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The tight gas field is greatly affected by pressure in the development process. Due to the different production time and formation pressure of each well in the gas field, the production characteristics of the gas well are obviously different. After the gas well sees water, it is impossible to formulate production measures efficiently and accurately. Therefore, by analyzing the production performance characteristics of gas wells, this paper carries out the classification research of tight gas wells, and formulates the corresponding production measures according to the classification results. Taking gas well energy and liquid production intensity as the reference standard of gas well classification, the dynamic parameter indexes characterizing gas well energy and liquid production intensity are established. Gas wells with different production characteristics are divided into six categories by clustering algorithm: high energy-low liquid, high energy-high liquid, medium energy-low liquid, medium energy high-liquid, low energy-low liquid, low energy-high liquid. Then the classification method of tight gas well is formed. In this paper, 50 wells in Linxing block are selected as the research object. The research results show that most of the wells in Linxing block are located in area V, belonging to low energy and low liquid wells. It is recommended to implement intermittent production. The classification based on gas well energy and liquid production intensity are of guiding significance for the formulation of production measures in the Linxing block.

The marginal wells in low-permeability oilfields are characterized by small storage size, scattered distribution, large regional span, low production, intermittent production, etc. The production mode of these wells is nonpipeline mode. In our previous work (Zhang et al., 2019), a novel mixed-integer linear programming (MILP) model using a discrete-time representation was presented for the operation scheduling of nonpipelined wells. However, too many discretization time points are required to ensure the accuracy of the model. Even for moderately sized problems, computationally intractable models can arise. The present paper describes a new continuous-time representation method to reformulate this schedule optimization problem. By introducing the continuous-time representation, the binary variables are largely reduced. The solution effect for different model sizes is also investigated. When the model size increases to a certain degree, a feasible solution cannot be obtained within a limited time. The results of a case study originated from a real oilfield in China show that the continuous-time model requires less time to obtain the optimal solution compared to the discrete-time model. In details, considering a same scale problem, the solution based on the continuous-time model saves 52.25% of the time comparing with the discrete-time model. The comparison validates the new model’s superiority.

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