Optimization of a flow regime identification system and prediction of volume fractions in three-phase systems using gamma-rays and artificial neural network

Volume fraction detection in multiphase systems using neutron activation analysis and artificial neural network

This study presents an application of an Artificial Neural Network (ANN) to detect fluids in an annular flow regime using Prompt-Gamma Neutron Activation Analysis (PGNAA). The ANN was trained using gamma-ray spectra resulting from neutron interactions with chemical elements found in fluids typical of multiphase flow in oil exploration. These spectra were generated through mathematical simulation using the MCNP6 Monte Carlo computer code to model nuclear particle transport. A241Am-Be polyenergetic neutron source was simulated for these calculations. Several combinations of fluid fractions were developed to create a dataset used for both training and evaluation of the ANN. The ANN demonstrated robust generalization capabilities by accurately predicting the volume fraction of the three investigated fluids (saltwater, oil, and gas), even for cases not included in the training phase. The combination of ANN and PGNAA proved effective for analyzing multiphase systems, with over 92% of all showing errors of less than 5%.

Development of analytical equations for void fraction in biphasic systems using gamma radiation and MCNP6 code

This study presents the development of mathematical equations for calculating void fractions in pipes using gamma densitometry. A traditional measurement setup, consisting of a137Cs point source and a NaI(Tl) scintillator detector, was simulated using the Monte Carlo method via the MCNP6 code. To validate the proposed equations, water-gas biphasic models were simulated in tubes with square and cylindrical cross-sections, varying diameters, and radiation sources (241Am, 137Cs, 60Co) through gamma-ray transmission. A comparative analysis with existing equations from the literature was conducted. The void fractions, determined from the transmission photopeak, were in close agreement with the actual values. The proposed equations demonstrated a maximum mean relative error of 0.21% for cylindrical tubes in stratified and annular flow regimes.

ACO-based feature selection and neural network modeling for accurate gamma-radiation based pipeline monitoring in the oil industry

This work presents a novel technique to improve oil pipeline monitoring capabilities, a vital activity in the oil and gas sector. Using Monte Carlo simulations, the work meticulously records data from a pipeline testing environment with various petroleum products and volume ratios. We apply the presented technique to mix four petroleum products-ethylene glycol, gasoline, crude oil, and gasoil-in different volumetric fractions to precisely determine their volume ratios. Many characteristics of the signal, including its mean, standard deviation, autocorrelation, zero-crossing rate, dominant frequency, power spectral density, harmonic-to-noise ratio, cross-frequency coupling, peak-to-peak amplitude, and fall time, are extracted after data collection. To select optimal features, an innovative approach utilizing ant colony optimization is deployed, systematically identifying the most informative feature combinations for volumetric ratio prediction. These meticulously chosen features serve as inputs to a multilayer perceptron (MLP) neural network tasked with accurately determining the volume ratio of the pipeline contents. Remarkably, the methodology showcases remarkable efficacy, with the root mean square error (RMSE) in volume ratio determination found to be less than 0.52. This significant finding not only underscores the robustness of the proposed approach but also promises to revolutionize pipeline monitoring techniques, offering unprecedented accuracy and efficiency in oil industry operations. This research thus represents a pivotal advancement in the field, with far-reaching implications for both academic research and practical applications within the oil and gas sector.

Application of deep neural network and gamma radiation to monitor the transport of petroleum by-products through polyducts

To continuously monitor information about the transport of fluids by sequential batches in polyduct, found in the petrochemical industry, it is necessary to manage the mixing zone - transmix - that occurs when two fluids are being transported. This scenario demonstrates the need to estimate the interface region and the purity of the fluids in this region to improve the management of the pipeline and, thus, reduce associated costs. This study presents a measurement system based on the dual-modality gamma densitometry technique in combination with a deep neural network with seven hidden layers to predict the purity level of four different fluids (Gasoline, Glycerol, Kerosene and Oil Fuel) in the transmix. The detection geometry is composed of a137Cs radioactive source (emitting gamma rays of 661.657 keV) and two NaI(Tl) scintillator detectors to record the transmitted and scattered photons. The study was performed by computer simulations using the MCNP6 code, and the information recorded in the detectors was used as input data for training and evaluating the deep neural network. The proposed intelligent measurement system is able to predict the purity level of fluids with errors with mean squared error values below 1.4 and mean absolute percentage error values below 5.73% for all analyzed data.

Application of Gamma Attenuation Technique and Artificial Intelligence to Detect Scale Thickness in Pipelines in Which Two-Phase Flows with Different Flow Regimes and Void Fractions Exist

Scale deposits can reduce equipment efficiency in the oil and petrochemical industry. The gamma attenuation technique can be used as a non-invasive effective tool for detecting scale deposits in petroleum pipelines. The goal of this study is to propose a dual-energy gamma attenuation method with radial basis function neural network (RBFNN) to determine scale thickness in petroleum pipelines in which two-phase flows with different symmetrical flow regimes and void fractions exist. The detection system consists of a dual-energy gamma source, with Ba-133 and Cs-137 radioisotopes and two 2.54-cm × 2.54-cm sodium iodide (NaI) detectors to record photons. The first detector related to transmitted photons, and the second one to scattered photons. The transmission detector recorded two signals, which were the counts under photopeak of Ba-133 and Cs-137 with the energy of 356 keV and 662 keV, respectively. The one signal recorded in the scattering detector, total counts, was applied to RBFNN as the inputs, and scale thickness was assigned as the output.