摘要
复杂断块油藏剩余油预测一直是指导油藏开发后期井位部署、剩余油挖潜工作的关键。为了提高复杂断块油藏剩余油饱和度场预测效率和精度,构建了反映不同构造深度、储层厚度、渗透率、孔隙度等属性的2×10^(4)个数值模拟正演模型,并得到相应的各个模型的剩余油饱和度场分布,从而构建形成饱和度场数据样本库。通过深度卷积对抗神经网络模型对样本库数据进行训练,其中,随机选取70%的数据作为训练集,30%的数据作为测试集,最终建立形成能够应用于实际区块的饱和度场预测方法。研究结果表明:新的方法无需再对实际区块进行数值模拟研究,只需输入实际区块的储层物性参数、井位坐标、注采量等信息,即可通过深度卷积对抗神经网络模型得到区块在不同时刻下的剩余油饱和度分布,方法预测精度达到90%以上。经过测试,深度卷积对抗神经网络模型表现出较好的泛化能力,模型能够被广泛应用于相似的复杂断块油藏剩余油饱和度场预测中,从而提高油藏开发研究的工作效率。
The prediction of remaining oil in complex fault block reservoirs has always been the key to guide well deployment and remaining oil tapping in the later stage of oil reservoir development.To improve the prediction efficiency and accuracy of remaining oil saturation field in complex fault block reservoirs,the paper constructs 2×10^(4) numerical simulation forward models reflecting different structure depth,reservoir thickness,permeability,and porosity,and acquires the distribution of remaining oil saturation field of each model,so as to build the saturation field data sample pool.Then deep convolutional adversarial neural network model is used for the training of data in sample pool,among which 70% of the data are randomly selected as the training set and 30% as the test set.Finally,the saturation field prediction method that can be applied to an actual block is established.The results show that for the new method,it does not need to carry out numerical simulation research on the actual block any more,and only requires to enter information such as physical reservoir parameters,well location coordinates and injection-production volume of the actual block,in which case the distribution of remaining oil saturation of the block at different moments can be obtained through the deep convolutional adversarial neural network model,with a prediction accuracy of up to 90%.After test,it is found that the deep convolutional adversarial neural network model has good generalization ability,and the model can be widely used for predicting the remaining oil saturation field in complex fault block reservoirs,which can greatly improve the research efficiency of reservoir exploitation.
作者
李祯
郭奇
卜亚辉
胡慧芳
Li Zhen;Guo Qi;Bu Yahui;Hu Huifang(Exploration and Development Research Institute,PetroChina Liaohe Oilfield Company,Liaoning Panjin 124000,China;Exploration and Development Research Institute,Sinopec Shengli Oilfield Company,Shandong Dongying 257000,China)
出处
《石油学报》
EI
CAS
CSCD
北大核心
2024年第4期698-707,共10页
Acta Petrolei Sinica
基金
国家科技重大专项“特高含水期提高采收率技术”(2011ZX05011)
长江学者和创新团队发展计划项目“复杂油藏开发和提高采收率的理论与技术”(IRT1294)资助。
关键词
深度学习
对抗神经网络
饱和度场预测
样本库
数值模拟
deep learning
adversarial neural network
saturation field prediction
sample pool
numerical simulation
