Design of drilling fluids, spacers, cement slurries, and fracturing fluids is often done by trial and error in the laboratory. In the first step, the required properties of these fluids are categorized and then efforts will be started with a rough idea of the optimal composition. This first guess usually depends on the experience of the laboratory analyst or fluid engineer. Afterward, the trial-and-error testing starts, and it continues until the fluid design moves closer to the desired fluid criteria. There are several test data that would not be used in this method, and it is difficult to digest a large amount of information by the user. Trial and error could be time-consuming, very costly, and misleading. Today, there is a need for an intelligent system that uses all the available data (big data), even if the data sets are not close to the desired goal, and offers insights for fluid designs.
This paper conducted a study on the application of machine-learning-based methodologies, including Gaussian-process regression (GPR) and artificial neural networks (ANNs), to reduce the costs of testing, integrate available experimental data, and eliminate the need for personnel supervision. These practical nonlinear-regression methods empower efficient and fast prediction tools that do not require including complex physics of the underlying system while integrating all available data from different sources. GPR, which is also known as Kriging in geostatistics literature, has exceptional advantages over traditional regression methods because it does not require a known form for regression function and also has the capability of determining the estimation error and the confidence interval. This machine-learning-based tool offers insights for intelligent fluid design and could reduce costs.