Predicting Equivalent Static Density of Fuzzy Ball Drilling Fluid by BP Artificial Neutral Network

Chen Yang,Lihui Zheng,Dengtian Mao,Zhaohong Wang

doi:10.1155/2015/340721

Abstract

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.

Highlights

Fuzzy ball drilling fluid is a gas-liquid two-phase material developed by Zheng et al [1]
We can see that, with the increasing of depth, the equivalent static density of fuzzy ball drilling fluid will decrease slowly; this conclusion is very important, which indicates that the gas cores can still exist in fuzzy ball drilling fluid as deep as 6000 m, which means the fuzzy ball drilling fluid can be used in deep well and maintain its excellent performance
A BP ANN was built in this paper to relate the density of fuzzy ball drilling fluid with pressure and temperature

Summary

Introduction

Fuzzy ball drilling fluid is a gas-liquid two-phase material developed by Zheng et al [1]. Their research thought, many researchers built models predicting equivalent static density of different drilling fluids at certain pressure and temperature based on lab PVT data [5,6,7,8]. BP ANN is introduced to relate the lab PVT data and density of fuzzy ball drilling fluid; the prediction model of equivalent static density versus depth is established based on the built BP ANN. The predicted results are compared with measured data and the results by multiple regression method This method could be used to predict density for other gas-liquid two-phase material versus different temperature and pressure

Prediction Model

Model Application

Findings

Conclusion