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History Matching with ECLIPSE
in the Pet. Software section I got the idea that it would be a good idea to have a thread just about how you do this. We can not go too much into the details as we will never see the model someone is working on, but I think general ideas and suggestions might be sufficient to help people in this area.
For myself I can say I have done some but not enough to say I am an expert as in our business you need to do 10 different things at a time and can not often spend a lot of time in one area. That was my working environment, "Now your on project X doing y, then your on project Y doing s." Never in one very long.
Well here we go ....
In the discussion I mentioned above someone was asking about a program SLB has called SIMOPT. I had once looked at it in my spare time. Using gradient sensitivities the programs helps to find a better history match. That sounds easy in those few words but requires much more work and understanding of what is happening.
With this in mind I would suggest the following work available for free which explains a bit this process in another application.
Sensitivity-Based History Matching Algorithms and Streamline Methods
The prominence of sensitivity-based history matching algorithms can be largely attributed to the rapid convergence they exhibit. Because of the computational challenge posed by even the smallest of field-scale history-matching endeavors, it becomes imperative for the computation of sensitivity coefficients to be as efficient as practically possible. One of the distinguishing features of streamline-based history matching algorithms is their superior efficiency in computing sensitivity coefficients.1 It
is the rapid sensitivity computation and thus applicability of the streamline-based method achieved in two phase applications that motivates the extension to three-phase production data researched in this work. The efficacy of the approach in calculating sensitivities is a direct consequence of the nature of the streamline formulation for modeling the dynamics
of fluid flow. In the streamline domain, the flow and transport equations are decoupled with a resulting reduction of the solution of a three-dimensional problem to a series of one-dimensional problems.14 In chapter II, we discuss the streamline formulation for the forward problem, and the sensitivity formulation for the inverse problem is detailed in chapter III
The prominence of sensitivity-based history matching algorithms can be largely attributed to the rapid convergence they exhibit. Because of the computational challenge posed by even the smallest of field-scale history-matching endeavors, it becomes imperative for the computation of sensitivity coefficients to be as efficient as practically possible. One of the distinguishing features of streamline-based history matching algorithms is their superior efficiency in computing sensitivity coefficients.1 It
is the rapid sensitivity computation and thus applicability of the streamline-based method achieved in two phase applications that motivates the extension to three-phase production data researched in this work. The efficacy of the approach in calculating sensitivities is a direct consequence of the nature of the streamline formulation for modeling the dynamics
of fluid flow. In the streamline domain, the flow and transport equations are decoupled with a resulting reduction of the solution of a three-dimensional problem to a series of one-dimensional problems.14 In chapter II, we discuss the streamline formulation for the forward problem, and the sensitivity formulation for the inverse problem is detailed in chapter III
link
Another good article from someone doing the work for a client and what the client was complaining about is this one
Real World
Some parameters are not important AT CERTAIN TIMES to the end result from a simulation. This is where sensitivities can tell you a lot (what to focus on).
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