摘要
准确预测井眼轨迹是井眼轨迹控制的基础,对提高钻井效率极为重要,但影响井眼轨迹变化的因素众多,且井下力学行为复杂,使井眼轨迹难以准确预测,为此,提出了一种双输入序列到序列模型(Di-S2S)。该模型考虑了钻压、钻速等时序特征以及钻进方式、地层分层、钻具组合等非时序特征,应用自然语言处理方法对非时序特征进行了数值化和降维表征,基于增量训练建立了模型动态更新机制。使用12口井数据进行了模型训练与验证,并与LSTM和BP模型进行了对比,结果显示,井斜角平均绝对误差分别较LSTM和BP模型降低49%和8%,方位角平均绝对误差分别较LSTM和BP模型降低了49%和24%。动态更新模型的井斜角和方位角平均绝对误差较离线模型分别降低了61%和67%,均低于0.2°,表明该模型精度较高,具备实时预测能力,可为导向钻井提供一定技术支撑。
Accurate prediction of well trajectory is fundamental to well trajectory control,and therefore,extremely important for improving drilling efficiency.However,there are many factors that may change well trajectory,and the downhole mechanical behavior is complex,which leads to high difficulties in accurately predicting well trajectory.This presents a dual-input sequence-tosequence(Di-S2S)model.The model considers time series features,such as WOB and ROP,and non-time series features,such as drilling mode,formation stratigraphy and BHA structure.The non-time series features were numerically characterized with dimensionality reduction via a natural language processing process,and a dynamic updating mechanism based on incremental training was built for the model.The data of 12 wells were analyzed with the Di-S2S model,and the results were compared with those of the LSTM and BP models.The results show that the average absolute error of well inclination angles is reduced by 49%and 8%respectively,and the average absolute error of azimuths is reduced by 49%and 24%,respectively,compared with the LSTM and BP models.Moreover,compared with the offline model,the average absolute errors of well inclination and azimuth of the dynamic updating model,both lower than 0.2°,are reduced by 61%and 67%respectively.The presented Di-S2S model has high accuracy and enables real-time prediction.This research provides technical support for steerable drilling.
作者
李臻
宋先知
李根生
张洪宁
祝兆鹏
王正
刘慕臣
LI Zhen;SONG Xianzhi;LI Gensheng;ZHANG Hongning;ZHU Zhaopeng;WANG Zheng;LIU Muchen(College of Petroleum Engineering,China University of Petroleum(Beijing),Beijing 102249,China;National Key Laboratory of Petroleum Resources and Engineering,Beijing 100016,China;SINOPEC Research Institute of Petroleum Engineering Technology,Beijing 102206,China;College of Mechanical and Transportation Engineering,China University of Petroleum(Beijing),Beijing 102249,China)
出处
《石油钻采工艺》
CAS
北大核心
2023年第4期393-403,共11页
Oil Drilling & Production Technology
基金
国家重点研发计划项目“变革性技术关键科学问题”(编号:2019YFA0708300)
国家杰出青年科学基金“油气井流体力学与工程”(编号:52125401)。
关键词
人工智能
井眼轨迹
实时预测
序列到序列模型
时序特征
非时序特征
artificial intelligence
well trajectory
real-time prediction
Di-S2S model
time series features
non-time series features
