distribution systems, which consist of plants and customers, as well as storage tanks, trucks and trailers. A mixedinteger linear programming (MILP) model is presented to minimize the total capital and operating cost, and to integrate short-term distribution planning decisions for the vehicle routing with long-term inventory decisions for sizing storage tanks at customer locations. In order to optimize asset allocation in the industrial gas distribution network by incorporating operating decisions, the model also takes into account the synergies among delivery schedule, tank sizes, customer locations and inventory profiles. To effectively solve large scale instances, we propose two fast computational strategies. The first approach is a two-level solution strategy based on the decomposition of the full scale MILP model into an upper level route selectiontank sizing model and a lower level reduced routing model. The second approach is based on a continuous approximation method, which estimates the operational cost at the strategic level and determines the tradeoff with the capital cost from tank sizing.Three cases studies including instances with up to 200 customers are presented to illustrate the applications of the models and the performance of the proposed solution methods.

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Section: Literature Reviewmentioning

confidence: 99%

Optimal Distribution-Inventory Planning of Industrial Gases. II. MINLP Models and Algorithms for Stochastic Cases

You

Pinto²,

Grossmann

et al. 2011

Ind. Eng. Chem. Res.

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“…Chen and Feng [18] established a model of oilield measure program which is predicted by the BP network. Carvalho et al [19] established an MIP model to work out ofshore oilfield infrastructure planning and applied decomposition method to solve the model. Ge et al [20] investigated the drilling of cluster horizontal wells and set up a platform program ming model.…”

Section: Introductionmentioning

confidence: 99%

Optimal Planning for Deepwater Oilfield Development Under Uncertainties of Crude Oil Price and Reservoir

Zhang¹,

Liang²,

Ma³

et al. 2018

Recent Insights in Petroleum Science and Engineering

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The development planning of deepwater oilield directly inluences production costs and beneits. However, the uncertainties of crude oil price and reservoir and the special production requirements make it diicult to optimize development planning of deepwater oilield. Although there have been a number of scholars researching on this issue, previous models just focused on several special working conditions and few have considered energy supply of loating production storage and oloading (FPSO). In light of the normal deepwater production development cycles, in this paper, a multiscenario mixed integer linear programming (MS-MILP) method is proposed based on reservoir numerical simulation, considering the uncertainties of reservoir and crude oil price and the constraint of energy consumption of FPSO, to obtain the globally optimal development planning of deepwater oilield. Finally, a real example is taken as the study objective. Compared with previous researches, the method proposed in this paper is testiied to be practical and reliable.

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“…Some early examples, mainly focusing on the development of reservoirs and wells of single fields, are [5] and [6]. [1], [7], [8], and [9] take a network perspective coordinating multiple fields. Several papers also model the influence of uncertainty, see for instance [10] on market uncertainty and [11] on uncertainty in reservoir properties.…”

Section: Introductionmentioning

confidence: 99%

Optimization Model to Analyse Optimal Development of Natural Gas Fields and Infrastructure

2015

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We present an optimization model for analysis of system development for natural gas fields, processing and transport infrastructure. In this paper we present our experience from performing analyses for the natural gas industry with the optimization model. We also present a model extension in the form of continuous investment decisions. This extension allows the capacity in pipelines, processing facilities and compressors to be determined within a given range by the model. We also give a partial model description along with a case example that demonstrates the importance of using continuous investment decisions when considering design in natural gas systems.

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An MILP model and solution technique for the planning of infrastructure in offshore oilfields

Cited by 44 publications

References 8 publications

Optimal Distribution-Inventory Planning of Industrial Gases. II. MINLP Models and Algorithms for Stochastic Cases

Optimal Distribution-Inventory Planning of Industrial Gases. II. MINLP Models and Algorithms for Stochastic Cases

Optimal Planning for Deepwater Oilfield Development Under Uncertainties of Crude Oil Price and Reservoir

Optimization Model to Analyse Optimal Development of Natural Gas Fields and Infrastructure

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