Development of Artificial Neural Network Models for the Simulation of a CaCO₃ Scale Formation Process in the Presence of Monoethylene Glycol (MEG) in Dynamic Tube Blocking Test Equipment

Abstract

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 R 2 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 R 2 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.

PetroPhase2021 Virtual Special Issue

“…15 Ferreira and Santos developed artificial neural network models to simulate the calcium carbonate scaling formation process in the presence of gas hydrate inhibitor monoethylene glycol. 16 Arenas-Blanco and Meji ́a-Ospino evaluated a sulfonated graphene oxide (SGO) nanofluid on the interfacial properties of light and medium crude oil−water systems. They showed that the sulfonation process can improve the thermal stability and formation of stable emulsions.…”

“…Savulescu and co-workers used a low-field nuclear magnetic resonance (NMR) technique to characterize the evolution of wax mobility during wax crystallization by measuring the relaxation time . Ferreira and Santos developed artificial neural network models to simulate the calcium carbonate scaling formation process in the presence of gas hydrate inhibitor monoethylene glycol . Arenas-Blanco and Mejía-Ospino evaluated a sulfonated graphene oxide (SGO) nanofluid on the interfacial properties of light and medium crude oil–water systems.…”

Research on the Scaling Mechanism and Countermeasures of Tight Sandstone Gas Reservoirs Based on Machine Learning

“…Therefore, an increasing number of scholars are exploring how to leverage machine learning to fully utilize the massive amount of data from oil fields to address the increasingly complex on-site production conditions. Researchers such as Zhao [23], Gabetta, G. [24], Paulo A. Paz [25], Wang [26], and Bruno X. Ferreira [27] have integrated machine learning into scaling considerations and achieved significant results.…”