Empower Your Teams with Deeper Insights
Aspen Plus Biochemical Process Modeling provides a comprehensive solution to optimize production from the reservoir to separation and processing plants, ensuring maximum profitability and ESG compliance. By empowering your teams with deeper insights, the software enables optimal oil and gas production throughout the upstream asset’s lifetime. It boosts confidence across multiple disciplines, including geologists, reservoir engineers, production engineers, process engineers, and control engineers, in enhancing the operation of reservoirs, wells, surface pipeline networks, and processing facilities. This results in an improved asset value of over 7%, a gain of $20-35 million per site annually, and an increase in production by 2-4%.
Production Optimization of Natural Gas Pipelines & Field Production Facilities
A prime example of the efficacy of Aspen Plus Biochemical Process Modeling is showcased through YPFB Andina’s success story. By deploying an integrated model encompassing multiple gas production facilities across Bolivia, YPFB Andina augmented their gas production, leading to a substantial $280 million increase in revenue within a single year.
Implement Key Processes to Improve Your Production Performance
The software offers key processes, such as Subsurface Science & Engineering, Process Engineering, and Advanced Process Control & Optimization, to enhance production performance. Subsurface Science & Engineering enables the identification of opportunities to extend an asset's life through production optimization and near-field exploration, utilizing advanced geophysics, petrophysics, geologic modeling, and reservoir and pipeline flow simulations. Process Engineering allows for the accurate prediction of yields and properties of process streams, emissions, and energy usage from surface processing facilities. Meanwhile, Advanced Process Control & Optimization ensures the optimization of production rates, minimizes energy consumption, and effectively manages process disruptions by implementing AI-embedded adaptive process control.