Forecasting gas density using artificial intelligence
Research output: Contribution to journal › Article › Research › peer-review
Authors
External Organisational units
- Shiraz University
- Faculty of Mechanical Engineering
- Petroleum University of Technology, Ahwaz, Iran
Abstract
Proper calculations of gas engineering require precise determination of gas properties and its associated variations with pressure and temperature. These properties can be determined by conducting experimental tests on gathered fluid samples from the bottom of the wellbore or at the surface as well as using equations of state and empirical correlations. This work is concentrated to develop a robust and quick model based on artificial network trained with teaching learning based optimization (ANN-TLBO) using 693 data sets at a wide range of pressure and temperature for gas density prediction. Comparing gas density from the predictive method and experimental results describe that the proposed ANN-TLBO model is of reliable accuracy for determining gas density. Sensitivity analysis also showed the extreme effect of temperature and pressure on gas density.
Details
Original language | English |
---|---|
Pages (from-to) | 903-909 |
Number of pages | 7 |
Journal | Petroleum science and technology |
Volume | 35.2017 |
Issue number | 9 |
DOIs | |
Publication status | Published - 10 Aug 2017 |
Externally published | Yes |