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.

Abstract: Methods for prediction and inhibition of calcium carbonate scale in hydrocarbon wells include generating a confusion matrix for each of several machine learning methods. The confusion matrix is generated by executing a machine learning model trained to predict a number of hydrocarbon wells containing scale. A probabilistic model is generated indicating an uncertainty associated with using the machine learning method to predict the number of hydrocarbon wells containing scale based on the confusion matrix. A cost metric is generated indicating a cost of implementing a scale inhibition program using the machine learning method based on the probabilistic model. A machine learning method having a lowest cost metric is selected. It is determined whether the lowest cost metric is less than a base cost of implementing the scale inhibition program using a scale inhibition chemical.

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.