A new real-time hole cleaning monitoring method based on downhole multi-point pressure measurement and data driven approach

Abstract

During the construction of long horizontal wells and extended reach wells, inadequate hole cleaning can lead to a series of drilling problems. Traditional hole cleaning analysis is simply based on theoretical models or surface vibrating screen data, and cannot accurately assess downhole cuttings distribution and existing problems. This paper introduces a novel research idea that combines the traditional drilling hydraulic model with the artificial intelligence method. A downhole true cuttings distribution technology based on measurement data, the inversion of flow characteristics from pressure in the hole cleaning field, and a new method for quantitatively evaluating the dynamic distribution of downhole cuttings using along-string measurement (ASM) data were proposed. The results depict that, the relationship between hole cleanliness and annular pressure loss in different hole sections under different working conditions is proportional. Second, under given flow conditions, a pressure-driven hole-cleaning model exerted a reverse pressure drop by inferring the effect of cuttings on the borehole. Third, downhole multipoint measurement indicated an accurate evaluation of the hole cleaning condition and provide detailed downhole information that avoided and solve inadequate hole cleaning of long horizontal wells and extended reach wells. In conclusion, the combination of these methods can overcome the defects of traditional hole cleaning analysis. The integrated approach is helpful in improving the practical technique in hole cleaning and promotes the large-scale application in the oil and gas industry.