考虑地质分层约束的长短期记忆循环神经网络测井曲线重构  

Logging Curve Reconstruction of Long Short-term Memory Recurrent Neural Network Considering Geological Stratification Constraints

作　　者：张亮 党海龙 刘庆海 曾俊 蔺建武 王涛 丁磊 ZHANG Liang;DANG Hai-long;LIU Qing-hai;ZENG Jun;LIN Jian-wu;WANG Tao;DING Lei(Research Institute of Shanxi Yanchang Petroleum(Group)Co.,Ltd,Xi'an 710075,China;Extra Low Permeability Oil and Gas Field Exploration and Development Engineering Technology Research Center of Shaanxi Province,Xi'an 710075,China)

机构地区：[1]陕西延长石油(集团)有限责任公司研究院,西安710075 [2]陕西省特低渗透油气田勘探与开发工程技术研究中心,西安710075

出　　处：《科学技术与工程》2024年第19期8045-8051,共7页Science Technology and Engineering

基　　金：陕西延长石油集团揭榜挂帅项目(ycsy2023jbgs-A-01)。

摘　　要：延长油田东部裸眼井区早期测井资料普遍只有自然电位(SP)、自然伽马(GR)及梯度电阻率(R2.5)三条曲线,因缺失声波(AC)、地层电阻率(RT)等测井曲线,难以满足精细油藏地质研究需求。东部裸眼井区开发时间长、单井产量低,重新测井缺乏可行性及经济性。采用长短期记忆循环神经网络(long short-term memory,LSTM)模型进行缺失测井曲线重构是一种经济有效方法,适用于地层测井序列数据。然而延长油田东部浅层油藏上覆黄土层段测井数据信号干扰大,直接应用模型精度较差。针对此问题,采用考虑地质分层约束的LSTM模型进行缺失测井曲线的重构,通过分层数据截取每口井长6层段测井数据作为样本数据,既保留了LSTM模型处理序列数据的优势,同时又避免了上覆黄土层测井数据对模型的干扰。利用裸眼井区完整测井数据进行模型训练优化和验证,讨论了考虑地质分层约束的LSTM测井曲线重构精度,结果表明通过引入地质分层约束,模型重构测井曲线精度更高。应用优化后模型实现裸眼井区50口仅有GR、SP、R2.5三条曲线数据井的AC、RT曲线重构,对50口井的142个射孔段进行二次解释,对比试油解释结论符合率达到89.4%,验证了该方法对测井曲线重构的实用性和有效性。The open hole well area located in the eastern part of the Yanchang Oilfield has an earlier logging time.There are only three logging data in this area:SP,GR and R2.5.Due to the lack of logging curves such as AC and RT,refined research on reservoir geology is difficult to meet.The region has the characteristics of long development time and low single well production,so it is infeasible and uneconomical to carry out logging again.Long short-term memory(LSTM)model can be used to reconstruct missing logging curves.It is an economical and effective method suitable for stratigraphic logging sequence data.However,the application effect of this model in the eastern part of Yanchang Oilfield is poor.The reason is that the loess layer overlying the shallow oil reservoirs in the eastern region has significant interference with logging data signals.To address this issue,the LSTM model was constrained by geological stratification and a new model was established to achieve curve reconstruction.The logging data of each well with a length of 6 layers is intercepted by layered data as sample data.This method not only retains the advantages of LSTM model for sequential data processing,but also avoids the interference of overlying loess layer logging data on the model.Open hole wells with complete logging data were utilized for model training and validation.The accuracy of LSTM logging curve reconstruction considering geological stratification constraints was discussed.The results indicate that by introducing geological stratification constraints,the accuracy of model reconstruction logging curves is higher.The optimized model was used to reconstruct AC and RT curves for 50 wells in the open hole area with only GR,SP,and R2.5 curve data.Secondary interpretation work was carried out on 142 perforated sections of 50 wells,and the coincidence rate of interpretation results compared to oil testing conclusions reached 89.4%.The practicality and effectiveness of this method in reconstructing logging curves have been verified.

关 键 词：测井曲线重构 长短期记忆神经网络 地质分层约束 声波时差 电阻率 东部裸眼井区 

分 类 号：TE151[石油与天然气工程—油气勘探]