Variable selection and uncertainty analysis of scale growth rate under pre-salt oil wells conditions using support vector regression

Abstract

The formation of inorganic scale, particularly calcium carbonate (CaCO3), is a persistent and one of the most serious and costly problems in the oil and gas industries. This event may cause partial to complete plugging, block valves, tubing and flowlines, and then reduce the production rates. This article proposes the use of support vector regression to build a nonlinear mapping between a set of variables (surface cladding, material, temperature, pressure, brine composition, and fluid velocity) and the scale build-up. The support vector regression is fed with data gathered from laboratory tests carried out on coupons that simulate realistic downhole conditions encountered in oil well bores from the pre-salt fields in Brazil. The proposed failure prediction framework is comprehensive as it entails the stages of hyperparameter tuning, variable selection, and uncertainty analysis, which are addressed by a combination of particle swarm optimization and bootstrap with support vector regression. The obtained results suggest that the bootstrapped particle swarm optimization + support vector regression is a valuable tool that may be used to support condition-based maintenance-related decisions.