The precipitation of inorganic scales in the oil and gas industry has been identified as a major issue for flow assurance and the optimization of oil and gas fields due to the damage that these precipitations can cause in reservoirs, well completions, and surface facilities. On the other hand, predicting these precipitations has always been challenging for engineers of petroleum, production, and production facilities. Although many commercial computer programs in the industry can predict inorganic scale precipitations with some accuracy, the majority have many limitations that can negatively impact prediction performance.

Machine learning (ML) has received substantial attention in the oil and gas industry in recent years. The purpose of this study is to investigate the use of machine learning algorithms as a new approach to predicting inorganic scale precipitations in oil and gas carbonate formations.

The methodology of the current study consists of gathering input and output data, such as pressure, temperature, artificial lifting type, target formation, water ionic composition, pH, TDS, and whether or not each well tends to precipitate the inorganic scale. The algorithms chosen for prediction are Naive Bayes (NA), Neural Network classifier (NN), Logistic Regression (LR), Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), and K- Nearest Neighbors (KNN), and they will be evaluated based on accuracy and other classification performance metrics.

The results of the models show that SVM, DT, and KNN are the best classifiers in terms of prediction accuracy scores with around 83%. Furthermore, a decision tree chart was created based on the Decision Tree (DT) model and can be used to examine the scale precipitation tendency for any future water sample. The chart is validated using real well cases from the same field, demonstrating a match between the predicted class (the well possesses or does not possess a high potential to precipitate inorganic scale) and the data collected in the well's interventions history reports. Based on the DT model, the artificial lifting method, target formation, pressure at the pump depth, and SO42-, HCO3- ionic compositions are found to be the strongest features that play a significant role in the scale precipitations in the studied field.

Implementing the proposed model will lead to many benefits, including properly employed well intervention resources, reduced oil deferment due to pump failures caused by scale precipitation, and reduced budget overspending entailed by unexpected failures in pumps, valves, or even surface facilities.

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