Influencing Factors and Prediction Methods for Production of Tight Oil Reservoir in Pingbei Oilfield

doi:10.7657/XJPG20220313

Abstract

Abstract:

Compared with conventional oil reservoirs, tight oil reservoirs have poor physical properties and low permeability, and wells drilled in these reservoirs need to be fractured for more production. Due to the geological features and special development techniques, there are many factors affecting the production of these reservoirs, and the simple analogy method commonly used on site for production prediction cannot meet the actual needs. In order to solve this problem, taking the tight oil reservoir in Pingbei oilfield of Ordos basin as the research object and based on the Darcy equation, the main influencing factors for production were quantitatively described through grey theoretical analysis. Moreover, a mathematical model was established by using the multiple regression method, and applied to predict the production of new wells in order to verify the reliability of the model. Production prediction by using the multidisciplinary method that combines the grey theory and multiple regression is more scientific and accurate than by using traditional methods, and it can provide a reference for the development of similar oil reservoirs.

Key words: Ordos basin, Pingbei oilfield, tight oil reservoir, influencing factor, quantitative nalysis, grey theory, production prediction, multiple regression

CLC Number:

TE328

HU Xinling, WANG Jian, PAN Lin. Influencing Factors and Prediction Methods for Production of Tight Oil Reservoir in Pingbei Oilfield[J]. Xinjiang Petroleum Geology, 2022, 43(3): 346-353.

Figures/Tables 9

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References 32

[1]	高玉甫. 陕北低压低渗透油田开发实践[M]. 北京: 中国石化出版社, 2004.
	GAO Yufu. Development practice of low-pressure and low-permeability oilfields in northern Shaanxi[M]. Beijing: China Petrochemical Press, 2004.
[2]	杨俊杰. 鄂尔多斯盆地构造演化与油气分布规律[M]. 北京: 石油工业出版社, 2002.
	YANG Junjie. Structural evolution and distribution of oil and gas in the Ordos basin[M]. Beijing: Petroleum Industry Press, 2002.
[3]	毛志强, 李进福. 油气层产能预测方法及模型[J]. 石油学报, 2000,21(5):58-61.
	MAO Zhiqiang, LI Jinfu. Method and models for productivity prediction of hydrocarbon reservoirs[J]. Acta Petrolei Sinica, 2000,21(5):58-61.
[4]	赵军, 郑立会. 数值模拟法预测单井初始产量的研究与应用[J]. 油气井测试, 2004,13(3):13-15.
	ZHAO Jun, ZHENG Lihui. The study and application for forecasting individual well initial production by numerical simulation[J]. Well Testing, 2004,13(3):13-15.
[5]	蒲谢洋, 胡永全, 赵金洲, 等. 致密油藏缝网压裂产量数值模拟[J]. 大庆石油地质与开发, 2015,34(6):136-140.
	PU Xieyang, HU Yongquan, ZHAO Jinzhou, et al. Numerical simulation of the network fracturing production for tight oil reservoirs[J]. Petroleum Geology & Oilfield Development in Daqing, 2015,34(6):136-140.
[6]	周彩兰, 刘敏. BP神经网络在石油产量预测中的应用[J]. 武汉理工大学学报, 2009,31(3):125-129.
	ZHOU Cailan, LIU Min. Application research on oil production forecasting based on BP neural network[J]. Journal of Wuhan University of Technology, 2009,31(3):125-129.
[7]	吴新根, 葛家理. 应用人工神经网络预测油田产量[J]. 石油勘探与开发, 1994,21(3):75-78.
	WU Xingen, GE Jiali. The application of artificial neural network in predicting output of oil fields[J]. Petroleum Exploration and Development, 1994,21(3):75-78.
[8]	BROCKWELL P J, DAVIS R A. Introduction to time series and forecasting[M]. Switzerland:Springer, 2016.
[9]	樊灵, 赵孟孟, 殷川, 等. 基于BP神经网络的油田生产动态分析方法[J]. 断块油气田, 2013,20(2):204-206.
	FAN Ling, ZHAO Mengmeng, YIN Chuan, et al. Oilfield production dynamic analysis method based on BP neural network[J]. Fault-Block Oil & Gas Field, 2013,20(2):204-206.
[10]	FULFORD D S, BOWIE B, BERRY M E, et al. Machine learning as a reliable technology for evaluating time/rate performance of unconventional wells[J]. SPE Economics & Management, 2016,8(1):23-39.
[11]	刘伟, 刘伟德, 顾杰. 基于机器学习方法的子层吸水率预测模型[J]. 石油科学与工程, 2019,182(10):63-67.
	LIU Wei, LIU Weide, GU Jie. Predictive model for water absorption in sublayers using a machine learning method[J]. Journal of Petroleum Science and Engineering, 2019,182(10):63-67.
[12]	邓聚龙. 灰色系统理论教程[M]. 武汉: 华中理工大学出版社, 1990.
	DENG Julong. Grey system theory course[M]. Wuhan: Huazhong University of Science and Technology Press, 1990.
[13]	刘思峰, 谢乃明. 灰色系统理论及其应用[M]. 北京: 北京科学出版社, 2010.
	LIU Sifeng, XIE Naiming. Grey system theory and its application[M]. Beijing: Beijing Science Press, 2010.
[14]	陈欢庆, 丁超, 杜宜静, 等. 储层评价研究进展[J]. 地质科技情报, 2015,34(5):66-74.
	CHEN Huanqing, DING Chao, DU Yijing, et al. Advances of reservoir evaluation researches[J]. Geological Science and Technology Information, 2015,34(5):66-74.
[15]	杨振周, 丁云宏, 蒋延学, 等. 灰色拓扑法在压裂井产量预测中的应用[J]. 石油学报, 2003,24(6):69-72.
	YANG Zhenzhou, DING Yunhong, JIANG Tingxue, et al. Application of the grey topology method to oil production prediction of fractured well[J]. Acta Petrolei Sinica, 2003,24(6):69-72.
[16]	刘秀婷, 杨军, 程仲平, 等. 油田产量预测的新方法及其应用[J]. 石油勘探与开发, 2002,29(4):74-76.
	LIU Xiuting, YANG Jun, CHENG Zhongping, et al. A new method of predicting oilfield output and its application[J]. Petroleum Exploration and Development, 2002,29(4):74-76.
[17]	龙昱. 灰色系统理论在地质研究中应用的探讨[J]. 地质科技情报, 1988,7(1):108-109.
	LONG Yu. Discussion on the application of grey system theory in geological research[J]. Geological Science and Technology Information, 1988,7(1):108-109.
[18]	孙敬, 刘德华, 张亮, 等. 低渗透油藏递减影响因素的灰色关联分析[J]. 特种油气藏, 2012,19(2):90-93.
	SUN Jing, LIU Dehua, ZHANG Liang, et al. Grey correlation analysis of the influencing factors on production decline in low permeability reservoirs[J]. Special Oil & Gas Reservoirs, 2012,19(2):90-93.
[19]	林德纯. 灰色系统在预测递减阶段气井产量中的应用[J]. 重庆科技学院学报, 2011,13(4):56-58.
	LIN Dechun. Application of grey mode in gas production prediction of decline stage[J]. Journal of Chongqing University of Science and Technology, 2011,13(4):56-58.
[20]	刘凤伟, 谢传礼, 刘超, 等. 基于灰色关联分析法的致密油储层定量评价[J]. 地质科技情报, 2016,35(5):168-175.
	LIU Fengwei, XIE Chuanli, LIU Chao, et al. Tight oil reservoir quantitative evaluation based on gray correlation analysis method[J]. Geological Science and Technology Information, 2016,35(5):168-175.
[21]	李智超, 赵正文, 钟仪华, 等. 小波神经网络在油田产量预测中的应用[J]. 大庆石油地质与开发, 2008,27(6):52-54.
	LI Zhichao, ZHAO Zhengwen, ZHONG Yihua, et al. Application of wavelet neural network in oilfield production prediction[J]. Petroleum Geology & Oilfield Development in Daqing, 2008,27(6):52-54.
[22]	刘巍, 刘威, 谷建伟. 基于机器学习方法的油井日产油量预测[J]. 石油钻采工艺, 2020,42(1):70-75.
	LIU Wei, LIU Wei, GU Jianwei. Oil production prediction based on a machine learning method[J]. Oil Drilling & Production Technology, 2020,42(1):70-75.
[23]	黎盼, 孙卫, 高永利, 等. 致密砂岩储层差异性成岩演化对孔隙度定量演化表征影响:以鄂尔多斯盆地马岭地区长8₁储层为例[J]. 地质科技情报, 2018,37(1):135-142.
	LI Pan, SUN Wei, GAO Yongli, et al. Quantitative calculation of tight sandstone reservoir porosity evolution based on different diagenesis:a case study from Chang 8₁ reservoir of Maling area,Ordos basin[J]. Geological Science and Technology Information, 2018,37(1):135-142.
[24]	王美石, 陈祥光. 多元线性回归方法在油田产量预测中的应用[J]. 油气田地面工程, 2004,23(11):25-26.
	WANG Meishi, CHEN Xiangguang. Application of multiple linear regression method in oilfield production prediction[J]. Oil-Gasfield Surface Engineering, 2004,23 (11):25-26.
[25]	宛利红, 刘波涛, 王新海, 等. 致密油藏多元回归产能预测方法研究与应用[J]. 油气井测试, 2015,24(1):17-19.
	WAN Lihong, LIU Botao, WANG Xinhai, et al. Study on multiple regression analysis in prediction of tight oil reservoir and its application[J]. Well Testing, 2015,24(1):17-19.
[26]	曲占庆, 黄德胜, 毛登周, 等. 基于灰色关联法的低渗气藏压裂效果影响因素分析[J]. 西北大学学报(自然科学版), 2014,44(4):603-606.
	QU Zhanqing, HUANG Desheng, MAO Dengzhou, et al. The influencing factors of fracturing effects in low permeability gas reservoir with gray correlation method[J]. Journal of Northwest University(Natural Science Edition), 2014,44(4):603-606.
[27]	张新顺, 王红军, 马锋, 等. 基于多元回归分析的致密油可采资源评价方法[J]. 石油与天然气地质, 2008,39(6):1 323-1 335.
	ZHANG Xinshun, WANG Hongjun, MA Feng, et al. Method to assess recoverable tight oil based on multiple regression analysis[J]. Oil & Gas Geology, 2008,39(6):1 323-1 335.
[28]	钟大康. 致密油储层微观特征及其形成机理:以鄂尔多斯盆地长6-长7段为例[J]. 石油与天然气地质, 2017,38(1):49-61.
	ZHONG Dakang. Micro-petrology,pore throat characteristics and genetic mechanism of tight oil reservoirs:a case from the 6th and 7th members of Triassic Yanchang formation in the Ordos basin[J]. Oil & Gas Geology, 2017,38(1):49-61.
[29]	王惠文, 孟洁. 多元线性回归的预测建模方法[J]. 北京航空航天大学学报, 2007,33(4):500-504.
	WANG Huiwen, MENG Jie. Predictive modeling on multivariate linear regression[J]. Journal of Beijing University of Aeronautics and Astronautics, 2007,33(4):500-504.
[30]	NATHANS L L, OSWALD F L, NIMON K, et al. Interpreting multiple linear regression:a guidebook of variable importance[J]. Practical Assessment,Research & Evaluation, 2012,17 (9):1-19.
[31]	陈永义, 俞小鼎, 高学浩, 等. 处理非线性分类和回归问题的一种新方法(Ⅰ):支持向量机方法简介[J]. 应用气象学报, 2004,15(3):345-354.
	CHEN Yongyi, YU Xiaoding, GAO Xuehao, et al. A new method for non-linear classify and non-linear regression Ⅰ:introduction to support vector machine[J]. Quarterly Journal of Applied Meteorology, 2004,15(3):345-354.
[32]	周芸, 周福建, 冯连勇, 等. 一种新型油气产量预测模型[J]. 大庆石油地质与开发, 2008,37(5):76-80.
	ZHOU Yun, ZHOU Fujian, FENG Lianyong, et al. A new model for predicting oil and gas production[J]. Petroleum Geology & Oilfield Development in Daqing, 2008,37(5):76-80.