油气勘探开发综合研究数字平台建设及应用

doi:10.13809/j.cnki.cn32-1825/te.2021.04.020

摘要/Abstract

摘要：

勘探开发综合研究流程节点多、专业性强,针对研究流程未能实现显性化、数据服务支持不足、可视化手段缺乏等问题,面向胜利油田勘探开发综合研究业务的需求,形成了标准化的勘探开发综合研究流程,研发了油气勘探开发综合研究数字平台,实现了研究数据快速获取、成果自动归档。集成专业软件数据服务、可视化分析支持手段,一键调用所需的专业软件,实现不同研究岗位多学科在线协同。通过标准规范的综合研究流程,便于新员工能快速进入角色,老员工的宝贵经验通过流程不断完善得以传承。该平台在胜利油田勘探开发研究院主要研究室进行推广应用,支撑了7个地震工区的勘探综合研究、15个新老区方案编制与优化等工作,大幅提升综合研究工作的效率和质量,为油田高效勘探、效益开发提供支持。

关键词: 勘探开发, 综合研究, 流程化, 协同化, 可视化

Abstract:

There are many nodes in the comprehensive research process of exploration and development, which are highly professional. In order to solve the problems that the research process is not explicit, and lack of data service support and visualization means, a standardized comprehensive research process of exploration and development has been formed, and a digital platform for comprehensive research of oil and gas exploration and development has been developed, so as to achieve rapid acquisition of research data and automatic archiving of research results. Integrating the professional software data service and visual analysis support means, and calling the required professional software with one click can realize multi-disciplinary online collaboration of different research posts. Through the standardized comprehensive research process, the new staff can be able to enter into the spirit of the role as soon as possible, and the valuable experience of the old staff can be inherited through the continuous improvement of the process. The platform has been widely applied in the main research office of Shengli Oilfield Exploration and Development Research Institute, supporting the exploration comprehensive research of seven seismic work areas, and the scheme preparation and optimization of 15 new and old areas, greatly improving the efficiency and quality of comprehensive research work, and providing support for efficient exploration and beneficial development of the oilfield.

Key words: exploration and development, comprehensive research, process oriented, collaboration, visualization

中图分类号:

TE311

杨耀忠,谭绍泉,孙业恒,穆星,马承杰,刘建涛. 油气勘探开发综合研究数字平台建设及应用[J]. 油气藏评价与开发, 2021, 11(4): 628-634.

YANG Yaozhong,TAN Shaoquan,SUN Yeheng,MU Xing,MA Chengjie,LIU Jiantao. Construction and application of digital platform for comprehensive research of oil and gas exploration and development[J]. Petroleum Reservoir Evaluation and Development, 2021, 11(4): 628-634.

图/表 4

参考文献 25

[1]	王志高, 卞法敏, 明光春. 勘探综合研究项目数据管理模式探讨[J]. 石油地球物理勘探, 2008, 43(1):119-122.
	WANG Zhigao, BIAN Famin, MING Guangchun. Discussion on data management mode in integrative exploration research projects[J]. Oil Geophysical Prospecting, 2008, 43(1):119-122.
[2]	彭英, 万剑华, 宋建, 等. 一种用于油田勘探的云服务平台的构建设计[J]. 石油地球物理勘探, 2012, 47(1):166-172.
	PENG Ying, WAN Jianhua, SONG Jian, et al. An architecture design of cloud computing service platform for oil exploration[J]. Oil Geophysical Prospecting, 2012, 47(1):166-172.
[3]	盛秀杰, 梅廉夫. 油气勘探和开发领域中间件的设计及实现[J]. 石油地球物理勘探, 2010, 45(4):602-605.
	SHENG Xiujie, MEI Lianfu. Designing and realization of middleware in E&P area[J]. Oil Geophysical Prospecting, 2010, 45(4):602-605.
[4]	陈强, 王宏琳. 数字油田:集成油田的数据、信息、软件和知识[J]. 石油地球物理勘探, 2002, 37(1):90-96.
	CHEN Qiang, WANG Honglin. Numeric oilfietd: integrating data, information,software and knowledge of oilfield[J]. Oil Geophysical Prospecting, 2002, 37(1):90-96.
[5]	吴钧, 于晓红. 大庆油田生产经营管理与辅助决策系统设计与实施[J]. 大庆石油地质与开发, 2019, 38(5):294-300.
	WU Jun, YU Xiaohong. Design and implementation of the production management and assistant decision-making system for Daqing Oilfield[J]. Petroleum Geology & Oilfield Development in Daqing, 2019, 38(5):294-300.
[6]	金晓波. 南川区块断层精细解释研究[J]. 油气藏评价与开发, 2018, 8(4):6-10.
	JIN Xiaobo. Study on the fine fault interpretation in Nanchuan district[J]. Reservoir Evaluation and Development, 2018, 8(4):6-10.
[7]	蔺鹏. 基于深水沉积学原理的地震属性分析新思路[J]. 特种油气藏, 2019, 26(3):30-35.
	LIN Peng. A new seismic attribute analysis with deep-water sedimentation[J]. Special Oil & Gas Reservoirs, 2019, 26(3):30-35.
[8]	卢伟, 李国福. 最大熵谱分解结合小波变换技术在层序地层划分中的应用[J]. 油气藏评价与开发, 2018, 8(1):1-3.
	LU Wei, LI Guofu. Application of maximum entropy spectrum decomposition combined with wavelet transform in the division of sequence stratigraphy[J]. Reservoir Evaluation and Development, 2018, 8(1):1-3.
[9]	宋辉, 陈伟, 李谋杰, 等. 基于卷积门控循环单元网络的储层参数预测方法[J]. 油气地质与采收率, 2019, 26(5):73-78.
	SONG Hui, CHEN Wei, LI Moujie, et al. A method to predict reservoir parameters based on convolutional neural network-gated recurrent unit(CNN-GRU)[J]. Petroleum Geology and Recovery Efficiency, 2019, 26(5):73-78.
[10]	袁士义, 王强. 中国油田开发主体技术新进展与展望[J]. 石油勘探与开发, 2018, 45(4):657-668.
	YUAN Shiyi, WANG Qiang. New progress and prospect of oilfields development technologies in China[J]. Petroleum Exploration and Development, 2018, 45(4):657-668.
[11]	熊钰, 徐宏光, 王玲, 等. 反九点井网加密前后流场变化及驱油效率实验研究[J]. 油气地质与采收率, 2020, 27(3):70-78.
	XIONG Yu, XU Hongguang, WANG Ling, et al. Experimental research on variation of flow field and displacement efficiency before and after infilling of inverted nine-spot well pattern[J]. Petroleum Geology and Recovery Efficiency, 2020, 27(3):70-78.
[12]	胡国强, 胡书勇, 李勇凯, 等. 低渗油藏注水开发五点井网有效驱替压力系统研究[J]. 油气藏评价与开发, 2017, 7(3):20-22.
	HU Guoqiang, HU Shuyong, LI Yongkai, et al. Effective displacement pressure of five-spot pattern by waterflood development in low permeability reservoir[J]. Reservoir Evaluation and Development, 2017, 7(3):20-22.
[13]	于金彪. 油藏数值模拟历史拟合分析方法[J]. 油气地质与采收率, 2017, 24(3):66-70.
	YU Jinbiao. History matching analysis method on reservoir numerical simulation[J]. Petroleum Geology and Recovery Efficiency, 2017, 24(3):66-70.
[14]	张茂林, 廖洪, 杨龙, 等. 页岩气藏储量计算方法分析[J]. 油气藏评价与开发, 2017, 7(3):67-73.
	ZHANG Maolin, LIAO Hong, YANG Long, et al. Reserve calculating method of shale gas reservoir[J]. Reservoir Evaluation and Development, 2017, 7(3):67-73.
[15]	谷建伟, 周梅, 李志涛, 等. 基于数据挖掘的长短期记忆网络模型油井产量预测方法[J]. 特种油气藏, 2019, 26(2):77-81.
	GU Jianwei, ZHOU Mei, LI Zhitao, et al. Oil well production forecast with long-shortterm memory network model based on data mining[J]. Special Oil & Gas Reservoirs, 2019, 26(2):77-81.
[16]	刘万伟, 刘瑞超, 张鸣歌. 石油勘探开发知识管理技术研究与应用[J]. 大庆石油地质与开发, 2019, 38(5):290-293.
	LIU Wanwei, LIU Ruichao, ZHANG Mingge. Research on the knowledge management technique for petroleum exploration and development and its application[J]. Petroleum Geology & Oilfield Development in Daqing, 2019, 38(5):290-293.
[17]	周灿灿, 李潮流, 王昌学, 等. 复杂碎屑岩测井岩石物理与处理评价[M]. 北京: 石油工业出版社, 2013.
	ZHOU Cancan, LI Chaoliu, WANG Changxue, et al. Petrophysics and treatment evaluation of logging complex clastic rocks[M]. Beijing: Petroleum Industry Press, 2013.
[18]	黄帅, 彭彩珍. 基于灰色关联的产量递减因素分析[J]. 油气藏评价与开发, 2018, 8(4):33-35.
	HUANG Shuai, PENG Caizhen. Study on production decline factors based on gray correlation[J]. Reservoir Evaluation and Development, 2018, 8(4):33-35.
[19]	李克文, 周广悦, 路慎强, 等. 一种基于机器学习的有利区评价新方法[J]. 特种油气藏, 2019, 26(3):7-11.
	LI Kewen, ZHOU Guangyue, LU Shenqiang, et al. A new method for favorable zone evaluation based on machine learning[J]. Special Oil & Gas Reservoirs, 2019, 26(3):7-11.
[20]	阳晓燕. 非均质油藏水驱开发效果研究[J]. 特种油气藏, 2019, 26(2):152-156.
	YANG Xiaoyan. Waterflood development effect study of heterogeneous reservoir[J]. Special Oil & Gas Reservoirs, 2019, 26(2):152-156.
[21]	刘兰芹. 胜坨油田整装砂岩油藏矢量化井网调整技术[J]. 长江大学学报(自科版): 2014, 11(13):109-111.
	LIU Lanqin. Vectorization well pattern adjustment technology for integrated sandstone reservoir in Shengtuo Oilfield[J]. Journal of Yangzte University(Natural Science Edition), 2014, 11(13):109-111.
[22]	张善义, 兰金玉, 李冰. 基于粒子群算法的综合调整方案优化方法[J]. 特种油气藏, 2019, 26(1):126-130.
	ZHANG Shanyi, LAN Jinyu, LI Bing. Comprehensive optimization of adjustment program based on particle swarm optimization[J]. Special Oil & Gas Reservoirs, 2019, 26(1):126-130.
[23]	田鸿照. 水平井注采井网和注采参数优化研究[J]. 石油化工应用, 2016, 35(8):6-9.
	WANG Hongzhao. Optimization of horizontal injection-production well pattern and parameter[J]. Petrochemical Industry Application, 2016, 35(8):6-9.
[24]	侯春华. 基于长短期记忆神经网络的油田新井产油量预测方法[J]. 油气地质与采收率, 2019, 26(3):105-110.
	HOU Chunhua. New well oil production forecast method based on long-term and short-term memory neural network[J]. Petroleum Geology and Recovery Efficiency, 2019, 26(3):105-110.
[25]	邴绍献. 基于特高含水期油水两相渗流的水驱开发特征研究[D]. 成都:西南石油大学, 2013.
	BING Shaoxian. Study on water drive development characteristics based on the oil-water two phase flow of ultra-high water cut stage[D]. Chengdu: Southwest Petroleum University, 2013.