石油钻采工艺2023，Vol.45Issue(4)：393-403,11.DOI:10.13639/j.odpt.202212019
基于双输入序列到序列模型的井眼轨迹实时智能预测方法
Real-time intelligent prediction of well trajectory based on dual-input sequence-to-sequence model
摘要
Abstract
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-to-sequence(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.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Key words
artificial intelligence/well trajectory/real-time prediction/Di-S2S model/time series features/non-time series features分类
能源科技引用本文复制引用
李臻,宋先知,李根生,张洪宁,祝兆鹏,王正,刘慕臣..基于双输入序列到序列模型的井眼轨迹实时智能预测方法[J].石油钻采工艺,2023,45(4):393-403,11.基金项目
国家重点研发计划项目"变革性技术关键科学问题"(编号:2019YFA0708300) （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). （编号:52125401）
