Despite forty years of simulation, the petroleum industry has been reluctant to discuss history match quality standards. This lack of standard is often justified by statements such as " the reservoir is unique" and the data type and quality vary from field to field. While those statements are true to a degree, they leave the non-simulation personnel confused as to whether a model is good or bad. This paper discusses quality control and history matching from experience of over 200 field simulation studies.
History match parameter tolerance depends upon: q Drive mechanisms and related key parameters q Reservoir architecture q Study objective q Data quality
There is often a big trade off between the quality of history match, size of model and time requirements. This paper discusses key parameters and how they apply to field simulation cases.
It must be noted that flow simulation tools are aimed at the most likely or P50 case. Higher quality studies with matching multiple parameters and good data can significantly increase the confidence level of a reserves determination and decrease the range of uncertainty. The reverse is also true some simulations with poor data are only applicable in a semiquantitative sense. This paper has important implications for using simulation as a reserves determination tool. It is important that simulation studies be graded in terms of history match quality, number of variables matched, percentage of wells matched, data quality, what parameters were adjusted and by how much so that assignments of proved P90, probable P50, and possible reserves can be estimated.
Although new technology and techniques hold great promise, workflow and quality control of data are still central issues in insuring reliable simulation forecasts as well.
In order to evaluate success and failure, as well as the need for simulation, we must clearly understand why simulation/reservoir forecasts are successful and why they fail. There are three general categories that control a simulation study:
Timing versus accuracy
Initial diagnostics
Coupling history match parameters versus history match Objectives
The following is a more detailed list of why sometimes reservoir forecasts have sometimes been unsuccessful:
The simulation study is taking too long relative to the business objective/decision
Paralysis by analysis; reservoir engineers being paralyzed by the analysis of data.
The engineer assuming every variable in a simulation model is critical.
Confusing complexity with accuracy. Assuming bigger and more complex simulation is always better or more accurate.
Failure to adequately represent reservoir heterogeneity.
Improper initial well diagnostic and reservoir diagnostic and failure to recognize reservoir mechanisms.
Failure to calibrate the geological model's overall average permeability.
Failure of coupling history match parameters to physical parameters that strongly control simulation forecast results.
The lack of understanding of how history match parameters are controlled by physical parameters, such as relative permeability, permeability distribution and porosity and translating those parameters to predict future performance.
Mechanisms and operational limitations should be recognized up front.
