Estimation of water content of natural gases using particle swarm optimization method

ABSTRACT

A precise estimation of natural gas water content is a significant constraint in appropriate planning of natural gas production, processing services and transmission. The main contribution of this research is to develop a machine learning approach for predicting water content of sweet and sour natural gases. In this regard, a joining of particle swarm optimization and an artificial neural network was utilized. The suggested model presents good predictions of the sour natural gas water content with following circumstances, including CO₂ contents of 0–40 mol%, H₂S contents of 0–50 mol%, pressures in range from atmospheric to 70,000 KPa for sour gas and 100,000 KPa for sweet gas, and temperatures from 10–200°C for sweet gases and 10–150°C for sour gases.

KEYWORDS:

• Artificial neural network
• modeling
• natural gas
• particle swarm optimization
• water content