An Accurate Critical Total Drawdown Prediction Model for Sand Production: Adaptive Neuro-fuzzy Inference System (ANFIS) Technique
Alakbari F.S. Mahmood S.M. Mohyaldinn M.E. Ayoub M.A. Hussein I.A. Muhsan A.S. Salih A.A. Abbas A.H.
April 2025Springer Nature
Arabian Journal for Science and Engineering
2025#50Issue 74993 - 5005 pp.
Sand production causes many problems in the petroleum industry. The sand production is predicted to control it in the early stages. Therefore, accurate prediction of sand production has been considered substantial in achieving successful sand control. Critical total drawdown (CTD) can indicate the sand production. The main drawback of the previous studies in predicting CTD is their lack of accuracy. Thus, this study aims to develop an accurate CTD estimation prediction model employing a trend analysis and adaptive neuro-fuzzy inference system (ANFIS). The method is chosen because of its higher performance; the model is built based on 23 published datasets from the Adriatic Sea. The developed ANFIS model is evaluated using various methods, namely, trend analyses. Trend analyses are conducted to show the effects of the features on the CTD to present the physical behavior. The model’s performance was also evaluated using statistical error analyses. In addition, the ANFIS and previously published models were assessed. The trend analyses show the correct relationship between all features and the CTD. In addition, the trend analyses for the previous models are discussed. The results show that the proposed ANFIS method outperforms published methods with an R of 0.9984 and an absolute average percentage relative error (AAPRE) of 4.293%.
Adaptive neuro-fuzzy inference system technique , ANFIS , Artificial intelligence , Critical total drawdown , Machine learning , Sand control
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Center of Flow Assurance, Institute of Subsurface Resources, Universiti Teknologi PETRONAS, Perak Darul Ridzuan,Bandar Seri Iskandar, 32610, Malaysia
Petroleum Engineering Department, Universiti Teknologi PETRONAS, Perak Darul Ridzuan,Bandar Seri Iskandar, 32610, Malaysia
Chemical and Petroleum Engineering Department, United Arab Emirates University, Al Ain, United Arab Emirates
Gas Processing Center, College of Engineering, Qatar University, P. O. Box 2713, Doha, Qatar
Department of Chemical Engineering, College of Engineering, Qatar University, P.O. Box 2713, Doha, Qatar
Mechanical Engineering Department, Universiti Teknologi PETRONAS, Perak Darul Ridzuan,Bandar Seri Iskandar, 32610, Malaysia
School of Mining and Geosciences, Nazarbayev University, Nur Sultan, 010000, Kazakhstan
Center of Flow Assurance
Petroleum Engineering Department
Chemical and Petroleum Engineering Department
Gas Processing Center
Department of Chemical Engineering
Mechanical Engineering Department
School of Mining and Geosciences
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