ABSTRACT
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, a241Am 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.
ABSTRACT
The Nuclear Engineering Department of the Military Institute of Engineering (SE/7-IME) is designing and simulation a neutron irradiator with 1 Ci of 241Am-9Be source. The objective of this irradiator is to generate a flux of neutrons to be used in research and teaching maintaining, for purposes of radiological protection, the rate of ambient dose equivalent, H*(10), below 10 µSv/h at 30 cm from the surface. This paper presents the proposed irradiator, values of H*(10) at different distances from the irradiator and the neutron flux in different points of the beam irradiation, all calculated using the MCNPX code.
ABSTRACT
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 137Cs and three properly positioned 2â³×2â³ 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, BaSO4. 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.
ABSTRACT
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 137Cs 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%.
