Optimization of drilling parameters based on Copula function method for the Chepai area

Abstract

In areas with complex stratigraphic lithology, the relationship between the penetration rate and drilling parameters should be fully considered to optimize the drilling process and improve drilling efficiency. The most frequently utilized methods for performing parameter optimization through correlation analysis are the correlation coefficient, principal component analysis, and grey correlation. The correlation coefficient method solely evaluates the extent of linear correlation between two variables, it cannot be applied to the non-linear connection between penetration rate and drilling parameters. The application of principal component analysis may produce inaccurate experimental findings due to the intricate and poorly co-varying nature of drilling parameters. The grey correlation method can lead to the substantial bias in the results because of the vast quantity of data analysed. Based on the vast quantity of data, using the copula function, the big data analysis method analyses the nonlinear relationship between penetration rate and drilling parameters. It constructs a united distribution function expression to determine the optimal parameter selection criteria. The in-situ drilling data from dozens of wells in the Chepaizi area are collected and optimized six types of parameters. The optimal parameter combination is determined. Following field investigation, there was a noteworthy increase of 34.83% in the average penetration rate.