Calcium carbonate scale prediction and inhibition in hydrocarbon wells using machine learning

Abstract

Methods for prediction and inhibition of calcium carbonate scale in hydrocarbon wells using machine learning include extracting training data including parameters from aqueous samples. Each aqueous sample is collected from a respective hydrocarbon well. The training data is classified in accordance with hydrocarbon production conditions of each hydrocarbon well. The classified training data is labeled in accordance with whether calcium carbonate scale has formed in each aqueous sample within a particular time period. A feature vector is determined from the labeled training data based on the parameters extracted from each aqueous sample. The feature vector is indicative of whether the respective hydrocarbon well contains calcium carbonate scale. A trained machine learning model is generated, wherein the machine learning model is trained based on the feature vector, to predict a number of the hydrocarbon wells containing calcium carbonate scale within the particular time period.