Integrating dynamic data into reservoir numerical models is essential for capturing the actual dynamic flow behavior. History matching efforts could prove to be fruitless without including such data into the reservoir model. For example, open hole logs and core sampling do not provide a satisfactory characterization of fractures and super-high permeability streaks — two important flow features that can be overlooked.
Failing to incorporate these small but critical flow features can lead to inaccurate simulation models, even if the total rates at the wellhead are captured by the history match. This inaccuracy usually transpires when model results are compared to actual production profiles, and could go unnoticed until the field performance starts to deviate from predictions.
The inclusion of dynamic permeability data into the reservoir model can be performed, either during the static geological modeling, or into the dynamic simulation model, as a conditioning step. Either way, the process starts by comparing the correlation based kh values to those obtained from pressure transient testing. Various methods can be used to utilize the results of this comparison, which vary from simple zone averaging to highly detail vertical permeability profiling.
In the geocellular model, the adjusted dynamic profiles are input for the permeability population by using several methods that range from kriging to stochastic approaches. The final conditioned permeability fields can be in addition to incorporating discrete fracture models or stratiform characterization and mapping as 2D or 3D trend parameters.
This paper explains how PLT profile based distribution of the dynamic permeability resulted in a large improvement of the history match results, and just as importantly, a shorter history matching process for a giant carbonate Middle East reservoir. The paper also shows alternative approaches for incorporating the dynamic permeability into the simulation model, but which did not produce the same desired results.