Name
Abstract | ||
|---|---|---|
The degree of success of many oil and gas drilling, completion, and production activities depends on the accuracy of the models used in the reservoir lateral prediction and description. In this paper, a hybrid MPSO-BP-RBFN model for predicting reservoir from seismic attributes is proposed. The model in which every particle consists of binary and real parts is able to simultaneously search for optimal network topology (the number of hidden nodes) and parameters, as it proceeds. The model has been used to reservoir lateral prediction of a reservoir zone and proved the model's applicability. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-01507-6_69 | ISNN (1) |
Keywords | Field | DocType |
hidden node,reservoir lateral prediction,seismic attribute,hybrid mpso-bp-rbfn model,reservoir zone,production activity,real part,optimal network topology,gas drilling,adaptive,oil and gas,particle swarm optimization,network topology | Particle swarm optimization,Computer science,Network topology,Artificial intelligence,Reservoir computing,Drilling,Machine learning,Binary number | Conference |
Volume | ISSN | Citations |
5551 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 11 | 4 |