Cuttings Transport Modeling in Wellbore Annulus in Oil Drilling Operation using Evolutionary Fuzzy System

Abstract

A difficult problem in drilling operation that concerns the very drilling parameters is the cutting transport process. Correct calculation of the cuttings concentration (hole cleaning efficiency) in the wellbore annulus using drilling variables such as the geometry of wellbore, rheology, and density of drilling fluid, and pump rate is very important for optimizing these variables. In this study, a hybrid evolutionary fuzzy system (EFS) using artificial intelligent (AI) techniques is presented for estimation of the cuttings concentration in oil drilling operation using operational drilling parameters. A well-organized genetic learning algorithm that computes fitness values by symbiotic evolution is used for extraction of the Takagi–Sugeno–Kang (TSK) type fuzzy rule-based system for the EFS. A determination coefficient (R2) of 0.877 together with a root mean square error (RMSE) of 1.4 between prediction and measured data for test data verified a very satisfactory model performance. Results confirmed that the estimation accuracy of the proposed EFS is better than other models such as Multiple Linear Regression (MLR), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) for hole cleaning modeling.

Keywords

• artificial intelligent methods
• drilling
• efs
• hole cleaning
• wellbore