基于GMDH-RSM方法的非常规油气生产技术多指标组合对产能的影响效应研究

doi:10.16381/j.cnki.issn1003-207x.2019.1450

摘要/Abstract

摘要： 地质指标、技术指标与产能的关联分析对非常规油气生产科学决策具有重要指导意义。为了将单因素影响产能的数量关系拓展到指标组合取值与产能量化关系的研究层面，本文以致密气开发为例，从“开发全流程”中不同因素影响产能因果关系切入，就“技术指标-产能”因果链剖析可控技术因素的多指标组合情景与产能的量化关系，提出生产技术条件优化的方案。构建GMDH-RSM模型分析，首先给出产能关键影响因素选择的科学依据；其次对筛选的关键因素分类，提取可控类指标；最后分析可控类技术指标的组合取值情景与产能的量化影响形式与可视化结果。结果显示技术指标不同取值组合通过二阶方程式影响产能。通过技术指标不同取值组合的情景选择可以获得相应的技术产出效果，实现产能控制。本文结论明确了致密气开发的技术指标对产能影响的数量关系形式，为产能管理提供了决策的实证参考。

关键词: 非常规油气；产能管理；多指标组合；响应面分析

Abstract: Causal correlation analysis between geological factors, technical factors and capacity of oil and gas is of great significance for scientific decision-making in unconventional oil and gas production. However, the quantitative relationship between single factor and the capacity is mainly discussed, and the combined scenario effect of similar influencing factors is not deeply studied. Tight gas production is taken as an example. From the perspective of the causal relationship chain between production factors and production capacity, a quantitative relationship between the combination scenario of multiple factors of controllable technical factors and capacity is analyzed to achieve optimizing production technical conditions. GMDH-RSM Combined method is constructed. Firstly, under the method of Group Method of Data Handling (GMDH), the selection method of key factors affecting capacity is given in this paper. Secondly, the controllable factors in actual production decisions are found by classification of key indicators. Finally, empirical analysis is made by Response Surface Methodology (RSM) on the quantitative relationship between the combination of multiple technical factors and the production capacity. The results show that: (1) different values of combinations of technical factors affect production capacity significantly through second-order equations; (2) selecting scenarios with different values of technical factors, the corresponding level of technical output, i.e. production, can be obtained, so as to achieve capacity control.The conclusion of this paper clarifies the quantitative relationship form of the impact of technical factors accompanying tight gas development on production capacity and provides an empirical reference for the decision-making of production capacity management.

Key words: unconventional oil and gas; capacity management; multi-factor combination; response surface analysis

中图分类号:

王树斌,卢全莹,柴建. 基于GMDH-RSM方法的非常规油气生产技术多指标组合对产能的影响效应研究[J]. 中国管理科学, 2022, 30(6): 275-286.

WANG Shu-bin,LU Quan-ying,CHAI Jian. Research on the Effects of Technology Multi-index Combination on Unconventional Oil and Gas Production with GMDH-RSM Method[J]. Chinese Journal of Management Science, 2022, 30(6): 275-286.

参考文献

［1］雷群, 杨立峰, 段瑶瑶, 等. 非常规油气“缝控储量”改造优化设计技术［J］. 石油勘探与开发, 2018, 45(4): 719-726.Lei Qun, Yang Lifeng, Duan Yaoyao, et al. Optimization design technology for "fract-ure-controlled reserves" reconstruction of unconventional oil and gas［J］. Petroleum Exploration and Development, 2018, 45(4): 719-726.
［2］焦方正. 非常规油气之“非常规”再认识［J］. 石油勘探与开发, 2019, 46(5): 803-810.Jiao Fangzheng. A new understanding of unconventional oil and gas［J］. Petroleum Exploration and Development, 2019, 46(5): 803-810.
［3］ Sébastien C, Julien M, Yann G.Unconventional oil and gas in France: From popular distrust to politicization of the underground［J］. The Extractive Industries and Society,2018,5(4):682-690.
［4］ Frank M.Using a segregated flow model to forecast production of oil, gas, and water in shale oil plays［J］.Journal of Petroleum Science and Engineering,2019,180:48-61.
［5］ Matino I, Dettori S, Colla V, et al.Forecasting blast furnace gas production and demand through echo state neural network-based models: Pave the way to off-gas optimized management［J］.Applied Energy,2019,253:Article 113578.https://doi.org/10.1016/j.apenergy.2019.113578.
［6］ Wang Shuhua, Chen Zan,Chen Shengnan.Applicability of deep neural networks on production forecasting in Bakken shale reservoirs［J］.Journal of Petroleum Science and Engineering,179: 112-125.
［7］ MakindeI Lee W J.Principal components methodology-A novel approach to forecasting production from liquid-rich shale (LRS) reservoirs［J］.Petroleum,2019,5(3):227-242.
［8］ Kim G, Lee H, Chen Zhuoheng, et al.Effect of reservoir characteristics on the productivity and production forecasting of the Montney shale gas in Canada［J］.Journal of Petroleum Science and Engineering,2019,182,Article 106276.https://doi.org/10.1016/j.petrol.2019.106276.
［9］ Wang Jianliang, Mohr S, Feng Lianyong, et al. Analysis of resource potential for China’s unconventional gas and forecast for its long-term production growth［J］.Energy Policy,2016,88:389-401.
［10］吴妍, 魏巍贤, 蔡文香. 中国非常规油气资源发展对策研究［J］. 中国人口·资源与环境, 2015, 25(S2): 292-295.Wu Yan, Wei weixian, Cai wenxiang. Study on development strategy of unconventional oil and gas resources in China［J］. China Population, Resources and Environment, 2015, 25(S2): 292-295.
［11］邹才能, 杨智, 何东博, 等. 常规—非常规天然气理论、技术及前景［J］. 石油勘探与开发, 2018, 45(4): 575-587.Zou Caineng, Yang Zhi, He Dongbo, et al. Conventional and unconventional natural gas theory, technology and prospects［J］. Petroleum Exploration and Development, 2018, 45(4): 575-587.
［12］王树斌, 郭菊娥, 曹馨予, 等. 基于GMDH-Bayesian-SEM的非常规油气开发技术效应研究——以致密气生产数据为例［J］. 北京理工大学学报(社会科学版), 2017, 19(6): 49-57.Wang Shubin, Guo Jue, Cao xinyu, et al. Effect of unconventional oil and gas development technology based on GMDH-Bayesian SEM: A case study of tight gas production data［J］. Journal of Beijing Institute of Technology (Social Science Edition), 2017, 19(6): 49-57.
［13］李玉伟, 龙敏, 汤继周, 等. 考虑裂尖塑性区影响的水力压裂缝高计算模型［J］. 石油勘探与开发, 2020, 47(1): 175-185.Li Yuwei, Long Min, Tang Jizhou, et al. Hydraulic fracture height calculation model considering the influence of crack tip plastic zone［J］. Petroleum Exploration and Development, 2020, 47(1): 175-185.
［14］张金武, 王国勇, 何凯, 等. 苏里格气田老井侧钻水平井开发技术实践与认识［J］. 石油勘探与开发, 2019, 46(2): 370-377.Zhang Jinwu, Wang Guoyong, He Kai, et al. Practice and understanding of lateral drilling horizontal well development technology in Sulige Gas Field［J］. Petroleum Exploration and Development, 2019, 46(2): 370-377.
［15］王军磊, 贾爱林, 位云生, 等. 基于多井模型的压裂参数—开发井距系统优化［J］. 石油勘探与开发, 2019, 46(5): 981-992.Wang Junlei, Jia Ailin, Wei Yunsheng, et al. Optimization of fracturing parameters-development well spacing system based on multi-well model［J］. Petroleum Exploration and Development, 2019, 46(5): 981-992.
［16］ Solarin S A, Gil-Alana L A, Lafuente C. An investigation of long range reliance on shale oil and shale gas production in the U.S. market［J］. Energy,2020,195, Article 116933, https://doi.org/10.1016/j.energy.2020.116933.
［17］ Wang Hui，Chen Li, Qu Zhiguo, et al. Modeling of multi-scale transport phenomena in shale gas production-A critical review［J］. Applied Energy,2020,262, Article 114575, https://doi.org/10.1016/j.apenergy.2020.114575.
［18］ Wang Qiang, Li Shuyu, Li Rongrong, et al. Forecasting U.S. shale gas monthly production using a hybrid ARIMA and metabolic nonlinear grey model［J］. Energy,2018,160：378-387.
［19］杨福霞, 徐江川, 杨冕, 等. 能源价格波动、诱导性技术进步与中国环境全要素生产率［J］. 中国管理科学, 2018, 26(11): 31-41.Yang Fuxia, Xu Jiangchuan, Yang Mian, et al. Energy price fluctuation, induced technological progress and environmental total factor productivity in China［J］. Chinese Journal of Management Science, 2018, 26(11): 31-41.
［20］夏晖, 王思逸, 蔡强. 多目标条件下企业碳配额分配和政府公平——基于(p,α)比例公平的视角［J］. 中国管理科学, 2019, 27(4): 48-55.Xia Hui, Wang Simiao, Cai Qiang. Carbon quota allocation and government equity under multi-objective conditions: Based on (P,α) proportional equity perspective［J］. Chinese Journal of Management Science, 2019, 27(4): 48-55.
［21］刘晓燕, 吕涛. 考虑先期和当期资源投入的能源应急协同研究［J］. 中国管理科学, 2019, 27(3): 180-188.Liu Xiaoyan, Lv Tao. Collaborative research on energy emergency response considering upfront and current resource input［J］. Chinese Journal of Management Science, 2019, 27(3): 180-188.
［22］宋妍, 陈赛, 张明. 地方政府异质性与区域环境合作治理——基于中国式分权的演化博弈分析［J］. 中国管理科学, 2020, 28(1): 201-211.Song Yan, Chen Sai, Zhang Ming. Local government heterogeneity and regional environmental cooperative governance: An Evolutionary game analysis based on Chinese decentralization［J］. Chinese Journal of Management Science, 2020, 28(1): 201-211.
［23］陈星星. 非期望产出下我国能源消耗产出效率差异研究［J］. 中国管理科学, 2019, 27(8): 191-198.Chen Xingxing. Research on output efficiency difference of energy consumption in China under non-expected output［J］. Chinese Journal of Management Science, 2019, 27(8): 191-198.
［24］谭英双, 张雪清, 衡爱民. 需求不确定下企业生产规模选择及影响因素研究［J］. 中国管理科学, 2018, 26(8): 138-145.Tan Yingshuang, Zhang Xueqing, Heng Aimin. Research on production scale selection and influencing factors of enterprises with uncertain demand［J］. Chinese Journal of Management Science, 2018, 26(8): 138-145.
［25］肖进, 孙海燕, 刘敦虎, 等. 基于GMDH混合模型的能源消费量预测研究［J］. 中国管理科学, 2017, 25(12): 158-166.Xiao Jin, Sun Haiyan, Liu Dunhu, et al. Research on energy consumption forecast based on GMDH hybrid model［J］. Chinese Journal of Management Science, 2017, 25(12): 158-166.
［26］郭菊娥, 张剑如, 薛鹏, 等. 基于GMDH-SVR致密气开发过程参数优化［J］. 运筹与管理, 2018, 27(11): 10-16.Guo Jue, Zhang Jianru, Xue Peng. Optimization of tight gas development process parameters based on GMDH-SVR［J］. Operations Research and Management, 2018, 27(11): 10-16.
［27］ Box G E P, Wilson K B. Breakthroughs in Statistics: On the experimental attainment of optimum conditions ［M］. New York: Springer, 1992.
［28］ Hill W J, Hunter W G. A review of response surface methodology: A literature survey［J］. Technometrics, 1966, 8(4): 571-590.
［29］柏帅蛟, 井润田, 李璞,等. 匹配研究中使用响应面分析的方法［J］. 管理评论, 2018, 30(3): 161-170.Bai Shuaijiao, Jing Runtian, Li Pu, et al. Response surface analysis in matching research［J］. Management Review, 2018, 30(3): 161-170.
［30］张兰霞, 张靓婷, 朱坦. 领导—员工认知风格匹配对员工创造力与创新绩效的影响［J］. 南开管理评论, 2019, 22(2): 165-175.Zhang Lanxia, Zhang Liangting, Zhu Tan. The influence of leader-employee cognitive style matching on employee creativity and innovation performance［J］. Nankai Management Review, 2019, 22(2): 165-175.
［31］裴艳丽, 姜汉桥, 李俊键, 等. 页岩气新井压裂规模优化设计［J］. 断块油气田, 2016, 23(2): 265-268.Pei Yanli, Jiang Hanqiao, Li Junjian, et al. Optimization design of fracturing scale for new shale gas wells［J］. Fault-block Oil and Gas Fields, 2016, 23(2): 265-268.
［32］王涛, 颜明, 郭海波. 一种新的回归分析方法——响应曲面法在数值模拟研究中的应用［J］. 岩性油气藏, 2011, 2: 100-104.Wang Tao, Yan Ming, Guo Haibo. A new regression analysis method——application of response surface method in numerical simulation［J］. Lithologic Reservoir, 2011, 2: 100-104.

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