A Support Vector Machine Approach for the Prediction of Drilling Fluid Density at High Temperature and High Pressure |
| |
| Authors: | G. Wang X.-L. Pu H.-Z. Tao |
| |
| Affiliation: | 1. State Key Laboratory of Oil-Gas Reservoir Geology &2. Exploitation , Southwest Petroleum University , Chengdu , China;3. Southwest Petroleum University, State Key Laboratory of Oil–Gas Reservoir Geology &4. Exploitation , Chengdu , China |
| |
| Abstract: | Abstract A support vector machine (SVM) approach was presented for predicting the drilling fluid density at high temperature and high pressure (HTHP). It is a universal model for water-based, oil-based, and synthetic drilling fluids. Available experimental data in the literature were used to develop and test this SVM model. Good agreement between SVM predictions and measured drilling fluid density values confirmed that the developed SVM model had good predictive precision and extrapolative features. The SVM model was also compared with the most popular models such as the artificial neural network (ANN) model, empirical correlations, and analytical models. Results showed that the SVM approach outperformed the competing methods for the prediction of drilling fluid density at HTHP. |
| |
| Keywords: | density drilling fluid model prediction support vector machine |
|
|
