Real-Time Prediction of Rate of Penetration in S-Shape Well Profile Using Artificial Intelligence Models

Abstract

Rate of penetration (ROP) is defined as the amount of removed rock per unit area per unit time. It is affected by several factors which are inseparable. Current established models for determining the ROP include the basic mathematical and physics equations, as well as the use of empirical correlations. Given the complexity of the drilling process, the use of artificial intelligence (AI) has been a game changer because most of the unknown parameters can now be accounted for entirely at the modeling process. The objective of this paper is to evaluate the ability of the optimized adaptive neuro-fuzzy inference system (ANFIS), functional neural networks (FN), random forests (RF), and support vector machine (SVM) models to predict the ROP in real time from the drilling parameters in the S-shape well profile, for the first time, based on the drilling parameters of weight on bit (WOB), drillstring rotation (DSR), torque (T), pumping rate (GPM), and standpipe pressure (SPP). Data from two wells were used for training and testing (Well A and Well B with 4012 and 1717 data points, respectively), and one well for validation (Well C) with 2500 data points. Well A and Well B data were combined in the training-testing phase and were randomly divided into a 70:30 ratio for training/testing. The results showed that the ANFIS, FN, and RF models could effectively predict the ROP from the drilling parameters in the S-shape well profile, while the accuracy of the SVM model was very low. The ANFIS, FN, and RF models predicted the ROP for the training data with average absolute percentage errors (AAPEs) of 9.50%, 13.44%, and 3.25%, respectively. For the testing data, the ANFIS, FN, and RF models predicted the ROP with AAPEs of 9.57%, 11.20%, and 8.37%, respectively. The ANFIS, FN, and RF models overperformed the available empirical correlations for ROP prediction. The ANFIS model estimated the ROP for the validation data with an AAPE of 9.06%, whereas the FN model predicted the ROP with an AAPE of 10.48%, and the RF model predicted the ROP with an AAPE of 10.43%. The SVM model predicted the ROP for the validation data with a very high AAPE of 30.05% and all empirical correlations predicted the ROP with AAPEs greater than 25%.

Interpretable Feature Construction and Incremental Update Fine-Tuning Strategy for Prediction of Rate of Penetration

“…Artificial neural networks (ANNs) have been widely used, demonstrating promising results in ROP prediction [16,17]. Other machine-learning models, such as random forest [16,18,19], extreme gradient boosting [14,20], long and short-term memory (LSTM) networks [21,22] and hybrid networks [23,24], have been applied to predict ROP based on historical drilling data. Bizhani et al [25] addressed the issue of uncertainty in data-driven models by developing a Bayesian neural network model for predicting ROP.…”

Artificial Intelligence Model in Predicting Geomechanical Properties for Shale Formation: A Field Case in Permian Basin

“…Among them, one of the fastestgrowing sectors is the petroleum industry. This tool is predominantly used for the estimation and optimization of petrophysical properties [15][16][17][18][19], geomechanical properties [20][21][22], reservoir fluid properties [23][24][25][26][27][28], and parameters related to drilling [29][30][31][32][33][34][35][36][37]. Researchers employed different ML methods, including an artificial neural network, fuzzy logic, functional network, etc.…”