Machine learning-based models for predicting permeability impairment due to scale deposition
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ORIGINAL PAPER-PRODUCTION ENGINEERING
Machine learning‑based models for predicting permeability impairment due to scale deposition Mohammadali Ahmadi1 · Zhangxin Chen1 Received: 21 April 2020 / Accepted: 20 June 2020 © The Author(s) 2020
Abstract 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. Keywords Machine learning · Data analytics · Support vector machine · Porous media · Formation damage · Scale deposition
Introduction Detrimental mineral deposition in formations that are operated to produce oil is known as one of the most problematic issues in the petroleum industry, especially when scales of barium and calcium sulfate cause a significant reduction in permeability due to pore throat plugs in reservoir rocks. Besides, these scales can adversely affect the productivity of wells through blocking tubing and casings (Boon et al. 1983; Cusack et al. 1987; Ahmed 2004). A mineral deposition is heavily and strongly influenced by a variety of parameters such as temperature fluctuation, pressure reduction, and getting mixed incompatible waters (Bertero et al. 1988; BinMerdhah et al. 2010; Moghadasi 2004). * Mohammadali Ahmadi [email protected] 1
Department of Chemical and Petroleum Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB T2N1T4, Canada
Moreover, deposition of sulfate scales is mainly caused by the injection of seawater saturated with sulfate anion into a formation containing high calcium, strontium, and barium cations for water flooding (Crabtree et al. 1999; Moghadasi et al. 2004; Frenier and Ziauddin 2008; Khatami et al. 2010; McElhiney 2001; Collin
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