Developing a machine learning tool to predict discharge temperatures of gas compressor
Abstract
Gas compressors are important equipment on the central processing platform PQP-HT. After dehydration and ensuring the dew point temperature in accordance with inlet conditions and specifications of the Nam Con Son gas pipeline (NCSP), natural gas is transferred to a gas compression system consisting of 2 compressor lines. Optimizing operating conditions by reducing the inlet pressure of the natural gas processing system is normally used to extend production time of a gas well. However, alterations in inlet operating conditions will directly affect the gas compressor system, potentially causing the discharge temperature to exceed safe operating thresholds.
Commercial thermodynamic simulation software (such as Hysys, ProII) is typically employed to assess the effect of changing gas compressor operating conditions on the outlet temperature of each stage. This allows simulation and selection of optimal working conditions to ensure safety within the natural gas processing system. Nevertheless, the cost of licensing and maintaining commercial software is substantial. Nowadays, machine learning algorithms are proven to be able to predict operating parameters based on historical data. Many studies have been devoted to accurately predicting compressor performance to improve operational efficiency. Machine learning algorithms have the advantage of highly precise prediction results and the ability to operate continuously and re-train automatically upon any operational condition change. Therefore, they can be used as a viable alternative to commercial thermodynamic simulation software.
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