侯亚伟,刘超,徐中波,等. 多层水驱开发油田采收率快速预测方法[J]. 石油钻探技术,2022, 50(5):82-87. DOI: 10.11911/syztjs.2022102
引用本文: 侯亚伟,刘超,徐中波,等. 多层水驱开发油田采收率快速预测方法[J]. 石油钻探技术,2022, 50(5):82-87. DOI: 10.11911/syztjs.2022102
HOU Yawei, LIU Chao, XU Zhongbo, et al. A method for rapidly predicting recovery of multi-layer oilfields developed by water-flooding [J]. Petroleum Drilling Techniques,2022, 50(5):82-87. DOI: 10.11911/syztjs.2022102
Citation: HOU Yawei, LIU Chao, XU Zhongbo, et al. A method for rapidly predicting recovery of multi-layer oilfields developed by water-flooding [J]. Petroleum Drilling Techniques,2022, 50(5):82-87. DOI: 10.11911/syztjs.2022102

多层水驱开发油田采收率快速预测方法

A Method for Rapidly Predicting Recovery of Multi-Layer Oilfields Developed by Water-Flooding

  • 摘要: 为了快速准确预测水驱开发油田的采收率,在考虑储层特征、流体性质等影响原油采收率因素的基础上,建立了基于反向传播神经网络优化算法的采收率快速预测方法。首先,以蓬莱19-3油田地质特征和流体性质为依据,建立了油藏数值模拟地质模型,选取渗透率变异系数、原油黏度、油层净毛比和生产压差等4个关键因素,每个因素选取5个水平,采用油藏数值模拟方法对625组数据进行了模拟,建立了625组采收率及其影响因素关系数据库;然后,基于BP网络及优化理论,建立了快速预测采收率的人工神经网络方法。选取500组数据作为算法训练集,125组数据进行测试,测试结果表明,125组测试数据的预测采收率相对误差范围为−2.91%~5.07%,平均相对误差为0.16%,满足工程精度要求。多层水驱开发油田采收率快速预测方法为蓬莱19-3油田及其他同类油田采收率快速预测提供了新的技术手段。

     

    Abstract: In order to quickly and accurately predict the recovery of oilfields developed by water flooding, a method for rapidly predicting oil recovery was established based on a back propagation (BP) neural network optimization algorithm with consideration of factors influencing the recovery, such as reservoir characteristics and fluid properties. Firstly, geological models for numerical reservoir simulation were constructed according to the geological characteristics and fluid properties of Penglai 19-3 Oilfield. Four key factors including coefficient of permeability variation, oil viscosity, net to gross ratio of oil layers, and production pressure differential were selected, with each factor defined into five levels. 625 groups of reservoir simulation cases were analyzed numerically, and a database indicating the relationship between the oil recovery of the cases and the influencing factors was established. Secondly, an artificial neural network (ANN) method for rapidly predicting oil recovery was set up based on BP neural network and optimization theory. Finally, 500 groups of data were selected as the algorithm training set, and 125 groups of data were tested for recovery predicting. The test result showed that the predicted oil recovery of the tested data had a relative error ranging from −2.91% to 5.07% with an average relative error of 0.16%, which met the requirement for engineering accuracy. The method for rapidly predicting recovery of multi-layer oilfields developed by water-flooding provides a new technical approach to rapidly predict the recovery of Penglai 19-3 Oilfield and other similar oilfields.

     

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