Research on Intelligent Production Optimization of Low-Permeability Tight Gas Wells

Le résumé fourni par la source

Gas well production prediction is an important means to determine the economic benefits of gas field development, and it is the key to realize the optimization of gas well production. However, with the continuous development of gas fields, the increasing number of low-yield and low-efficiency wells disrupted the original symmetry in the overall well distribution and production structure. Traditional production capacity prediction methods are difficult to adapt to complex geological conditions and dynamic production characteristics and cannot meet the requirements of refined management of gas fields. In this paper, a CNN-LSTM-attention hybrid prediction model incorporating physical constraints (P-C-L-A) is proposed to predict production per well. The P-C-L-A model integrates CNN’s local feature capture capability, LSTM’s time-dependent modeling, and the attention mechanism’s critical state focusing function. Moreover, the gas well decline law is embedded into the loss function to realize the joint drive of physical constraints and data of the decline curve. Compared with the traditional BP neural network, the model in this paper has higher accuracy, and the root mean square error of the proposed method is reduced by 24.41%. Furthermore, this paper proposes a full life cycle intelligent optimization production strategy of “initial static similar production + historical data-driven rolling production”. For wells in the early stage of production, static production allocation is carried out by matching wells with similar geological engineering parameters based on the symmetry of the characteristic parameters of similar production wells through the k-nearest neighbor value algorithm. For stable production wells, a machine learning model is built to predict short-term production and dynamic production optimization is achieved by rolling updates of production data. The proposed method can be extended to the production prediction of other tight gas wells using similar technical processes.

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