The Hybrid DBSCAN-Transformer Framework for High-Precision Phase Fraction Measurement in Low-Energy Gamma Flowmeter

Multiphase flow metering is widely employed in the oil and gas industry, particularly for measuring gas-liquid-solid multiphase flow at drilling outlets. Low-energy gamma flowmeters offer relatively high metering accuracy, with phase fraction errors for gas, liquid, and solid typically within ±10%. However, in practical applications, fluid viscosity often causes substances to adhere to the photon detector, leading to measurement deviations that can reach 18% or more. To overcome this limitation, this paper proposes a hybrid Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-Transformer regression framework, referred to as D-Transformer. DBSCAN removes isolated abnormal detector responses before overlapping time-series windows are generated, while the Transformer captures temporal dependencies among operating variables, raw phase-fraction readings, and multi-energy photon counts. Under experiment-wise five-fold evaluation, D-Transformer obtains R2 values of 0.982, 0.985, and 0.981 and RMSE values of 0.0134, 0.0122, and 0.0138 for the gas, liquid, and solid phase fractions, respectively. Component ablations and baseline comparisons show that the complete framework outperforms the no-ResNet, no-DBSCAN, CNN-GRU-Attention, CNN-LSTM, ridge-regression, and uncorrected-flowmeter alternatives.