Intelligent Diagnosis of Leakage Characteristics in Low-Pressure Carbonate Gas Wells by Integrating Geological-Engineering-Material Data

To address the leakage challenges in low-pressure carbonate gas wells in the Lower Paleozoic formations of the Changqing region, data from 45 wells and over 80 geological, engineering, and material parameters were collected. Introducing big data evaluation principles, quantitative relationships between all 80+ original parameters and leakage rates were established. Using the “peeling algorithm,” the complete influence relationships of geological-engineering-material parameters on leakage characteristics were determined. Weight coefficients of different factors under multi-condition scenarios were obtained, main controlling factors were screened, and a main controlling equation for leakage rate prediction during workover operations was developed. This enables quantitative pre-operation leakage rate prediction and forms an intelligent prediction and matching technology for temporary plugging in low-pressure Lower Paleozoic gas wells. The technology provides guidance for selecting plugging materials based on main controlling factors under low-pressure conditions, improving the first-attempt success rate of leakage control in low-pressure gas wells.