基于实时钻进参数的孔隙压力智能预测技术
Intelligent Prediction of Pore Pressure Using Real-time Drilling Parameters
李萍 1于琛 1王建龙 1杨恒 2贾培娟 1李邓玥 1冯永存3
作者信息
- 1. 中国石油集团渤海钻探工程有限公司工程技术研究院
- 2. 中国石油大学(北京)安全与海洋工程学院
- 3. 中国石油大学(北京) 石油工程学院
- 折叠
摘要
针对当前孔隙压力预测方法存在适用范围限制、精度不足、计算繁琐和无法实时预测等问题,提出了一种基于实时钻进数据的地层孔隙压力预测方法.基于测井数据计算孔隙压力的理论真实值,作为预测的学习目标;通过相关系数法及模型选择法,确定了 8 项关键参数:大钩载荷、泵压、机械钻速、钻压、转速、排量、密度和黏度;基于这些参数,采用 3 种集成机器学习算法,分别建立孔隙压力的实时预测模型.训练集预测结果分析表明:XGBoost和LightGBM模型在关键评估指标上表现良好,而随机森林模型存在过拟合的现象;XGBoost和LightGBM模型的预测趋势更加稳定,在预测精度和稳定性上更具优越性;所有模型在更换钻头造成钻头参数与钻头磨损情况变化后均产生了一定的平移偏差.后期可通过探究钻头特性与预测偏差的具体关系,或通过调整模型、对预测结果适当修正来进一步提高预测准确性.该预测方法不仅提高了预测精度,还为现场工程师提供了实时决策支持,有助于钻井策略的优化并降低风险.
Abstract
The existing pore pressure prediction methods have limitations like restricted application,insuffi-cient accuracy,complex computation,and inability to predict in real-time manner.This paper presents a new method for predicting pore pressure based on real-time drilling data.Firstly,the logging data is used to calculate the theoretic actual value of pore pressure,which serves as the learning objective for prediction.Secondly,by the correlation coefficient and model selection methods,eight key parameters were determined,including hook load,pump pressure,rate of penetration,weight on bit,RPM,flow rate,density and viscosity.Then,three ma-chine learning algorithms were adopted respectively to build real-time pore pressure prediction models.The predic-tion results of train set show that the XGBoost and LightGBM models yield good results of key performance indica-tors(KPIs),while the random forest(RF)model has the problem of over fitting.The prediction results of the test set show that the XGBoost and LightGBM models are more superior in prediction accuracy and stability.All models produce translation deviations when parameter change and bit wear occur after the bit is replaced.The rela-tionship between the bit features and the prediction deviation can be investigated,or the models be modified to properly correct the prediction results,so as to achieve higher prediction accuracy.The proposed method is more accurate,and also provides real-time support for field decision making,thereby facilitating drilling optimization and risk reduction.
关键词
地层孔隙压力/机器学习/智能预测/钻进参数/随机森林Key words
pore pressure/machine learning/intelligent prediction/drilling parameter/random forest引用本文复制引用
基金项目
中国石油集团渤海钻探工程有限公司工程技术研究院科研项目(2022BC66F)
出版年
2024
NETL
NSTL
万方数据