{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T19:02:57Z","timestamp":1771268577130,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T00:00:00Z","timestamp":1755043200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deep Earth Probe and Mineral Resources Exploration -National Science and Technology Major Project","award":["2024ZD1004406"],"award-info":[{"award-number":["2024ZD1004406"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>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\u2019s local feature capture capability, LSTM\u2019s time-dependent modeling, and the attention mechanism\u2019s 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 \u201cinitial static similar production + historical data-driven rolling production\u201d. 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.<\/jats:p>","DOI":"10.3390\/sym17081311","type":"journal-article","created":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T09:43:01Z","timestamp":1755078181000},"page":"1311","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Research on Intelligent Production Optimization of Low-Permeability Tight Gas Wells"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3022-6222","authenticated-orcid":false,"given":"Yi","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Petroleum Engineering, Xi\u2019an Shiyou University, Xi\u2019an 710065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Li","sequence":"additional","affiliation":[{"name":"CNOOC (China) Limited Tianjin Branch, Tanggu, Tianjin 300450, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengguo","family":"Yang","sequence":"additional","affiliation":[{"name":"No.3 Gas Production Plant of PetroChina Changqing Oilffeld Company, Ordos 017300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kewen","family":"Qiang","sequence":"additional","affiliation":[{"name":"College of Petroleum Engineering, Xi\u2019an Shiyou University, Xi\u2019an 710065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Petroleum Engineering, Xi\u2019an Shiyou University, Xi\u2019an 710065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Liu","sequence":"additional","affiliation":[{"name":"Production & Operation Management Department of PetroChina Changqing Oilffeld Company, Xi\u2019an 710018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiansheng","family":"Wei","sequence":"additional","affiliation":[{"name":"No.3 Gas Production Plant of PetroChina Changqing Oilffeld Company, Ordos 017300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3442-033X","authenticated-orcid":false,"given":"Rui","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Petroleum Engineering, Xi\u2019an Shiyou University, Xi\u2019an 710065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,13]]},"reference":[{"key":"ref_1","first-page":"125","article-title":"Understanding and Insights from Physical Simulation Experiments of Gradual Pressure Reduction in Tight Sandstone Gas Reservoirs","volume":"42","author":"Li","year":"2022","journal-title":"Nat. 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