Optimizing Condensate Yield with Digital Twin Technology

This technical report details a study conducted by ADNOC Sour Gas and AspenTech to optimize condensate yield through the use of a process simulation digital twin. The study aimed to enhance profit margins and reduce greenhouse gas emissions by adjusting the Reid vapor pressure (RVP) during condensate hydrotreater operations. The report outlines the methodology employed in two phases: the first phase involved generating a manual table to identify the true vapor pressure (TVP) at varying RVPs, while the second phase developed a data-enabled simulation model linked to actual plant data. The hybrid model allowed for accurate predictions of TVP under operational conditions, enabling adjustments to maximize condensate yield. The outcomes included an increase in condensate yield and a reduction in vapor load on recovery systems, leading to significant operational efficiencies. Additionally, the report discusses the machine-learning approach utilized to enhance decision-making and predictive capabilities in the oil and gas sector, emphasizing the importance of data quality and model validation.