SIMULATED ANNEALING
In static Geologic Modeling and Dynamic Simulation the reservoir heterogeneity

description represents one of the most critical phases.

How much a modeling method can approach the reality can be only statistically

defined.

If we consider a Sequential Gaussian Simulation approach, for each input data set

an output set of multiple realizations will be calculated.

In this context further results add new constraints to define a statistical solution.

Simulated Annealing like the Sequential Simulation method honors the univariate

statistics, spatial relationship and relationships among different attributes.

It also has the flexibility to incorporate other contraints.

The simulation Algorithm that incorporates the constraints is described by the

Objective Function.

Alternatively we can dynamically perform such a kind of simulation through

Neural Networks with the Hopfield Net.

Through the competitive logic of this network, optimal results can be achieved.

Simulated Annealing can be optimally used to describe heterogeneity in vulcanic

formations or carbonate diagenetical evolution.