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This study focused on developing machine learning models to detect leak size and location in transient state conditions. The model was designed for an onshore methane–hydrogen blending gas pipeline in Canada. Base case simulations revealed significant effects on mass flow and pressure due to leaks, with the system taking approximately 6 h to reach a steady state from transient conditions. This made it essential to analyze the flow characteristics during the transient state. Trend data from the pipeline’s inlet and outlet were examined, considering the leak size and location. To better represent the data over time, a method was used to create two-dimensional images, which were then fed into a CNN (convolutional neural network) for training. The model’s accuracy was assessed using classification accuracy and a confusion matrix. By refining the data acquisition process and implementing targeted screening procedures, the model’s classification accuracy increased to over 80%. In conclusion, this study demonstrates that machine learning can enable rapid and accurate leak detection in transient state conditions. The findings are expected to complement existing leak detection methods and support operators in making faster, more informed decisions.

Offshore oil and gas fields pose significant challenges due to their lower accessibility compared to onshore fields. To enhance operational efficiency in these deep-sea environments, it is essential to design optimal fluid production conditions that ensure equipment durability and flow safety. This study aims to develop a smart operational solution that integrates data from three offshore gas fields with a dynamic material balance equation (DMBE) method. By combining the material balance equation and inflow performance relation (IPR), we establish a reservoir flow analysis model linked to an AI-trained production pipe and subsea pipeline flow analysis model. We simulate time-dependent changes in reservoir production capacity using DMBE and IPR. Additionally, we utilize SLB’s PIPESIM software to create a vertical flow performance (VFP) table under various conditions. Machine learning techniques train this VFP table to analyze pipeline flow characteristics and parameter correlations, ultimately developing a model to predict bottomhole pressure (BHP) for specific production conditions. Our research employs three methods to select the deep learning model, ultimately opting for a multilayer perceptron (MLP) combined with regression. The trained model’s predictions show an average error rate of within 1.5% when compared with existing commercial simulators, demonstrating high accuracy. This research is expected to enable efficient production management and risk forecasting for each well, thus increasing revenue, minimizing operational costs, and contributing to stable plant operations and predictive maintenance of equipment.

This study explores the potential of SiO 2 and Al 2 O 3 nanoparticles (NPs) to enhance oil recovery in carbonate reservoirs through a novel hybrid low salinity water flooding (LSWF) approach. By evaluating four injection fluids—deionized (DI) water/SiO 2 , DI/Al 2 O 3 , low salinity water (LSW)/SiO 2 , and LSW/Al 2 O 3 —this research systematically examines their effects on rock surface wettability and interfacial tension. The findings reveal a key finding: the LSW/NP flooding process effectively balances NP-induced plugging and rock dissolution, thereby maintaining the pore structure integrity of carbonate rocks. SiO 2 NPs demonstrated a unique ability to shift rock wettability from oil-wet to intermediate-wet conditions, while Al 2 O 3 NPs exhibited superior interfacial activity, significantly reducing interfacial tension. Notably, hydrophobic Al 2 O 3 NPs achieved the most substantial reduction in capillary pressure, facilitating an earlier breakthrough and enhanced oil displacement efficiency. Among all injection fluids, LSW/Al 2 O 3 emerged as the optimal choice, delivering faster and more efficient oil recovery compared to DI/NP systems. This study is pivotal in demonstrating the combined effects of LSW and tailored NP properties, offering a effective approach for enhanced oil recovery (EOR) in carbonate reservoirs. The findings underscore the importance of selecting appropriate NPs to optimize hybrid LSWF processes and provide practical guidance for the design of next-generation EOR technologies.