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

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.

Modelling and Simulating Eulerian Venturi Effect of SBM to Increase the Rate of Penetration with Roller Cone Drilling Bit

“…Previous research [19,27,32] has provided explanations for the effective review of the solid-liquid exchange coefficient, K sl . When the volume percentage of the liquid phase, a l > 0.8, then, K sl , is transformed from Equations ( 6)- (9). The current study does not, however, predict that the fluid will flow in a uniform laminar flow due to the rotating drill pipe and the release of drilling fluids [33] from the bit's nozzles to improve rate of penetration.…”

“…Interestingly, the cost-time index of drill operations is meant for drill managers operators to determine and devise magnificent means of optimizing cost and tim The impact of bit design elements might be comprehended and used to enhance bit hydraulics with the aid of computational methodologies. A computational technique [9][10][11][12][13] called computational fluid dynamics (CFD) is useful for modeling fluid flow phenomena in drill bit designs with complex geometries that involve bit rotation and multi-phase conditions downhole of the well. Through the application of CFD to improve drill bit design, drilling performance can be boosted.…”

Hybrid physics-machine learning models for predicting rate of penetration in the Halahatang oil field, Tarim Basin

“…Mendes 12 also presented a methodology based on a neural network model for ROP and a neuro-genetic adaptive controller to address the problem that relationships between operational variables affecting ROP are complex and not easily modeled. In addition, with the boom in ML algorithms approximately 2010, more and more ML methods are being used for ROP prediction, including Moran 13 , Arabjamaloei 14 , Esmaeili 15 , Ning 16 , Zare 17 , Bodaghi 18 , Hegde 19 , Mantha 20 , Hegde 21 , Anemangely 22 , Soares 7 , Sabah 23 , Felipe 2 , Korhan 24 , Li 25 , Mohammad 26 , Gan 27 , Hazbeh 28 , Salaheldin 29 , Zhang 30 , Ren 31 , Zhang 32 , Brenjkar 33 , Riazi 34 , Song 35 , Wang 36 , Mohammad 37 , Kaveh 38 and so on. Judging from the increasing number of articles published each year in recent years on the use of machine learning for ROP prediction, it can be amply demonstrated that ML methods are well suited for application in the field of ROP prediction.…”