The development of a cost-effective multiphase flow meter to determine the individualphase flow rates of oil, water and gas was investigated through the exploitation of asingle 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 mminternal diameter, stainless steel catenary riser in the Cranfield University multiphaseflow test facility. Gamma radiation attenuation data was collected for two photonenergy 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-periodicwaveforms in the time-varying multiphase flow densities and discriminatorycorrelations between statistical features of the gamma count data and key multiphaseflow parameters.The development of a mechanistic approach to infer the multiphase flow rates from thegamma attenuation information was investigated. A model for the determination of theindividual phase flow rates was proposed based on the gamma attenuation levels; whilequasi-periodic waveforms identified in the multiphase fluid density were observed toexhibit a strong correlation with the gas and liquid superficial phase velocity parametersat fixed water cuts.Analysis of the use of pattern recognition techniques to correlate the gammadensitometer data with the individual phase superficial velocities and the water cut wasundertaken. Two neural network models were developed for comparison: a singlemultilayer-perceptron and a multilayer hierarchical flow regime dependent model. Thepattern recognition systems were trained to map the temporal fluctuations in themultiphase mixture density with the individual phase flow rates using statistical featuresextracted from the gamma count signals as their inputs. Initial results yielded individualphase flow rate predictions to within ±10% based on flow regime specific correlations.
展开▼
