A Novel Artificial Neural Network-Based Correlation for Evaluating the Rate of Penetration in a Natural Gas Bearing Sandstone Formation: A Case Study in a Middle East Oil Field

• Published in: Journal of sensors Vol. 2022; pp. 1 - 14
• Main Authors: Al-AbdulJabbar, Ahmad, Mahmoud, Ahmed Abdulhamid, Elkatatny, Salaheldin, Abughaban, Mahmoud
• Format: Journal Article
• Language: English
• Published: New York Hindawi 13.01.2022; Hindawi Limited
• Subjects: Accuracy; Artificial neural networks; Discriminant analysis; Drilling; Empirical equations; Gas bearings; Machine learning; Mechanical properties; Natural gas; Neural networks; Oil fields; Penetration; Petroleum industry; Regression analysis; Sandstone; Training; Viscosity; Wells
• ISSN: 1687-725X; 1687-7268
• DOI: 10.1155/2022/9444076

This study presented an empirical correlation to estimate the drilling rate of penetration (ROP) while drilling into a sandstone formation. The equation developed in this study was based on the artificial neural networks (ANN) which was learned to assess the ROP from the drilling mechanical parameters. The ANN model was trained on 630 datapoints collected from five different wells; the suggested equation was then tested on 270 datapoints from the same training wells and then validated on three other wells. The results showed that, for the training data, the learned ANN model predicted the ROP with an AAPE of 7.5%. The extracted equation was tested on data gathered from the same training wells where it estimated the ROP with AAPE of 8.1%. The equation was then validated on three wells, and it determined the ROP with AAPEs of 9.0%, 10.7%, and 8.9% in Well-A, Well-B, and Well-D, respectively. Compared with the available empirical equations, the equation developed in this study was most accurate in estimating the ROP.