摘要
储层可压性的准确评价是储层压裂设计和压后产能评估的重要前提。目前,采用岩石力学参数进行页岩可压裂性评价取得了较好的现场应用效果。因此,如何准确获取岩石力学参数成为至关重要的问题。通过建立一种基于物理信息约束的神经网络模型,该模型采用物理和数据双驱动,仅使用少量数据就能够实现岩石力学参数的准确预测。为验证模型性能的优异性,采用人工神经网络、随机森林和XGBoost模型与之进行对比。结果表明,物理信息约束的神经网络在少量数据下预测岩石力学参数的平均准确率高于95%,性能远优于其他模型。采用物理信息约束的神经网络预测得到弹性模量、泊松比、抗拉强度和断裂韧性4种岩石力学参数,基于岩石力学参数对储层可压性的影响,建立了基于脆性指数和力学参数的可压性评价方法。最后,以渤海湾盆地沧东凹陷K2段不同储层可压性为例进行验证。结果表明:研究区整体可压性较好,其中,纹层状混合质页岩可压裂指数高于0.7,可压性良好;纹层状长英质页岩、厚层状灰云质页岩和薄层灰云质页岩可压裂指数均处在0.4~0.7,可压性中等。评价结果与实际施工现场各储层日采油量进行对比,证实了可压性智能评价方法的可靠性,该方法可以推广至页岩储层可压性评价工作中。
The accurate evaluation of reservoir fracability is an essential prerequisite for the fracturing design and postfracturing productivity evaluation of reservoirs.Rock mechanical parameters have been applied to the fracability evaluation of shales presently,exhibiting great field application performance.Accordingly,it is crucial to obtain accurate rock mechanical parameters.This study developed a physics-informed neural network(PINN)model.Driven by data and physical information,the PINN model can accurately predict rock mechanical parameters using only a small amount of data.To verify its performance,the PINN model was compared with the artificial neural network,random forest,and XGBoost models.The comparison results show that the PINN model yielded an average accuracy greater than 95%,outperforming other models.Using the PINN model,this study obtained four rock mechanical parameters,namely modulus of elasticity,Poisson's ratio,tensile strength,and fracture toughness.Given the influence of rock mechanical parameters on reservoir fracability,this study developed an evaluation method for reservoir fracability based on the brittleness index and mechanical parameters.This fracability evaluation method was applied to reservoirs in the K2 member in the Cangdong sag of the Bohai Bay Basin.The evaluation results indicate generally high fracability of the study area.Specifically,lamellar mixed shales showed a fracability index of higher than 0.7,indicating high fracability,while lamellar felsic shales and thickly and thinly laminated shales comprising calcareous and dolomitic rocks of equal amounts exhibited fracability indices of 0.4‒0.7,indicating moderate fracability.The comparison between the evaluation results and the daily oil production of various reservoirs at the construction site verified the reliability of the smart fracability evaluation method developed in this study.Therefore,this fracability evaluation method can be applied to the fracability evaluation of shale reservoirs.
作者
李玉伟
李子健
邵力飞
田福春
汤继周
LI Yuwei;LI Zijian;SHAO Lifei;TIAN Fuchun;TANG Jizhou(School of Petroleum Engineering,Northeast Petroleum University,Daqing 163318,China;School of Environment,Liaoning University,Shenyang 110036,China;Sinosteel Maanshan General Institute of Mining Research Co.,Ltd.,Maanshan 243000,China;Petroleum Engineering Research Institute,PetroChina Dagang Oilfield Company,Tianjin 300280,China;School of Ocean and Earth Science,Tongji University,Shanghai 200092,China)
出处
《煤田地质与勘探》
EI
CAS
CSCD
北大核心
2023年第10期37-51,共15页
Coal Geology & Exploration
基金
国家自然科学基金面上项目(52174024)
中国石油科技创新基金项目(2021DQ02-0501)。
关键词
页岩油储层
岩石力学参数
可压性
机器学习
物理信息约束
shale oil researvoirs
rock mechanical parameter
fracability
machine learning
physics-informed
