基于混合粒子群算法的煤层气井位优化方法

doi:10.12108/yxyqc.20200616

岩性油气藏 ›› 2020, Vol. 32 ›› Issue (6): 164–171.doi: 10.12108/yxyqc.20200616

基于混合粒子群算法的煤层气井位优化方法

刘明明¹, 王全², 马收¹, 田中政¹, 丛颜¹

1. 华美孚泰油气增产技术服务有限责任公司, 北京 100101;
2. 石化盈科信息技术有限责任公司, 北京 100007

收稿日期:2019-12-13 修回日期:2020-02-12 出版日期:2020-12-01 发布日期:2020-10-30
第一作者:刘明明(1989-),男,硕士,主要从事油气田增产技术方面的研究工作。地址:(100101)北京市朝阳区北辰西路8号北辰世纪中心A座805室。Email:upc_lmm@163.com。

Well placement optimization of coalbed methane based on hybrid particle swarm optimization algorithm

LIU Mingming¹, WANG Quan², MA Shou¹, TIAN Zhongzheng¹, CONG Yan¹

1. SinoFTS Petroleum Services Ltd., Beijing 100101, China;
2. Petro-CyberWorks Information Technology Co., Ltd., Beijing 100007, China

Received:2019-12-13 Revised:2020-02-12 Online:2020-12-01 Published:2020-10-30

摘要/Abstract

摘要： 井位的部署直接关系到煤层气开发的采气速度及经济效益。基于粒子群算法的劣势和模拟退火算法的优势，提出了一种混合粒子群算法，其以净现值为目标函数，单井控制面积和井位为变量，结合油藏数值模拟方法，优选出净现值最大的单井控制面积和井位，并利用matlab编程来实现。结果显示，基于混合粒子群算法的井位优化方法能够快速确定最优井位，计算量较穷举法大幅度降低；沁水盆地煤层气田的最优单井控制面积为0.2 km²；对于最优单井控制面积，混合粒子群算法得到的最优净现值比常规矩形井网的净现值增加12.55%；最优井位分布与含气量、渗透率密切相关，其中渗透率的影响尤为重要，最优井位是含气量与渗透率的最优组合。该研究成果为煤层气开发井位优化提供了新方法。

关键词: 煤层气, 井位优化, 单井控制面积, 混合粒子群算法, 数值模拟, 沁水盆地

Abstract: The well locations directly influence the gas recovery factor and the economic benefit of coalbed methane development projects. Based on the disadvantage of particle swarm optimization and the advantage of simulated anneal algorithm,a hybrid particle swarm optimization algorithm was proposed. This algorithm took the net present value as the objective function,the single well control area and well location as variables,and combined the reservoir numerical simulation method to optimize the single well control area and well location with the largest net present value(NPV),which was realized by matlab programming. The results show that the well location optimization based on hybrid particle swarm optimization algorithm overcomes the disadvantage of the conventional well pattern which is experience-dependent. It needs less amount of computation to determine the optimal well location compared with exhaust algorithm. The optimal single well control area of coalbed gas field in Qinshui Basin is 0.2 km². For the optimal control area of a single well,the optimal NPV obtained by hybrid particle swarm optimization algorithm is 12.55% higher than that of the conventional rectangular well pattern. The optimal well location is closely related to gas content and permeability,among which the permeability distribution is particularly important,and it is an optimal combination of gas content and permeability. The research results provide a new method for the well location optimization of CBM development.

Key words: coalbed methane, well location optimization, single well control area, hybrid particle swarm optimization algorithm, numerical simulation, Qinshui Basin

中图分类号:

TE32+4

刘明明, 王全, 马收, 田中政, 丛颜. 基于混合粒子群算法的煤层气井位优化方法[J]. 岩性油气藏, 2020, 32(6): 164–171.

LIU Mingming, WANG Quan, MA Shou, TIAN Zhongzheng, CONG Yan. Well placement optimization of coalbed methane based on hybrid particle swarm optimization algorithm[J]. Lithologic Reservoirs, 2020, 32(6): 164–171.

参考文献

[1] 杨甫, 贺丹, 马东民, 等.低阶煤储层微观孔隙结构多尺度联合表征.岩性油气藏, 2020, 32(3):14-23. YANG F, HE D, MA D M, et al. Multi-scale joint characterization of micro-pore structure of low-rank coal reservoir. Lithologic Reservoirs, 2020, 32(3):14-23.
[2] 未志杰, 康晓东, 刘玉洋, 等.煤层气藏全流固耦合数学模型. 岩性油气藏, 2019, 31(2):151-158. WEI Z J, KANG X D, LIU Y Y, et al. A fully coupled fluid flow and geomechanics model for coalbed methane reservoir. Lithologic Reservoirs, 2019, 31(2):151-158.
[3] 艾林, 周明顺, 张杰, 等.基于煤岩脆性指数的煤体结构测井定量判识.岩性油气藏, 2017, 29(2):139-144. AI L, ZHOU M S, ZHANG J, et al. Quantitative identification of coal structure based on coal rock brittleness index by logging data. Lithologic Reservoirs, 2017, 29(2):139-144.
[4] 苏朋辉, 夏朝辉, 刘玲莉, 等.澳大利亚M区块低煤阶煤层气井产能主控因素及合理开发方式.岩性油气藏, 2019, 31(5):121-128. SU P H, XIA Z H, LIU L L, et al. Main controlling factors of productivity and reasonable development methods of low-rank coalbed methane in block M of Australia. Lithologic Reservoirs, 2019, 31(5):121-128.
[5] 高为, 金军, 易同生, 等.黔北小林华矿区高阶煤层气藏特征及开采技术.岩性油气藏, 2017, 29(5):140-147. GAO W, JIN J, YI T S, et al. Enrichment mechanism and mining technology of high rank coalbed methane in Xiaolinhua coal mine, northern Guizhou. Lithologic Reservoirs, 2017, 29(5):140-147.
[6] 倪小明, 王延斌, 接铭训, 等.晋城矿区西部地质构造与煤层气井网布置关系.煤炭学报, 2007, 32(2):146-149. NI X M, WANG Y B, JIE M X, et al. The relations between geological structure in the western Jincheng diggings and coal-bed methane wells arrangement. Journal of China Coal Society, 2007, 32(2):146-149.
[7] 杨秀春, 叶建平.煤层气开发井网部署与优化方法.中国煤层气, 2008, 5(1):13-17. YANG X C, YE J P. Well pattern optimization design for CBM development. China Coalbed Methane, 2008, 5(1):13-17.
[8] 史进, 吴晓东, 韩国庆, 等.煤层气开发井网优化设计.煤田地质与勘探, 2011, 39(6):20-23. SHI J, WU X D, HAN G Q, et al. Optimization design of CBM well grid pattern. Coal Geology & Exploration, 2011, 39(6):20-23.
[9] 张双斌, 苏现波, 郭红玉, 等.煤层气井排采过程中压裂裂缝导流能力的伤害与控制.煤炭学报, 2014, 39(1):124-128. ZHANG S B, SU X B, GUO H Y, et al. Controlling the damage of conductivity of hydraulic factures during the process of drainage in coalbed methane well. Journal of China Coal Society, 2014, 39(1):124-128.
[10] BECKNER B L, SONG X. Field development planning using simulated annealing:Optimal economic well scheduling and placement. SPE 30650, 1995.
[11] NORRENA K P, DEUTSCH C V. Automatic determination of well placement subject to geostatistical and economic constraints. SPE 78996, 2002.
[12] ONWUNALU J E, DURLOFSKY L J. Application of a particle swarm optimization algorithm for determining optimum well location and type. Computational Geosciences, 2010, 14(1):183-198.
[13] ONWUNALU J E, DURLOFSKY L J. A new well-pattern-optimization procedure for large-scale field development. SPE Journal, 2011, 16(3):594-607.
[14] 姜瑞忠, 刘明明, 徐建春, 等.遗传算法在苏里格气田井位优化中的应用.天然气地球科学, 2014, 25(10):1603-1609. JIANG R Z, LIU M M, XU J C, et al. Application of genetic algorithm for well placement optimization in Sulige gas field. Natural Gas Geoscience, 2014, 25(10):1603-1609.
[15] 姜瑞忠, 杨宜渤.基于新型遗传算法的碳酸盐岩油气藏布井研究.计算机科学, 2018, 45(11 A):584-586. JIANG R Z, YANG Y B. Research on well distribution in carbonate reservoirs based on novel genetic algorithm. Computer Science, 2018, 45(11 A):584-586.
[16] KENNEDY J, EBERHART R. Particle swarm optimization. Proceedings of IEEE international conference on neural networks, 1995, 4(2):1942-1948.
[17] METROPOLIS N, ROSENBLUTH A W, ROSENBLUTH M N, et al. Equation of state calculations by fast computing machines. The Journal of Chemical Physics, 1953, 21:1087-1092.
[18] KIRKPATRICK S, GELATT C D, VECCHI M P. Optimization by simulated annealing. Science, 1983, 42(3):671-680.
[19] BEHNAMIAN J, GHOMI S M T F. Development of a PSO-SA hybrid metaheuristic for a new comprehensive regression model to time-series forecasting. Expert Systems with Applications, 2010, 37(2):974-984.
[20] NIKNAM T, AMIRI B, OLAMAEI J, et al. An efficient hybrid evolutionary optimization algorithm based on PSO and SA for clustering. Journal of Zhejiang University(Science A), 2009, 10(4):512-519.
[21] HADIDI A, KAVEH A, AZAR B F, et al. An efficient hybrid algorithm based on particle swarm and simulated annealing for optimal design of space trusses. International Journal of Optimization in Civil Engineering, 2011, 1(3):377-395.
[22] VAN LAARHOVEN P J M, AARTS E H L. Simulated annealing:Theory and applications. Dordrecht:Springer, 1987:7-15.
[23] Al-MUDHAFER W J. A practical economic optimization approach with reservoir flow simulation for infill drilling in a mature oil field. SPE 164612, 2013:1-14.
[24] 张树林, 黄耀琴.净现值法:一种计算经济极限井网密度的新方法.地质科技情报, 2004, 23(1):78-80. ZHANG S L, HUANG Y Q. Net present value method:a new method to calculate economy limit well density. Geological Science and Technology Information, 2004, 23(1):78-80.

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基于混合粒子群算法的煤层气井位优化方法

Well placement optimization of coalbed methane based on hybrid particle swarm optimization algorithm

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