For the Liuzhuang gas storage reservoir with edge and bottom water, excessively high bottomhole pressure of production wells can cause edge and bottom water breakthrough, which reduces the effective storage capacity; whereas excessively low bottomhole pressure makes it difficult to achieve the gas production targets. Therefore, determining the reasonable wellhead pressure of production wells is of great significance for the efficient operation of gas storage reservoir. In this study, a comparison of seven critical liquid-carrying flow rate models, including the droplet inversion model and the liquid film inversion model, was made. The Belfroid critical liquid-carrying model was identified as the most accurate for evaluating liquid loading in the Liuzhuang gas storage reservoir. On this basis and in combination with nodal analysis, the relationship curves between gas production rate and critical liquid-carrying flow rate under different reservoir permeabilities and skin factors were obtained, leading to the development of wellhead pressure charts indicating the risk of liquid loading in gas wells of the Liuzhuang gas storage reservoir. This study provides technical support for field operations in diagnosing liquid loading in production wells.

This study aims to further enhance the oil recovery of reservoirs in the Zhong-2 Block of the Gudao Oilfield by identifying the most effective microbial-flooding activator systems and applying them in the field. We began by analyzing the structure of the reservoirs' endogenous microbial communities to understand the potential impact of microbial flooding. This was followed by determining commonly used activator systems based on their abilities to stimulate oil-displacement functional bacteria. Through laboratory experiments on oil displacement efficiency and sweep characteristics, we determined the optimal activator injection method (injection ratio) and the requisite bacterial concentration for maximal microbial-flooding efficacy. Finally, we selected the optimal activator systems and applied them to field tests. Our findings suggest the target block is highly receptive to microbial-flooding. In terms of performance, the activator systems ranked as No. 3 > No. 4 > No. 1 > No. 2. Interestingly, a deep activator system, when compared to the top-performing No. 3 system, exhibited a higher bacterial concentration peak and longer peaking duration. Optimal oil displacement effects were observed at a 1:4 vol ratio between the No. 3 activator and deep activator systems, with bacterial concentrations of up to 10⁶ cells/mL or above. Field tests with the selected activator systems, following a specific injection protocol, demonstrated a notable increase in oil production and a reduction in water cut.

CO₂ flooding is a widely recognized method for enhanced oil recovery (EOR). This study aims to develop an accurate prediction method for determining the location and migration pathway of CO₂ front, which plays an essential role in designing effective CO₂ injection schemes and optimizing production strategies. Given the challenges of directly monitoring CO₂ front movement in subsurface reservoirs, numerical well testing serves as an effective tool for indirectly inferring the location and migration characteristics of the CO₂ front. This study established a numerical well-testing model based on a compositional framework to characterize interactions among multiple components during CO₂ flooding. The methodology used in this model involves generating well-testing curves of CO₂ flooding and then determining their flow stages based on CO₂ distribution within reservoirs. Accordingly, a new well-testing analysis approach was proposed to determine the CO₂ zone front and mixing zone front. This approach was applied to a pilot study of a practical oilfield, where it effectively predicted the positions of both fronts. The findings of this study reveal that the CO₂ zone front and the mixing zone front correspond to the beginning of the first horizontal segment and the endpoint of the upward segment in the pressure derivative curve, respectively. This study introduces a cost-effective and time-efficient method for CO₂ front monitoring, addressing the challenges of high costs and prolonged durations typically associated with CO₂-EOR operations.