Gamma radiation methods for clamp-on multiphase flow metering

Abstract

The development of a cost-effective multiphase flow meter to determine the individual phase flow rates of oil, water and gas was investigated through the exploitation of a single clamp-on gamma densitometer and signal processing techniques. A fast-sampling (250 Hz) gamma densitometer was installed at the top of the 10.5 m high, 108.2 mm internal diameter, stainless steel catenary riser in the Cranfield University multiphase flow test facility. Gamma radiation attenuation data was collected for two photon energy ranges of the caesium-137 radioisotope based densitometer for a range of air, water and oil flow mixtures, spanning the facility’s delivery range. Signal analysis of the gamma densitometer data revealed the presence of quasi-periodic waveforms in the time-varying multiphase flow densities and discriminatory correlations between statistical features of the gamma count data and key multiphase flow parameters. The development of a mechanistic approach to infer the multiphase flow rates from the gamma attenuation information was investigated. A model for the determination of the individual phase flow rates was proposed based on the gamma attenuation levels; while quasi-periodic waveforms identified in the multiphase fluid density were observed to exhibit a strong correlation with the gas and liquid superficial phase velocity parameters at fixed water cuts. Analysis of the use of pattern recognition techniques to correlate the gamma densitometer data with the individual phase superficial velocities and the water cut was undertaken. Two neural network models were developed for comparison: a single multilayer-perceptron and a multilayer hierarchical flow regime dependent model. The pattern recognition systems were trained to map the temporal fluctuations in the multiphase mixture density with the individual phase flow rates using statistical features extracted from the gamma count signals as their inputs. Initial results yielded individual phase flow rate predictions to within ±10% based on flow regime specific correlations.