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 (BaSO) 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) 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.

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 10 times higher than the AC for both UNSCEAR suggested values and non-HBRA.

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.

Determination of eccentric deposition thickness on offshore horizontal pipes by gamma-ray densitometry and artificial intelligence technique.

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.

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.

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 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.