Predicting Equivalent Static Density of Fuzzy Ball Drilling Fluid by BP Artificial Neutral Network - Chen Yang; Zhaohong Wang; Lihui Zheng; Dengtian Mao (Advances in Materials Science and Engineering)
A back-propagation artificial neutral network model is built based on 220 groups of PVT experimental data to predict the equivalent static density versus depth for fuzzy ball drilling fluid which is a kind of gas-liquid two-phase material. The model is applied in the Mo80-C well located in Sichuan Province of China; the maximum relative error between calculated results and measured data is less than 2%. By comparing with the multiple regression model, the present model has a higher precision and flexibility. The equivalent static density of fuzzy ball drilling fluid from ground to the depth of 6000 m is predicted by the present model, and the results show that the equivalent static density of fuzzy ball drilling fluid will decrease slowly with the growth of depth, which indicates that the gas cores of the fuzzy balls still can exist as deep as 6000 m.