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During the production of oil and gas, barium sulfate (BaSO 4 ) scale occurs on the inner walls of the tubes leading the reduction of the internal diameter, making the fluid passage difficult and complicating the calculation of volume fractions of fluids. In this sense, this study presents a methodology for the development of volume fractions of fluids multiphase meters and the prediction of barium sulfate (BaSO 4 ) scale thickness. The spectra obtained by two NaI(Tl) detectors that record the transmitted and scattered beams are used as input data for the artificial neural network without the need of any parametrization method. Theoretical models for annular flow regime were developed using MCNP6 code. Different volume fractions and scale thickness values of oil-water-gas were generated as a data set to train and evaluate the neural network. The results indicate that it is possible to calculate the volume fraction regardless the scale thickness in offshore oil industry pipes. More than 88% of the results showed errors below 5% for all investigated samples., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

In the oil production industry, water is used as a fluid injected into the well to raise the oil when the well is depressurized. Water thus produced presents variations in the concentrations of dissolved salts, as there is a mixture of different types of water, related to its origin (such as connate water, sea water). Because it is reused in oil production, water needs to be monitored to maintain the standard suitable for its use as it can be hypersaline, contributing to the encrustation of pipes and contamination of underground water reservoirs. In this study, a noninvasive method was developed to determine the salt concentration in seawater. The method uses a detection system that contains a NaI(Tl) detector, a 241 Am source, and a sample holder to measure the mass attenuation coefficient of saltwater samples. For validation, the same setup was also simulated using the MCNPX code. Saltwater samples with different concentrations of NaCl and KBr were used as a proxy for seawater. The mass attenuation coefficients for the simulation exhibited the smallest relative errors (up to 6.2%), and the experimental ones exhibited the highest relative errors (up to 25%) when compared with theoretical values., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

The extraction of oil is accompanied by water and sediments that, mixed with the oil, cause the formation of scale depositions in the pipelines walls promoting the reduction of the inner diameter of the pipes, making it difficult for the fluids to pass through interest. In this sense, there is a need to control the formation of these depositions to evaluate preventive and corrective measures regarding the waste management of these materials, as well as the optimization of oil extraction and transport processes. Noninvasive techniques such as gamma transmission and scattering can support the determination of the thickness of these deposits in pipes. This paper presents a novel methodology for prediction of scale with eccentric deposition in pipes used in the offshore oil industry and its approach is based on the principles of gamma densitometry and deep artificial neural networks (DNNs). To determine deposition thicknesses, a detection system has been developed that utilizes a 1 mm narrow beam geometry of collimation aperture comprising a source of 137 Cs and three properly positioned 2″×2″ NaI(Tl) detectors around the system, pipe-scale-fluid. Crude oil was considered in the study, as well as eccentric deposits formed by barium sulfate, BaSO 4 . The theoretical models adopted a static flow regime and were developed using the MCNPX mathematical code and, secondly, used for the training and testing of the developed DNN model, a 7-layers deep rectifier neural network (DRNN). In addition, the hyperparameters of the DRNN were defined using a Baysian optimization method and its performance was validated via 10 experiments based on the K-Fold cross-validation technique. Following the proposed methodology, the DRNN was able to achieve, for the test sets (untrained samples), an average mean absolute error of 0.01734, mean absolute relative error of 0.29803% and R2 Score of 0.9998813 for the scale thickness prediction and an average accuracy of 100% for the scale position prediction. Therefore, the results show that the 7-layers DRNN presents good generalization capacity and is able to predict scale thickness with great precision, regardless of its position inside the tube., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Knowing the volume fraction in a multiphase flow is of fundamental importance in predicting the performance of many systems and processes, it has been possible to model an experimental apparatus for volume fraction studies using Monte Carlo codes. Artificial neural networks have been applied for the recognition of the pulse height distributions in order to obtain the prediction of the volume fractions of the flow. In this sense, some researchers are unsure of which Monte Carlo code to use for volume fractions studies in two-phase flows. This work aims to model a biphasic flow (water and air) experiment in a stratified regime in two Monte Carlo-based codes (MCNP-X and Gate/Geant4), and to verify which one has the greatest benefits for researchers, focusing on volume fractions studies., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

This paper presents a methodology to precise identify the interface region, which is formed in the transport of petroleum by-products in polyducts, using gamma densitometry. The simulated geometry is compose for a collimated 137 Cs source and a NaI(Tl) detector to measure the transmitted beam. The modeling was validated experimentally on stratified flow regime using water and oil. The different volume fractions were calculated using the MCNPX code in order to determine the region interface with an accuracy of 1%., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Knowledge of the flow regime and the volume fraction in multiphase flow is of fundamental importance in predicting the performance of many systems and processes. This study is based on gamma-ray pulse height distribution pattern recognition by means of an artificial neural network. The detection system uses appropriate one narrow beam geometry, comprising a gamma-ray source and a NaI(Tl) detector. The models for annular and stratified flow regimes were developed using MCNPX code, in order to obtain adequate data sets for training and testing of the artificial neural network. Several experiments were carried out in the stratified flow regime to validate the simulated results. Finally, Ansys-CFX was used as computational fluid dynamics software to simulate two different volume fractions, which were modeled and transformed in voxels and transferred to MCNPX code. The use of computational fluid dynamics is of great importance, because it brings the studies closer to the reality. All flow regimes were correctly recognized and the volume fractions were appropriately predicted with relative errors less than 1.1%., Competing Interests: Declaration of competing interest There were no conflicts and all comments were answered., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Stirrers and mixers are highly used in chemical, food, pharmaceutical, cosmetic, concrete industries and others. During the fabrication process, the equipment may fail to appropriately stir or mix the solution. Besides that, it is also important to determine when the right homogeneity of the mixture is attained. Thus, it is very important to have a diagnosis tool for these industrial units to assure the quality of the product and maintain market competitiveness. Nuclear techniques, such as gamma densitometry, are widely used in industry to overcome a sort of difficulties, as they are minimally non-invasive techniques. This paper presents a method based on the principles of the radioactive particle tracking technique to predict the instantaneous position of a radioactive particle to monitor a concrete mixture inside an industrial unit by means of Monte Carlo method and artificial neural network. Counts obtained by an array of detectors properly positioned around the mixing canister will be correlated to each other, by means of an appropriate mathematical search location algorithm, in order to predict the instantaneous positions occupied by an inserted radioactive particle. The simulation consists of a detection geometry of eight NaI(Tl) scintillator detectors, a 662 keV 137 Cs point source with isotropic emission of gamma-rays and a polyvinyl chloride tank. At first, the tank is air filled and, afterwards, filled with concrete made with Portland cement. The modeling of the detection system is performed using the MCNPX code. For both medium, the correlation coefficient was 0.99 for all coordinates, which indicates that this methodology could be a good tool to evaluate industrial mixers., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Scale can be defined as chemical compounds that are inorganic, initially insoluble, and precipitate accumulating on the internal walls of pipes, surface equipment, and/or parts of components involved in the production and transport of oil. These compounds, when precipitating, cause problems in the oil industry and consequently result in losses in the optimization of the extraction process. Despite the importance and impact of the precipitation of these compounds in the technological and economic scope, there remains difficulty in determining the methods that enable the identification and quantification of the scale at an initial stage. The use of gamma transmission technique may provide support for a better understanding of the deposition of these compounds, making it a suitable tool for the noninvasive determination of their deposition in oil transport pipelines. The geometry used for the scale detection includes a 280-mm diameter steel tube containing barium sulphate (BaSO 4 ) scale ranging from 0.5 to 6 cm, a gamma radiation source with divergent beam, and a NaI(Tl) 2 × 2″ scintillation detector. The opening size of the collimated beam was also evaluated (2-7 mm) to quantify the associated error in calculating the scale. The study was done with computer simulation, using the MCNP-X code, and the results were validated using analytical equations. Data obtained by the simulation were used to train an artificial neural network (ANN), thereby making the study system more complex and closer to the real one. The input data provided for the training, testing, and validation of the network consisted of pipes with 4 different internal diameters (D1, D2, D3, and D4) and 14 different scale thicknesses (0.5 to 7 cm, with steps of 0.5 cm). The network presented generalization capacity and good convergence, with 70% of cases with less than 10% relative error and a linear correlation coefficient of 0.994, which indicates the possibility of using this study for this purpose., (Copyright © 2018 Elsevier Ltd. All rights reserved.)