Classification of Dense-Gas/Liquid Flow Patterns in Horizontal Pipe Through Pressure Gradient Time Series Encoded Into Images and Deep Learning

Summary Gas-liquid flow is one of the most observed in nature and in many industrial applications. The presence of dense gas is a special case (i.e., a flow with a liquid/gas viscosity ratio of 10 or even less). Dense-gas/liquid flow is found in the Brazilian offshore production scenario, which is characterized by subsea lines operating under reservoir pressures typically ranging from 100 bar to 500 bar, temperatures around 60°C, and high CO2 content, while upstream of the choke valve pressures commonly range between 30 bar and 160 bar. Despite its relevance, the technical literature shows significant gaps. Although some studies employ dynamic pressure signals to analyze two-phase flows, most rely on numerical simulations or high-frequency experimental systems. Consequently, the analysis and classification of dense-gas/liquid flow using low-frequency differential pressure signals that are precisely the most widely available measurements in offshore operations have not been addressed. Likewise, there are no approaches that integrate multiple signal-to-image encoding techniques with advanced hybrid deep learning architectures for flow-pattern characterization from pressure-gradient signals. To address these gaps, we introduce a methodology that transforms low-frequency pressure-gradient time series into multivariate images using four complementary encoding techniques. Gramian angular fields (GAFs) [Gramian angular summation field (GASF)/Gramian angular difference field (GADF)] capture temporal correlations and angular relationships; recurrence plots (RPs) reveal recurrence patterns and nonlinear dynamic structures; and Markov transition fields (MTF) model state-transition probabilities while preserving the statistical structure of the signal. These representations are integrated into a unified 2 × 2 mosaic, generating a final image of size 2m × 2n. In addition, a hybrid deep learning model convolutional neural network (CNN)-transformer with coordinated attention is proposed, designed to leverage both local features and global dependencies. The convolutional modules extract fine spatial patterns, the Swin Transformer blocks learn long-range relationships through hierarchical self-attention, and the coordinated attention module preserves directional information by recalibrating feature responses along the horizontal and vertical axes. This combination enables the model to capture rapid local variations in the pressure gradient as well as global hydrodynamic trends, both essential in complex multiphase systems. By relying exclusively on low-frequency pressure-gradient signals, which are typically available in offshore monitoring systems, the proposed method offers strong industrial applicability. The results show that the hybrid model achieves 99% accuracy in classifying dense-gas/liquid flow patterns, significantly outperforming traditional approaches based on physical correlations or statistical analysis. These advancements position the methodology as a significant contribution to multiphase-flow characterization in the oil and gas industry.