Abstract:

The epidermal coefficient is mostly used to describe the reservoir damage degree of drilling fluid, but cannot quantitatively characterize the relation between the specific performance index and the reservoir damage degree, and effectively guide the optimization and adjustment of field drilling fluid performance. However, for the big data method, it has obvious advantages in multi-factor analysis. Based on this, a multi-parameter drilling fluid reservoir injury model is established to realize the working fluid optimization to protect the reservoir. To this end, the on-site data of nine completion wells withe fuzzy ball drilling fluid and six other adjacent completion wells are collected. The average daily output difference is took as the target function, with seven parameters of drilling fluid density, apparent viscosity, plastic viscosity, funnel viscosity, dynamic plastic ratio, dynamic shear force, and pH value as the independent variables. Firstly, the multiple regression method is used to establish a multi-parameter model. Then the mathematical model of drilling fluid on reservoir damage is established by the main controlling factors found by cocoon stripping algorithm. Finally, the quantitative relationship between the fluid performance and the average daily yield is defined. The study found that the regression coefficients of apparent viscosity, density, dynamic plastic ratio, and pH value are -1.561, 0.428, -0.535, 1.60, respectively, indicating that the apparent viscosity of fuzzy ball drilling fluid caused greater damage to the reservoir, density, dynamic plastic ratio, while the density and pH value have the protection effect of reservoir. In the case of the adjustment of drilling fluid performance of the horizontal section in Well-Yan5-V1 guided by the regression model, the average daily production after commissioning is increased by nearly 800 m³. In conclusion, compared with the on-site evaluation methods such as well testing, the big data method can only accurately diagnose the damage degree but also provide theoretical basis for the optimization of site drilling fluid performance. Meanwhile, it also provide a method for evaluating reservoir damage, and the application effect on site is obvious.

Key words: big data, reservoir damage, fuzzy ball drilling fluid, performance, poor production