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A novel empirical correlation for waterflooding performance prediction in stratified reservoirs using artificial intelligence
Neural Computing and Applications ( IF 6 ) Pub Date : 2020-07-04 , DOI: 10.1007/s00521-020-05158-1
Shams Kalam , Sidqi A. Abu-Khamsin , Hasan Y. Al-Yousef , Rahul Gajbhiye

Water has been used as an injected fluid for decades to improve oil recovery, commonly known as waterflooding. Simulating this process is very expensive, especially for the post-water breakthrough analysis in stratified oil reservoirs. The existing correlations do not predict waterflooding performance in heterogeneous reservoirs accurately. Most of the methods do not account for pattern flooding and consider piston-like displacement with non-communicative layers. In this study, a model has been developed using artificial neural networks (ANNs) for predicting the recovery performance of a layered reservoir undergoing a five-spot-pattern waterflood. In addition to the ANN model, a mathematical equation is presented based on ANN to predict the oil recovery in pattern waterflooding with and without crossflow between the layers for different rock wettabilities. A novel parameter—wettability indicator (WI)—has also been introduced that can be used to quantify the rock’s wettability based only on the relative permeability curves. The results showed that the introduction of the new term (WI) significantly decreased the simulation runs in comparison with existing relative permeability models. ANN approach was compared with non-linear regression (NLR) and adaptive neuro-fuzzy inference system (ANFIS). The ANN model outperformed NRL and ANFIS in terms of least mean absolute percentage error (MAPE) and highest coefficient of determination (R2). The new correlation was tested with an unseen data set, two different real field cases, an analytical model, and a semi-analytical model. The training and testing data show good match and accuracy with R2 of 0.9973 and 0.997, respectively. MAPE of the predicted recovery efficiency using a blind data set was around 7%. The developed correlation can be a useful tool for a quick estimate of the waterflood oil recovery before a large simulation model is built and ran.



中文翻译:

基于人工智能的分层储层注水性能预测的新型经验关联

数十年来,水一直被用作注入流体,以提高采油率,通常称为注水。模拟此过程非常昂贵,尤其是对于分层油藏的水后突破分析而言。现有的相关性不能准确预测非均质油藏的注水性能。大多数方法不考虑模式泛滥,而是考虑非连通层的类活塞位移。在这项研究中,已经使用人工神经网络(ANN)开发了一个模型,用于预测经历五点模式注水的分层储层的采收性能。除了人工神经网络模型之外,提出了一种基于神经网络的数学方程式,以预测不同岩石润湿性情况下在层间有无交叉流的情况下注水的采收率。还引入了一种新的参数-润湿性指标(WI)-仅可用于根据相对渗透率曲线来量化岩石的润湿性。结果表明,与现有的相对渗透率模型相比,新术语(WI)的引入显着降低了模拟运行。将ANN方法与非线性回归(NLR)和自适应神经模糊推理系统(ANFIS)进行了比较。在最小平均绝对百分比误差(MAPE)和最高确定系数方面,ANN模型优于NRL和ANFIS(还引入了一种新的参数-润湿性指标(WI)-仅可用于根据相对渗透率曲线来量化岩石的润湿性。结果表明,与现有的相对渗透率模型相比,引入新术语(WI)显着减少了模拟运行。将人工神经网络方法与非线性回归(NLR)和自适应神经模糊推理系统(ANFIS)进行了比较。在最小平均绝对百分比误差(MAPE)和最高确定系数方面,ANN模型优于NRL和ANFIS(还引入了一种新的参数-润湿性指标(WI)-仅可用于根据相对渗透率曲线来量化岩石的润湿性。结果表明,与现有的相对渗透率模型相比,引入新术语(WI)显着减少了模拟运行。将人工神经网络方法与非线性回归(NLR)和自适应神经模糊推理系统(ANFIS)进行了比较。在最小平均绝对百分比误差(MAPE)和最高确定系数方面,ANN模型优于NRL和ANFIS(将人工神经网络方法与非线性回归(NLR)和自适应神经模糊推理系统(ANFIS)进行了比较。在最小平均绝对百分比误差(MAPE)和最高确定系数方面,ANN模型优于NRL和ANFIS(将人工神经网络方法与非线性回归(NLR)和自适应神经模糊推理系统(ANFIS)进行了比较。在最小平均绝对百分比误差(MAPE)和最高确定系数方面,ANN模型优于NRL和ANFIS(R 2)。使用一个看不见的数据集,两个不同的实际案例,一个分析模型和一个半分析模型测试了新的相关性。训练和测试数据显示出良好的匹配度和准确性,R 2分别为0.9973和0.997。使用盲数据集的预测回收率的MAPE约为7%。在建立和运行大型仿真模型之前,建立的相关性可以成为快速估算注水采收率的有用工具。

更新日期:2020-07-05
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