An Accurate Critical Total Drawdown Prediction Model for Sand Production: Adaptive Neuro-fuzzy Inference System (ANFIS) Technique

Fahd Saeed Alakbari, Muhammad Jawad Khan, Mysara Eissa Mohyaldinn, Mohammed Abdalla Ayoub, Ibnelwaleed A. Hussein, Ali Samer Muhsan, Abdullah Abduljabbar, Azza Hashim Abbas

2024

Full text Open access: Hybrid Metadata (CSL-JSON)
DOI 10.1007/s13369-024-09556-8
Data confidence 85 high
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Abstract

Abstract 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%.