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Development of Artificial Neural Network Models for the Simulation of a CaCO 3 Scale Formation Process in the Presence of Monoethylene Glycol (MEG) in Dynamic Tube Blocking Test Equipment

2022; American Chemical Society; Volume: 36; Issue: 4 Linguagem: Inglês

10.1021/acs.energyfuels.1c03364

1520-5029

Bruno X. Ferreira, Carlos Roberto Hall Barbosa, João Cajaiba, Vinícius Kartnaller, Brunno Ferreira dos Santos,

Hydraulic Fracturing and Reservoir Analysis

The precipitation of gas hydrate and inorganic salts (scale) during oil and gas production represents a significant flow assurance hindrance for the industry. Chemical inhibitors can prevent the fouling process, but specific inhibitors to address a problem could result in synergistic or adverse effects. Simulations in tubes and pipelines are necessary to understand these behaviors by assessing the scaling tendency of the water. The primary objective of this study was to create models using an artificial neural network (ANN) of the multilayer perceptron (MLP) type for the simulation of the calcium carbonate scaling formation process in the presence of monoethylene glycol (MEG), a typical gas hydrate inhibitor. A database was obtained from 38 tube blocking test (TBT) experiments with different conditions. The models were developed using MATLAB R2020a, splitting the database into two groups on the ratio of 70:30, respectively, train and test ones, preserving the time dependency of the differential pressure (ΔP) data. The ANNs were created using six inputs (temperature, pressure, calcium and bicarbonate concentrations, MEG concentration, and ΔP measured at a selected time) and one output (ΔP measured at a later time). The goal was to explore how monitoring the conditions in a pipeline can predict the evolution of the scaling process. We investigated two scenarios for the ΔP prediction: a near future (one step ahead) and a far future (five steps ahead). The MLP models demonstrated high performance, with an R2 higher than 92.9% for both training and test groups for both prediction horizons. Then, these models were tested with a second data group to evaluate their applicability to control the systems. The best models showed good scaling prediction, with R2 ranging from 80.0% to 99.9%. These results represent a promising step toward applying machine learning techniques to simulate and predict scaling tendencies in controlled pipelines.