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

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.