Comparison of accuracy and computational performance between the machine learning algorithms for rate of penetration in directional drilling well

• Main Authors: Omid Hazbeh, Saeed Khezerloo-ye Aghdam, Hamzeh Ghorbani, Nima Mohamadian, Mehdi Ahmadi Alvar, Jamshid Moghadasi
• Format: Article
• Language: English
• Published: KeAi Communications Co., Ltd. 2021-09-01
• Series: Petroleum Research
• Subjects: Rate of penetration; Artificial intelligence; Directional drilling; MLP; Prediction
• Online Access: http://www.sciencedirect.com/science/article/pii/S2096249521000089

Oil and gas reservoirs are of the main assets of countries possessing them. Production from these reservoirs is one of the main concerns of engineers, which can be achieved by drilling oil and gas reservoirs. Construction of hydrocarbon wells is one of the most expensive operations in the oil industry. One of the most important parameters affecting drilling cost is the rate of penetration (ROP). This paper predicts the rate of penetration using artificial intelligence and hybrid models on Kaboud oil field well #7 in the directional stage. In this study, different models were constructed through various approaches based on 1878 dataset obtained from drilling operation in the well#7. Then, the accuracy of the constructed models was compared with each other. It was found that the MLP-ABC algorithm predicts the rate of penetration more accurately, by far, as compared with other methods. The MLP-ABC algorithm achieves impressive ROP prediction accuracy (RMSE = 0.007211 m/h; AAPD = 0.1871%; R2 = 1.000 for the testing subset). Consequently, it can be concluded that this method is applicable to predict the drilling rate in that oilfield.