Like most technologies in the oil and gas industry, NGL recovery and fractionation equipment and systems are always evolving to better optimize. Professional research and discussion is constantly shaping new innovation, presented as white papers, journal articles, and even conferences like the NGL Recovery Optimization & Cryoplant Facilities Design 2015 that took place this past February. Researchers are always looking at innovative ways to reduce energy consumption in NGL processing, from using mixed refrigerant cycles to optimizing horsepower for residue and refrigeration compression cycles. These improvements are driven just as much by market trends as by inquisitive engineers and demanding environments.

For an example of optimizing NGL recovery, we turn to an article in Oil and Gas Journal that discussed the construction of the Saudi Aramco NGL-recovery plant. A lack of an electric network and ground water combined with other harsh environmental realities drove the developers to optimize the facility to be more energy and resource efficient from the start. The engineers on the project made several energy-saving moves in their design, including opting for chilling during acid-gas dehydration rather than a full triethylene glycol (TEG) dehydration system. This allowed the team to remove components like strippers, reboilers, and feed-residue heat exchangers and lighten the power load of acid-gas compressors. Extra savings were found in swapping a thermosyphon reboiler in for unreliable side-draw pumps and optimizing the heat oil system by using the spare heat from the recycle line of the hot oil outlet.

Another method of optimizing NGL processes is through the use of a combination of sensors and software to improve sustained and peak performance. Such technology is increasingly possible through advances in wireless technologies and data management systems. Rockwell Automation, for example, has created a Natural Gas Liquid Fractionation application that uses data sensors and predictive modeling tools to compare real-time operational data with predicted data. If the real-time results don’t match desired results, tweaks to the controls associated with deethanizers, debutanizer bottoms, and other components can be made to maximize efficiency. It can also automate component streams based on fluctuations in economic value and automatically adjust energy to streams based on the concept of diminishing returns.