Quantum mechanics

Sediment

Algorithm

Scale (ratio)

Engineering

Biology

Permeability (electromagnetism)

Geotechnical engineering

Genetic programming

Support vector machine

Water injection (oil production)

Deposition (geology)

Petroleum engineering

Relative permeability

Offshore geotechnical engineering

Biochemistry

Computer science

Correlation coefficient

Geology

Porosity

Paleontology

Physics

Produced water

Membrane

Least squares support vector machine

Water flooding

Artificial intelligence

Chemistry

Approximation error

Artificial neural network

Biological system

Machine learning

Journal of Petroleum Exploration and Production Technology

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.

Machine learning-based models for predicting permeability impairment due to scale deposition