Screening Decommissioned Oil and Gas Pipeline Cleaners Using Big Data Analytics Methods

Rongguang Li, Junqi Zhao, Ling Sun, Long Jin, Sixun Chen and Lihui Zheng ()
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Rongguang Li: North Pipeline Company of the National Pipeline Network Group, Langfang 065099, China
Junqi Zhao: College of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, China
Ling Sun: North Pipeline Company of the National Pipeline Network Group, Langfang 065099, China
Long Jin: College of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, China
Sixun Chen: North Pipeline Company of the National Pipeline Network Group, Langfang 065099, China
Lihui Zheng: College of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, China

Energies, 2025, vol. 18, issue 13, 1-25

Abstract: Traditional methods, such as full-factorial, orthogonal, and empirical experiments, show limited accuracy and efficiency in selecting cleaning agents for decommissioned oil and gas pipelines. They also lack the ability to quantitatively analyze the impact of multiple variables. This study proposes a data-driven optimization approach to address these limitations. Residue samples from six regions, including Dalian and Shenyang, were analyzed for inorganic components using XRD and for organic components using GC. Citric acid was used as a model cleaning agent, and cleaning efficiency was tested under varying temperature, agitation, and contact time. Key variables showed significant correlations with cleaning performance. To further quantify the combined effects of multiple factors, multivariate regression methods such as multiple linear regression and ridge regression were employed to establish predictive models. A weighted evaluation approach was used to identify the optimal model, and a method for inverse prediction was proposed. This study shows that, compared with traditional methods, the data-driven approach improves accuracy by 3.67% and efficiency by 82.5%. By efficiently integrating and analyzing multidimensional data, this method not only enables rapid identification of optimal formulations but also uncovers the underlying relationships and combined effects among variables. It offers a novel strategy for the efficient selection and optimization of cleaning agents for decommissioned oil and gas pipelines, as well as broader chemical systems.

Keywords: oil and gas pipelines; decommissioning; transportation; residues; cleaning; cleaning agents; big data; screening (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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