Abstract: Exploration and exploitation of hydrocarbons involve multiple disciplines. Each of these disciplines supplies data in different way, in different scale ( support ), in different resolution and in different reliability. To integrate mul-
tiple disciplines data in a numerical reservoir model is a tendency of reservoir modeling at present day. This paper discusses the methods of and problems encountered in applying simulated annealing combinatorial optimization al-
gorithm to reservoir stochastic conditional simulation, which include the methods of constructing and updating ob-ject function, choosing initial state, perturbing mechanism, annealing schedule and integrating data from differ-
ent sources. Over other reservoir stochastic modeling techniques, two principal advantages of simulated annealing method are that it can integrate different kinds of data more feasibly and can keep reservoir spatial structure more accurately. Its main disadvantage is the vast amount of CPU time needed.

Key words: Reservoir, Stochastic process, Simulation