WANG Xiangchun, LIU Hao, WANG Chao, et al. Big data method for evaluating reservoir damage degree of fuzzy ball drilling fluid[J]. Petroleum Reservoir Evaluation and Development, 2021, (4): 605-612. DOI: 10.13809/j.cnki.cn32-1825/te.2021.04.017.
Big data method for evaluating reservoir damage degree of fuzzy ball drilling fluid
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 mu
lti-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 m
3
. 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