Modelling rate of penetration in drilling operations using RBF, MLP, LSSVM, and DT models

Mohsen Riazi; Hossein Mehrjoo; Reza Nakhaei; Hossein Jalalifar; Mohammadhadi Shateri; Masoud Riazi; Mehdi Ostadhassan; Abdolhossein Hemmati-Sarapardeh

doi:10.1038/s41598-022-14710-z

Modelling rate of penetration in drilling operations using RBF, MLP, LSSVM, and DT models

Sci Rep. 2022 Jul 8;12(1):11650. doi: 10.1038/s41598-022-14710-z.

Authors

Mohsen Riazi^{1

2}, Hossein Mehrjoo¹, Reza Nakhaei², Hossein Jalalifar¹, Mohammadhadi Shateri³, Masoud Riazi², Mehdi Ostadhassan^{4

5

6}, Abdolhossein Hemmati-Sarapardeh⁷

Affiliations

¹ Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
² Enhanced Oil Recovery (EOR) Research Center, IOR/EOR Research Institute, Shiraz University, Shiraz, Iran.
³ Département de génie des systèmes, École de technologie supérieure, Montreal, QC, Canada.
⁴ Key Laboratory of Continental Shale Hydrocarbon Accumulation and Efficient Development, Ministry of Education, Northeast Petroleum University, Daqing, 163318, China.
⁵ Institute of Geosciences, Marine and Land Geomechanics and Geotectonics, Christian-Albrechts-Universität, 24118, Kiel, Germany.
⁶ Department of Geology, Ferdowsi University of Mashhad, Mashhad, Iran.
⁷ Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran. hemmati@uk.ac.ir.

Abstract

One of the most important problems that the drilling industry faces is drilling cost. Many factors affect the cost of drilling. Increasing drilling time has a significant role in increasing drilling costs. One of the solutions to reduce drilling time is to optimize the drilling rate. Drilling wells at the optimum time will reduce the time and thus reduce the cost of drilling. The drilling rate depends on different factors, some of which are controllable and some are uncontrollable. In this study, several smart models and a correlation were proposed to predict the rate of penetration (ROP) which is very important for planning a drilling operation. 5040 real data points from a field in the South of Iran have been used. The ROP was modelled using Radial Basis Function, Decision Tree (DT), Least Square Vector Machine (LSSVM), and Multilayer Perceptron (MLP). Bayesian Regularization Algorithm (BRA), Scaled Conjugate Gradient Algorithm and Levenberg-Marquardt Algorithm were employed to train MLP and Gradient Boosting (GB) was used for DT. To evaluate the accuracy of the developed models, both graphical and statistical techniques were used. The results showed that DT-GB model with an R² of 0.977, has the best performance, followed by LSSVM and MLP-BRA with R² of 0.971 and 0.969, respectively. Aside from that, the proposed empirical correlation has an acceptable accuracy in spite of simplicity. Moreover, sensitivity analysis illustrated that depth and pump pressure have the highest effects on ROP. In addition, the leverage approach approved that the developed DT-GB model is valid statistically and about 1% of the data are suspected or out of the applicability domain of the model.