This paper introduces the main components of a computational tool developed to obtain the GHG (Greenhouse Gas), intensity and total emissions during the lifetime of petroleum production facilities taking into account all variations in quantity and quality of products and various possible operational configurations. It is a tool developed to help process engineers in designing the production facility equipment to optimize operational efficiency and minimize GHG emissions.
During the lifecycle of petroleum production facilities the material streams vary significantly. At the beginning of the operation, e.g. in the pre-salt region of Brazil, the volume of gas produced has an upward profile which reaches a maximum between 4 and 6 years of operation. Also, the CO2 content of this gas is significant and with a tendency to increase during the life of the well due to the well characteristics and due to gas re-injection (gas storage).
Depending on the design and local conditions of the production facility it may also be necessary to import fuel (diesel or natural gas to produce heat and power) either since the start of the operations or when the production curve starts to decline.
Many operational parameters of the processes undertaken to treat the products in an oil and gas production off-shore facility are function of time (due to variations not only in the quantity but also in the quality of the products) and, it is necessary to perform the entire material and energy balance for the operation to obtain the total GHG emissions as a function of time.
A computational tool was developed using the language Delphi to perform those material and energy balances for various configurations of the facility aiming at determining the total GHG emissions from combustion, flaring and venting at each stage of the production lifetime. This implies in automating the data input as a function of time, setting up the equipment configuration for each phase of the life-cycle and obtaining a comprehensive material balance for all equipment taking into account all stages of the treatment and possible alternatives.
It is thus possible to predict, for a platform’s activity profile during its lifetime, the annual quantities of GHG emitted.