摘要:
A method, system, a program storage device and apparatus are disclosed for conducting a reservoir simulation, using a reservoir model of a region of interest, wherein the region of interest has been gridded into cells. Each cell has one or more unknown variable. Each cell has a node. A graph of the nodes is represented by a sparse matrix. The graph is an initially decomposed into a pre-specified number of domains, such that each cell exists in at least one domain. The cells and domains are numbered. Each cell has a key, the key of each cell is the set of domain numbers to which the cell belongs. Each cell has a class, the class of each cell being the number of elements in the cell. The cells are grouped into connectors, each connector being the set of cells that share the same key. Each connector having a connector class, the connector class being the number of elements in the key of the connector. Each connector having only one higher-order neighbor connector is merged with such higher-order neighbor connector. The class of all locally maximum class connectors is reset to the maximum class of held by any connector. The maximum class connector is forced to contain only one cell. The connectors are ordered in increasing order of class. Interpolation operator and restriction operator are constructed from the ordered connectors. The interpolation operator and restriction operator are used to construct a coarse grid. The coarse grid may be used to determine the unknown variables of the cells.
摘要:
A method, system and apparatus are disclosed for conducting a reservoir simulation, using a reservoir model of a gridded region of interest. The grid of the region of interest includes one or more types of cells, the type of cell being distinguished by the number of unknown variables representing properties of the cells. The cells share a common variable as an unknown variable. The method includes the steps of identifying different cell types for the grid; constructing an overall matrix for the reservoir model based on the different cell types; at least partially decoupling the common variable from the other unknown variables in the matrix by using a reduction process to yield a reduced matrix; mathematically breaking up the variables in the reduced matrix into k subsets by cell types; applying an overlapping multiplicative Schwartz procedure to the reduced matrix to obtain a preconditioner and using the preconditioner to solve for the unknown variables.