摘要
地应力是油气藏全生命周期中井壁稳定分析、水力压裂设计、地层出砂预防、套损预测防治和油气开采措施制定的重要基础参数.针对缺乏三维地震资料情况下的三维地应力场预测问题,以工区已钻井地应力测井解释结果为基础,建立了一种基于物理约束的分布式神经网络三维地应力预测模型.首先,采用测井解释方法获得工区已钻井地应力单井剖面,并通过Kriging插值构建三维地质模型;其次,将测井数据和三维地质模型的三维空间坐标喂入3个并行的全连接神经网络,引入地应力物理约束条件,提出了数据和物理双约束的条件,进而通过三维空间坐标实现三维地应力场的预测;再次,优选了数据和物理双约束的权重参数,与人工神经网络、支持向量回归、随机森林等三种机器学习模型以及克里金插值模型进行了对比;最后,评价了不同机器学习模型预测地应力的效果.研究结果表明:(1)研究区域的地应力满足潜在正断层应力状态,即垂向地应力>最大水平地应力>最小水平地应力;(2)数据和物理双约束权重参数取值对预测结果有显著影响,当权重参数λ=0.2时,三维地应力预测效果最优;(3)与人工神经网络、支持向量回归、随机森林、克里金插值等模型相比,本文模型预测得到的地层三维地应力和孔隙压力更准确,在工区内测试集预测得到的垂向地应力、最大水平地应力、最小水平地应力和地层孔隙压力的最大相对误差分别为0.63%、7.59%、7.16%、3.21%,而且,本文模型能够更加准确捕捉地应力梯度的三维变化特征,水平井段地应力和地层孔隙压力参数的正态分布特征明显.结论认为,物理约束的分布式神经网络模型能有效融合地形构造与地应力之间的联系,提高三维地应力预测的准确性和可解释性,为油田三维地应力场的预测提供了一种新的思路.
In-situ stress is an important basic parameter in the whole life cycle of an oil and gas reservoir,such as wellbore stability analysis,hydraulic fracturing design,sand production prevention,casing damage prediction and prevention,and oil and gas exploitation measures.Aiming to address the challenge of predicting 3D in-situ stress fields in the absence of 3D seismic data,a physics-constrained distributed neural network model(PDNN)was proposed for 3D in-situ stress prediction.This model was developed based on the logging interpretation results of in-situ stress for drilled wells in the working area.Firstly,the single well profile of in-situ stress was obtained by logging interpretation method,and the 3D geological model was constructed by Kriging interpolation.Secondly,the logging data and the 3D spatial coordinates of the 3D geological model were input into three fully connected neural networks.The physical constraints of the in-situ stress were introduced,the conditions of both the data and the physical constraints were proposed,and the 3D spatial coordinates were employed to forecast the 3D in-situ stress field.Subsequently,the weight parameters of both the data and the physical constraints were selected and compared with three machine learning models,including the artificial neural network(ANN),support vector regression(SVR),and random forest(RF),as well as the Kriging interpolation model.Finally,the effect of different machine learning models in predicting in-situ stress was then evaluated.The results indicated that:(1)The in-situ stress in the study area was consistent with the potential normal faulting stress state,namely,vertical principal stress>maximum horizontal principal stress>minimum horizontal principal stress.(2)The value of the weight parameter,which incorporates both the data and the physical constraints,had a significant influence on the prediction results.The optimal prediction performance for the 3D in-situ stress was achieved when the weight parameter was set to λ=0.2.(3)In comparison to the
作者
马天寿
向国富
桂俊川
贾利春
唐宜家
MA TianShou;XIANG GuoFu;GUI JunChuan;JIA LiChun;TANG YiJia(State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation,Southwest Petroleum University,Chengdu 610500,China;PipeChina Energy Storage Technology Co.,Ltd.,Shanghai 200122,China;Research Institute of Shale Gas,PetroChina Southwest Oil and Gasfield Company,Chengdu Sichuan 610051,China;Drilling and Production Engineering Technology Research Institute,Chuanqing Drilling Engineering Co.Ltd.,CNPC,Guanghan Sichuan 618300,China;Engineering Technology Research Institute,PetroChina Southwest Oil&Gasfield Company,Chengdu 610017,China)
出处
《地球物理学报》
SCIE
EI
CAS
CSCD
北大核心
2024年第8期3211-3228,共18页
Chinese Journal of Geophysics
基金
国家自然科学基金项目(51604230)
四川省自然科学基金重点项目(2024NSFSC0023)资助。
关键词
三维地应力
孔隙压力
神经网络
物理约束
数据驱动
地质模型
3D in-situ stress
Pore pressure
Neural network
Physical constraint
Data-driven
Geological model
