Water injection is one of the robust techniques to maintain the reservoir pressure and produce trapped oil from oil reservoirs and improve an oil recovery factor. However, incompatibility between injected water and reservoir water causes an unflavored issue named “scale deposition.” Owing to the deposited scales, effective permeability of a reservoir reduced, and pore throats might be plugged. To determine formation damage owing to scale deposition during a water injection process, two well-known machine learning methods, least squares support vector machine (LSSVM) and artificial neural network (ANN), are employed in the present paper. To improve the performance of the LSSVM method, a metaheuristic optimization algorithm, genetic algorithm (GA), is used. The constructed LSSVM model is examined using real formation damage data samples experimentally measured, which was reported in the literature. According to the obtained outputs of the above models, LSSVM has a high performance based on the correlation coefficient, and infinitesimal uncertainty based on a relative error between the model predictions and the corresponding actual data samples was less than 15%. Outcomes from this study indicate the useful application of the LSSVM approach in the prediction of permeability reduction due to scale deposition, and it can lead to a better and more reliable understanding of formation damage effects through water flooding without expensive laboratory measurements.

中文翻译：

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基于机器学习的模型，用于预测因水垢沉积而导致的渗透性损害

注水是保持储层压力并从储油层中捕集油并提高采油率的可靠技术之一。但是，注入水和储层水之间的不相容性会引起一个无味的问题，称为“水垢沉积”。由于沉积的水垢，储层的有效渗透率降低，孔喉可能被堵塞。为了确定在注水过程中由于水垢沉积造成的地层损伤，本文采用了两种众所周知的机器学习方法，即最小二乘支持向量机（LSSVM）和人工神经网络（ANN）。为了提高LSSVM方法的性能，使用了一种元启发式优化算法，即遗传算法（GA）。使用实验测量的真实地层破坏数据样本检查了构建的LSSVM模型，该文献已报道。根据以上模型的输出结果，LSSVM基于相关系数具有较高的性能，并且基于模型预测和相应的实际数据样本之间的相对误差的无穷不确定性小于15％。这项研究的结果表明，LSSVM方法在预测因水垢沉积而导致的渗透率降低方面是有用的，并且可以通过更好的方法更可靠地了解水驱对地层损害的影响，而无需进行昂贵的实验室测量。LSSVM具有基于相关系数的高性能，并且基于模型预测与相应实际数据样本之间的相对误差的无穷不确定性小于15％。这项研究的结果表明，LSSVM方法在预测因水垢沉积而导致的渗透率降低方面是有用的，并且可以通过更好的方法更可靠地了解水驱对地层损害的影响，而无需进行昂贵的实验室测量。LSSVM具有基于相关系数的高性能，并且基于模型预测与相应实际数据样本之间的相对误差的无穷不确定性小于15％。这项研究的结果表明，LSSVM方法在预测因水垢沉积而导致的渗透率降低方面是有用的，并且可以通过更好的方法更可靠地了解水驱对地层损害的影响，而无需进行昂贵的实验室测量。