A robust fuzzy logic-based model for predicting the critical total drawdown in sand production in oil and gas wells

• Published in: PloS one Vol. 16; no. 4; p. e0250466
• Main Authors: Alakbari, Fahd Saeed, Mohyaldinn, Mysara Eissa, Ayoub, Mohammed Abdalla, Muhsan, Ali Samer, Hussein, Ibnelwaleed A
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 26.04.2021; Public Library of Science (PLoS)
• Subjects: Biology and Life Sciences; Computer and Information Sciences; Engineering and Technology; Fuzzy algorithms; Fuzzy logic; Fuzzy systems; Medicine and Health Sciences; Physical Sciences; Research and Analysis Methods; Malaysia
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0250466

Sand management is essential for enhancing the production in oil and gas reservoirs. The critical total drawdown (CTD) is used as a reliable indicator of the onset of sand production; hence, its accurate prediction is very important. There are many published CTD prediction correlations in literature. However, the accuracy of most of these models is questionable. Therefore, further improvement in CTD prediction is needed for more effective and successful sand control. This article presents a robust and accurate fuzzy logic (FL) model for predicting the CTD. Literature on 23 wells of the North Adriatic Sea was used to develop the model. The used data were split into 70% training sets and 30% testing sets. Trend analysis was conducted to verify that the developed model follows the correct physical behavior trends of the input parameters. Some statistical analyses were performed to check the model's reliability and accuracy as compared to the published correlations. The results demonstrated that the proposed FL model substantially outperforms the current published correlations and shows higher prediction accuracy. These results were verified using the highest correlation coefficient, the lowest average absolute percent relative error (AAPRE), the lowest maximum error (max. AAPRE), the lowest standard deviation (SD), and the lowest root mean square error (RMSE). Results showed that the lowest AAPRE is 8.6%, whereas the highest correlation coefficient is 0.9947. These values of AAPRE (<10%) indicate that the FL model could predicts the CTD more accurately than other published models (>20% AAPRE). Moreover, further analysis indicated the robustness of the FL model, because it follows the trends of all physical parameters affecting the CTD.