Distributed fiber optic temperature sensing provides significant advantages for production monitoring in complex geological environments due to its high precision, real-time capability, and long-term stability. However, its expanding application generates increasingly complex temperature datasets that challenge conventional production profile interpretation methods. To address these challenges, in this study, the researchers developed an intelligent interpretation framework to combine physically constrained forward modeling with data-driven machine learning techniques. A forward model of wellbore temperature profiles was established based on the fundamental principles of momentum conservation, energy conservation, and two-phase flow dynamics. Sensitivity analysis was used to identify key controlling factors, including production rate, geothermal gradient, reservoir thickness, crude oil heat capacity, and crude oil density, which were then used to generate representative training datasets. Three neural network architectures, including a fully connected neural network, a radial basis function network, and a back propagation network, were systematically trained and compared. The fully connected neural network demonstrated superior prediction accuracy and generalization capability, offering a robust tool for production profiling. Field validation using actual distributed fiber optic temperature-sensing monitoring data from commingled production wells confirmed the method’s practical effectiveness, with predicted production rates strongly agreeing with the measured values across multiple reservoir layers. The proposed framework provides a reliable, efficient solution for interpreting the production profiles of multilayer wells under single-phase flow conditions. This study establishes a foundational methodology that can be extended to more complex multiphase flow scenarios in future research, thereby contributing to intelligent and automated reservoir management.

Due to the dissimilarity among different producing layers, the influences of inter-layer interference on the production performance of a multi-layer gas reservoir are possible. However, systematic studies of inter-layer interference for tight gas reservoirs are really limited, especially for those reservoirs in the presence of water. In this work, five types of possible inter-layer interferences, including both absence and presence of water, are identified for commingled production of tight gas reservoirs. Subsequently, a series of reservoir-scale and pore-scale numerical simulations are conducted to quantify the degree of influence of each type of interference. Consistent field evidence from the Yan'an tight gas reservoir (Ordos Basin, China) is found to support the simulation results. Additionally, suggestions are proposed to mitigate the potential inter-layer interferences. The results indicate that, in the absence of water, commingled production is favorable in two situations: when there is a difference in physical properties and when there is a difference in the pressure system of each layer. For reservoirs with a multi-pressure system, the backflow phenomenon, which significantly influences the production performance, only occurs under extreme conditions (such as very low production rates or well shut-in periods). When water is introduced into the multi-layer system, inter-layer interference becomes nearly inevitable. Perforating both the gas-rich layer and water-rich layer for commingled production is not desirable, as it can trigger water invasion from the water-rich layer into the gas-rich layer. The gas-rich layer might also be interfered with by water from the neighboring unperforated water-rich layer, where the water might break the barrier (eg weak joint surface, cement in fractures) between the two layers and migrate into the gas-rich layer. Additionally, the gas-rich layer could possibly be interfered with by water that accumulates at the bottom of the wellbore due to gravitational differentiation during shut-in operations.