摘要
老油田开发井数多、纵向上多套层系发育、油水关系复杂,地层对比人工解释工作量大、多解性强。常规大数据研究思路将多条测井曲线和分层样本标签通过选定的一种机器学习方法一次性建立样本预测模型,该方法预测模型精度低、收敛困难。针对性地提出了一种基于数据驱动和循环滑动时窗的小层智能划分方法,优选对地质分层敏感的测井曲线作为特征参数,为丰富样本库采取“窗口对点”的循环滑动时窗方法多次进行样本数据采集,通过优化不同机器学习方法的超参数,得到最佳训练模型,使用该模型对小层智能划分结果进行预测。分析结果表明,滑动时窗长度为20、步长为2时进行样本采集,基于随机森林方法构建的小层智能划分模型预测准确率达88.4%,优于常规一次性建模预测方法,取得最优的测试效果。
There are many development wells in mature oilfields with multiple series of strata in the vertical direction and complex oil-water relationships.Thus,the manual interpretation of stratigraphic correlation has heavy workload and multiple solutions.In conventional research based on big data,a sample prediction model is established at one time with multiple logging curves and layered sample labels by a selected machine learning algorithm.However,this method has low accuracy and difficult convergence.To tackle the problems,this paper proposes a small layer intelligent division method based on data-driven and cyclic sliding time window.The logging curves sensitive to geological stratification are selected as the characteristic parameters.To enrich the sample database,the paper collects sample data many times with the“window-to point”circular sliding time window method.The optimal traimodel is obtained by optimizing the hyper-parameters of different machine learning algorithms.The model is used to predict the results of small layer partitioning.Analysis results show that when the length of the sliding time window is 20 ning and the step size is 2,the small layer intelligent division model based on the random forest method has a prediction accuracy of 88.4%,which is better than the conventional prediction method based on modeling at one time and achieves the best test effect.
作者
徐鹏晔
XU Pengye(Exploration and Development Research Institute,Shengli Oilfield Company,SINOPEC,Dongying City,Shandong Province,257015,China)
出处
《油气地质与采收率》
CAS
CSCD
北大核心
2022年第1期113-120,共8页
Petroleum Geology and Recovery Efficiency
基金
中国石化科技攻关项目“基于大数据技术的油藏精细表征方法研究”(P20071-1)。
关键词
小层智能划分
机器学习
数据驱动
滑动时窗
随机森林
small layer intelligent division
machine learning
data-driven
sliding time window
random forest
