Low-salinity waterflooding (LSWF) has, in the past decade, attained a lot of attention to enhance oil recovery. In LSWF, diluted water is injected into an oil reservoir to improve oil recovery. The injected low-saline water changes the wettability of the reservoir, which leads to higher oil recovery. The recovery of an oil reservoir can be predicted from simulators, which are tedious, expensive, and time-consuming. Therefore, there is a need for a simple, quick, and inexpensive substitute to predict the oil recovery factor for low-salinity waterfloods. This paper presents a novel empirical correlation based on a feed-forward neural network to predict LSWF recovery efficiency in a heterogeneous reservoir at and beyond water breakthrough. The proposed model is valid for a broad range of dimensionless input parameters—degree of dilution of high saline water, mobility ratio, degree of reservoir heterogeneity, permeability anisotropy ratio, API gravity, and production water cut. The new empirical correlation was developed using 20,000 simulated data points obtained from simulation results to cover a wide range of input values. The LSWF simulation model was developed and validated with a model of a real carbonate reservoir located in the Madison formation in Wyoming. The artificial neural network (ANN) model parameters were optimized by conducting extensive sensitivities of ANN parameters (hidden layer neurons, training algorithms, and transfer functions). Moreover, an interesting trend analysis was conducted to validate the physical behavior of the ANN model, and a comparison with the unseen dataset was performed. To evaluate the performance of the newly developed correlation, three statistical indices were used, including the average absolute percentage error (AAPE). AAPE was 1.69% and 1.84% for the training and testing datasets, respectively. The proposed ANN model is limited to a single-stage, low-saline waterfloods for a 5-spot pattern.

在过去的十年中，低盐度注水（LSWF）在提高采油率方面引起了很多关注。在LSWF中，将稀释水注入储油罐以提高采油率。注入的低盐度水会改变储层的润湿性，从而提高采油率。可以通过模拟器来预测油藏的回收，这是乏味，昂贵且费时的。因此，需要一种简单，快速且便宜的替代品来预测低盐度注水的采油率。本文提出了一种基于前馈神经网络的新型经验相关性，以预测在突破水位及超出水位的非均质油藏中的LSWF采收率。提出的模型适用于广泛的无量纲输入参数-高盐水稀释度，流动度比，储层非均质度，渗透率各向异性比，API重力和采出水量。使用从仿真结果获得的20,000个仿真数据点开发了新的经验相关性，以涵盖广泛的输入值。使用位于怀俄明州麦迪逊组的真实碳酸盐储层模型开发并验证了LSWF模拟模型。人工神经网络（ANN）模型参数通过对ANN参数（隐藏层神经元，训练算法和传递函数）进行广泛的敏感性优化。此外，进行了有趣的趋势分析以验证ANN模型的物理行为，并与看不见的数据集进行了比较。为了评估新开发的相关性的性能，使用了三个统计指标，包括平均绝对百分比误差（AAPE）。对于训练和测试数据集，AAPE分别为1.69％和1.84％。所提出的ANN模型仅限于5点模式的单级低盐注水。