http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-11422284-B2
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_c15bf302cfd15f3b63bc4b9c5d8b9ccf |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-088 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-04 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-0436 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01V99-005 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-0454 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-043 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F30-20 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F30-20 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G01V99-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04 |
| filingDate | 2018-10-11-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate | 2022-08-23-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_29634c11583adaccf2645fa57e480218 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_b30a69168fef46fa572847d28dc00513 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_931adc7de8e3ed5c0f0b5a118294302e |
| publicationDate | 2022-08-23-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | US-11422284-B2 |
| titleOfInvention | System for improved reservoir exploration and production |
| abstract | An architecture for predicting and modeling geological characteristics of a reservoir includes one or more neural networks, a static modeling module, a dynamic modeling module, and a fuzzy inference engine to provide recommendations for drilling a wellbore. The neural networks receive log data for coordinates along a well trajectory, and determine a geophysical relationship for a property of a subterranean formation as a function of distance vectors between the coordinates along the well trajectory and one or more sets of randomly generated coordinates. The static modeling module generates three-dimensional static models of a volume of interest based on predicted properties of formations residing therein from the neural networks. The dynamic modeling module determines connectivity values between clusters of formations based on nodal connectivity of neighboring clusters, assigns pressure values across the volume of interest, and generates a three-dimensional dynamic model for the volume of interest based on the pressure values. |
| priorityDate | 2017-10-11-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 33.