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

Fahd Saeed Alakbari, Mysara Eissa Mohyaldinn, Mohammed Abdalla Ayoub, Abdullah Abduljabbar Salih, Azza Hashim Abbas

Research output: Contribution to journalArticlepeer-review


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 sand's 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 industry's cost and time.

Original languageEnglish
Article numbere17639
Issue number7
Publication statusPublished - Jul 2023
Externally publishedYes


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

ASJC Scopus subject areas

  • General


Dive into the research topics of 'A decision tree model for accurate prediction of sand erosion in elbow geometry'. Together they form a unique fingerprint.

Cite this