Uncertain multi-objective optimal model of oilfield development planning and its algorithm

In this paper, we discuss the formulation of the oilfield development plan in case of significant nondeterminacy in oilfield development. And the plan needs to ensure production and achieve minimum cost and maximum new recoverable reserves. The uncertain factors in oilfield development are analyzed...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Journal of ambient intelligence and humanized computing Ročník 8; číslo 5; s. 769 - 779
Hlavní autori: Ji, Xiaoyu, Yan, Sen, Feng, Siyu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2017
Springer Nature B.V
Predmet:
Artificial Intelligence
Computational Intelligence
Decision making
Engineering
Expected values
Genetic algorithms
Linear programming
Mathematical programming
Minimum cost
Multiple objective analysis
Oil field equipment
Oil fields
Oil recovery
Optimization
Original Research
Petroleum production
Probability distribution
Production planning
Reserves
Robotics and Automation
Sorting algorithms
User Interfaces and Human Computer Interaction
Workloads
Uncertain multi-objective programming
Multi-objective genetic algorithm
Oilfield development planning
Uncertain programming
ISSN:1868-5137, 1868-5145
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:In this paper, we discuss the formulation of the oilfield development plan in case of significant nondeterminacy in oilfield development. And the plan needs to ensure production and achieve minimum cost and maximum new recoverable reserves. The uncertain factors in oilfield development are analyzed in this paper, and we consider the uncertain nature of the stimulation effect of measures and new recoverable reserves per well and quantify them. On this basis, an uncertain multi-objective optimal model of oilfield development planning is constructed. The model aims to minimize the expectation of development cost and maximize the expectation of new recoverable reserves, and optimizes the workload of each measure under the constraints including the oil production and the resources limitation. Based on uncertainty theory, the model is transformed into a deterministic model. And a nondominated sorting genetic algorithm with elite strategy is developed to solve the model and get the Pareto solution set. Then the multi-attribute decision-making is applied to select the multiple development plans, which provides the basis for the decision-making of the oilfield development plan. Finally, a numerical example is given to verify the effectiveness of the model and algorithm, and their practical application values under the background of oilfield development planning.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1868-5137
1868-5145
DOI:10.1007/s12652-017-0530-4