为在一定投资规模和约束条件下优化气田开发顺序及气田产能规模,实现产品分成合同模式下多个气田整体开发经济效益最大化,结合国内外气田群的开发现状,利用定量关系快速准确描述气田生产动态,并将基于产品分成合同模式的净现值作为目标函数,建立了优化气田投产时间和产能规模的混合整数非线性规划模型。提出求解该模型的自适应分层嵌套遗传算法,对模型求解变量和约束条件进行处理,并对标准遗传算法的遗传结构、遗传算子和终止条件设定3方面进行改进,形成气田群整体高效开发建模和求解技术。将不采用数学模型优化的多方案对比法、罚函数处理约束条件的标准遗传算法,以及自适应分层嵌套遗传算法的优化结果进行对比。结果表明：优化模型建立准确,提出的求解方法在收敛性和计算速度方面表现良好,可实现气田群整体开发的有序接替。图4表4参17
To optimize production schedule and production plan of multiple gas fields with certain amount of investment and constraints and to maximize their economic benefits under the production sharing contact (PSC) mode, a quantitative relationship was applied to describe the production performance depending on the development status of multiple gas fields in China and abroad. Furthermore, with the PSC-based net present value (NPV) as the objective function, a mixed integer nonlinear programming model for gas fields with optimized production schedule and productivity was established. An adaptive layer-embedded genetic algorithm was proposed to solve this model. Through handling the variables and constraints for solving this model and improving the genetic structure, genetic operators and termination conditions of standard genetic algorithm, modeling and solving techniques were formed for integrated and efficient development of multiple gas fields. Results obtained by three methods, i.e. multi-scheme comparison without mathematical model, standard genetic algorithm which induces penalty function to treat constraints, and adaptive layer-embedded genetic algorithm, were compared. The proposed optimization model is accurate, and the proposed layer-embedded genetic algorithm provides satisfactory convergence and calculation rate, ensuring that multiple gas fields could be exploited orderly.
[1] ARONOFSKY J S, WILLIAMS A C. The use of linear programming and mathematical models in underground oil production[J]. Management Science, 1962, 8(4): 394-407.
[2] FRAIR L, DEVINE M. Economic optimization of offshore petroleum development[J]. Management Science, 1975, 21(12): 1370-1379.
[3] SULLIVAN J. A computer model for planning the development of an offshore gas field[J]. JPT, 1982, 34(7): 1555-1564.
[4] HARDING T J, RADCLIFFE N J, KING P R. Hydrocarbon production scheduling with genetic algorithms[J]. SPEJ, 1998, 3(2): 99-107.
[5] CULLICK A S, HEATH D, NARAYANAN K, et al. Optimizing multiple-field scheduling and production strategy with reduced risk[R]. SPE 84239, 2003.
[6] 郭春秋, 李方明, 刘合年, 等. 气藏采气速度与稳产期定量关系研究[J]. 石油学报, 2009, 30(6): 908-911.
GUO Chunqiu, LI Fangming, LIU Henian, et al. Analysis of quantitative relationship between gas offtake and plateau duration of natural gas reservoir[J]. Acta Petrolei Sinica, 2009, 30(6): 908-911.
[7] HOLLAND J H. Adaptation in natural and artificial systems[M]. Cambridge: MIT Press, 1992: 153-158.
[8] TALBI H, DRAA A, BATOUCHE M. A new quantum-inspired genetic algorithm for solving the travelling salesman problem[J]. IEEE International Conference on Industrial Technology, 2004, 3: 1192-1197.
[9] FICHTER D P. Application of genetic algorithms in portfolio optimization for the oil and gas industry[R]. SPE 62970, 2000.
[10] SARICH M D. Using genetic algorithms to improve investment decision making[R]. SPE 68725, 2001.
[11] MAN K F, TANG K S, KWONG S. Genetic algorithms: Concepts and applications[J]. IEEE Trans Industrial Electronics, 1996, 43: 519-534.
[12] 余一欣, 殷进垠, 郑俊章, 等. 阿姆河盆地成藏组合划分与资源潜力评价[J]. 石油勘探与开发, 2015, 42(6): 750-756.
YU Yixin, YIN Jinyin, ZHENG Junzhang, et al. Division and resources evaluation of hydrocarbon plays in the Amu Darya Basin, Central Asia[J]. Petroleum Exploration and Development, 2015, 42(6): 750-756.
[13] 王进财, 赵伦, 张祥忠, 等. 古潜山岩溶储集层特征及其对产能的控制作用[J]. 石油勘探与开发, 2015, 42(6): 779-786.
WANG Jincai, ZHAO Lun, ZHANG Xiangzhong, et al. Buried hill karst reservoirs and their controls on productivity[J]. Petroleum Exploration and Development, 2015, 42(6): 779-786.
[14] MICHALEWICZ Z. Genetic algorithms+data structures=evolution programs[M]. New York: Springer, 1996: 159-177.
[15] MICHALEWICZ Z, JANIKOW C Z, KRAWCZYK J B. A modified genetic algorithm for optimal control problems[J]. Computers & Mathematics with Application, 1992, 23(12): 83-94.
[16] GOLDBERG D E. Genetic algorithms in search, optimization and machine learning[M]. Boston: Addison-Wesley Professional, 1989: 1-83.
[17] DAVIS L. Applying adaptive algorithms to epistatic domains[J]. International Joint Conference on Artificial Intelligence, 1985, 1: 162-164.
