摘要
四川盆地下志留统龙马溪组页岩是目前我国页岩气勘探开发主要领域之一。为了实现页岩气水平井勘探开发方案优化、产量综合精细挖掘、节约开发成本、提高最终采收率等目标,基于非放射性示踪剂精细解释,开展了利用分形维数对龙马溪细分小层的产气规律特征进行描述,再通过GA-BP神经网络建立产量预测模型对产量进行有效预测,从而解决了排采初期快速评价产能的问题。利用非放射性示踪剂解释结果结合动、静态资料建立页岩气压裂井单井各小层精细解释产气剖面,从而分析长宁页岩气示范区气藏储层龙一段各小层产气特征,对地质导向“铂金箱体”具有非常重要的意义。目前新投产的页岩气井大部分未进行放喷测试,造成初期页岩气产能难以评价,基于非放射性示踪剂精细解释的页岩气井产量预测方法能够快速评价页岩气初期产能,提高了产量预测的效率,从而有效指导页岩气单井合理的开发。
Silurian Longmaxi group shale in Sichuan basin is one of the main areas of shale gas exploration and development.In order to achieve the goals of optimization of exploration and development plans for comprehensive fine exploitation,saving development costs,and improving final oil recovery,based on the fine interpretation of non-radioactive tracers,the fractal dimension was used to describe the gas production law characteristics of the subdivision of the Longmaxi subdivision layer,and then the production prediction model is established through the GA-BP neural network to effectively predict the production capacity.Therefore,the problem of rapid evaluation of production capacity in the early stage of drainage is solved.By using the interpretation results of non-radioactive tracer and combining with the dynamic and static data,the fine interpretation gas production profile of each small layer in a single well of shale gas fracturing well is established,moreover,the analysis of the gas production characteristics for each small layer in the Longmaxi formation 1 of the gas reservoir in the Changning Shale Gas Demonstration zone is of great significance for the geological guidance of the"platinum box".At present,most of the newly put into production wells have not been put into production test,which makes it difficult to evaluate the early productivity,a rapid productivity evaluation model is provided,which improves the efficiency of productivity prediction and effectively guides the rational development of shale gas single wells.
作者
陈学忠
欧阳诚
瞿子易
唐谢
罗鑫
何健
CHEN Xuezhong;OU Yangcheng;QU Ziyi;TANG Xie;LUO Xin;HE Jian(Sichuan Changning Natural Gas Development Co.,Ltd.,Chengdu,Sichuan 610017,China;Geological Exploration&Development Research Institute,CNPC Chuanqing Drilling Engineering Co.,Ltd.,Chengdu,Sichuan 610051,China)
出处
《钻采工艺》
CAS
北大核心
2023年第4期77-81,共5页
Drilling & Production Technology
基金
国家自然科学基金层面上项目“深层海相页岩气气藏水赋存机制及跨尺度流动模拟研究”(编号:52074235)。
关键词
非放射性示踪剂
页岩气
产量预测
地面测试
神经网络算法
non-radioactive tracer
shale gas
productivity prediction
well test
neural network algorithm
