Calculation of volume fractions regardless scale deposition in the oil industry pipelines using feed-forward multilayer perceptron artificial neural network and MCNP6 code.

During the production of oil and gas, barium sulfate (BaSO4) 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 (BaSO4) 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.

Lifetime cancer risk increase due to consumption of some foods from a High Background Radiation Area.

Activity concentration (AC) in foods produced and commonly consumed in a High Background Radiation Area (HBRA) was analyzed. The AC were obtained by spectrophotometry and by the radiochemical separation method. The AC were up to 104 times higher than the AC for both UNSCEAR suggested values and non-HBRA. It was noted that the lifetime cancer risk was increased in 4 decimal places, taking the risk from the "statistically negligible range" (<10-6) to "middle range" (between 10-4 and 10-6).

Monitoring system of oil by-products interface in pipelines using the gamma radiation attenuation.

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%.

Flow regime and volume fraction identification using nuclear techniques, artificial neural networks and computational fluid dynamics.

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%.

Comparison between codes MCNPX and Gate/Geant4 in volume fraction studies.

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.