Prediction of equivalent static density of water-based fuzzy ball drilling fluid by BP neural network method

In order to study the equivalent static density changing rule of water-based fuzzy ball drilling fluid with different well depth, BP neural network is established, based on the PVT experimental data of fuzzy ball drilling fluid, reflecting the relationship between pressure, temperature and drilling fluid density. On the basis of the network，the equivalent static density prediction model in different well depth is established, and the well depth is associated with pressure and temperature. Compared with the result predicted by multiple regressions, the BP neural network method can obtain higher accuracy, and are more consistent with the static equivalent density in Well M80-C1 at the depth of 0~2 500 m. We predicted the equivalent static density of water-based fuzzy ball drilling fluid in Moxi area at the depth of 2 500~6 000 m by BP neural network, discovering it decreased with depth increase, which proved the fuzzy ball was not compressed into continuous phase at HTHP. The gas ball expansion capacity by temperature was greater than that by pressure with well depth increase, which proved the fuzzy ball structure resistance to compression is strong and temperature can strengthen the blocking effect of fuzzy ball. BP network provides a new mathematical method to predict the downhole equivalent static density of water-based fuzzy ball drilling fluid.