A decision tree model for accurate prediction of sand erosion in elbow geometry

Alakbari F.S. Mohyaldinn M.E. Ayoub M.A. Salih A.A. Abbas A.H.
July 2023 Elsevier Ltd

Heliyon
2023 #9 Issue 7

Erosion of piping components, e.g., elbows, is a hazardous phenomenon that frequently occurs due to sand flow with fluids during petroleum production. Early prediction of the sands erosion rate (ER) is essential for ensuring a safe flow process and material integrity. Some models have been applied to determine the ER of the sand in the literature. However, these models have been created based on specific data to require a model for application to wide-range data. Moreover, the previous models have not studied relationships between independent and dependent variables. Thus, this research aims to use machine learning techniques, namely linear regression and decision tree (DT), to predict the ER robustly. The optimum model, the DT model, was evaluated using various trend analysis and statistical error analyses (SEA) techniques, namely the correlation coefficient (R). The evaluation results proved proper physical behavior for all independent variables, along with high accuracy and the DT model robustness. The proposed DT method can accurately predict the ER with R of 0.9975, 0.9911, 0.9761, and 0.9908, AAPRE of 5.0%, 6.27%, 6.26%, and 5.5%, RMSE of 2.492E-05, 6.189E-05, 9.310E-05, and 5.339E-05, and STD of 13.44, 6.66, 8.01, and 11.44 for the training, validation, testing, and whole datasets, respectively. Hence, this study delivers an effective, robust, accurate, and fast prediction tool for ER determination, significantly saving the petroleum industrys cost and time.

Data-driven methods , Decision tree , Erosion rate , Machine learning , Sand production

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Petroleum Engineering Department, Universiti Teknologi PETRONAS, Perak Darul Ridzuan, Bandar Seri Iskandar, 32610, Malaysia
Institute of Hydrocarbon Recovery, Universiti Teknologi PETRONAS, Perak Darul Ridzuan, Bandar Seri Iskandar, 32610, Malaysia
School of Mining and Geosciences, Nazarbayev University, Nur Sultan, 010000, Kazakhstan

Petroleum Engineering Department
Institute of Hydrocarbon Recovery
School of Mining and Geosciences

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