| Predicate |
Object |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F2119-14
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F2119-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F2113-08 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F30-27
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F30-28
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16C20-70
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16C20-30
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N21-3577
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N21-35 |
| classificationIPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F119-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F113-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F119-14 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G01N21-35
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G01N21-3577
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F30-28
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F30-27
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16C20-30
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16C20-70 |
| filingDate |
2021-05-11-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate |
2022-07-12-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationDate |
2022-07-12-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
CN-113239621-B |
| titleOfInvention |
A PVT Measurement Method Based on Elastic Net Regression Algorithm |
| abstract |
The present invention provides a PVT measurement method based on an elastic network regression algorithm, comprising steps 1. placing a downhole infrared spectrometer; step 2. obtaining the composition and relative content of crude oil in real time; step 3. inputting an elastic network regression model; The measured composition parameters are input into the model to obtain the PVT characteristics of the crude oil. The present invention trains three models respectively, these models are aimed at PVT characteristics: bubble point pressure, dissolved gas-oil ratio and oil layer volume coefficient, and the method of cross-validation and hyperparameter adjustment is used in all three models to ensure model accuracy and Robustness, and finally achieve PVT prediction of crude oil through input composition data. |
| priorityDate |
2021-05-11-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |