| dc.description.abstract | To ensure optimal reservoir management, utilization of systematic and efficient tools to forecast and analyze future performance is essential. Reservoir simulation is one of the most important tools used to estimate reserves, predict fluid flow behavior, and analyze future production. Geological modeling is critical to successfully simulate reservoir behavior. Hence, managing geological uncertainties, such as lack of reliability in geological observations, measurements and calculations, are important to ensure optimal reservoir management.
For this purpose, Petrel was used to make a geological model based on real field data from Gullfaks. A part of the field was chosen, with four injectors and two producers. The model consists of oil and water, with no free gas present. The production method was waterflooding and the production time was of 40 years. A sensitivity analysis, the one at a time (OAT) method, was used to look at the effect on field performance by systematically changing geological variables, such as number of high permeability channels, channel orientation, fault transmissibility, porosity, and permeability. The main output we looked at was oil recovery, oil production, water cut and water injection. In addition, we looked at the economic aspect by investigating net present value (NPV).
The Base Model has fully open faults and an oil recovery efficiency of 0.47. When incorporating channels into the model, the efficiency ranged from 0.36 to 0.44. Further, when adjusting the orientation, the efficiency varied from 0.36 to 0.52. Adding variations for the fault transmissibility multiplier (FTM), from fully open to fully sealing, resulted in a significant impact on oil recovery, reducing the efficiency to a range of 0.14 to 0.50. Additionally, changing the porosity and permeability of the facies, making them more similar to each other, resulted in lower oil recovery efficiencies of 0.44 and 0.45. The impact of porosity and permeability on flow and flow path results in flooding of different areas within the model, which contributes to the observed variation in oil recovery across the different scenarios.
Varying FTM has the most significant impact, both on production and NPV. Reduction of FTM directly affects the oil recovery, as a much smaller part of the reservoir is flooded due to the injectors and producers being in separate fault sections, but also since the production shifts from displacement to pressure depletion. | |