Reservoir simulations : machine learning and modeling (Record no. 784)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01535nam a2200205Ia 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250408165437.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 230421s2020||||xx |||||||||||||| ||eng|| |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9780128209578 |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | English |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 S86 |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Sun, Shuyu |
| Relator term | Author |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Zhang, Tao |
| Relator term | Co-Author |
| 245 #0 - TITLE STATEMENT | |
| Title | Reservoir simulations : machine learning and modeling |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication | United Kingdom: |
| Name of publisher | Gulf Professional Publishing, |
| Year of publication | 2020. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | viii, 332p.; 21cms. |
| 500 ## - GENERAL NOTE | |
| General note | Reservoir Simulation: Machine Learning and Modeling helps the engineer step into the current and most popular advances in reservoir simulation, learning from current experiments and speeding up potential collaboration opportunities in research and technology. This reference explains common terminology, concepts, and equations through multiple figures and rigorous derivations, better preparing the engineer for the next step forward in a modeling project and avoid repeating existing progress. Well-designed exercises, case studies and numerical examples give the engineer a faster start on advancing their own cases. Both computational methods and engineering cases are explained, bridging the opportunities between computational science and petroleum engineering. This book delivers a critical reference for today’s petroleum and reservoir engineer to optimize more complex developments. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Artificial intelligence |
| General subdivision | Machine learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Artificial intelligence |
| General subdivision | Computer simulation |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Books |
| Withdrawn status | Lost status | Damaged status | Not for loan | Permanent Location | Current Location | Shelving location | Full call number | Accession Number | Koha item type |
|---|---|---|---|---|---|---|---|---|---|
| Dr. S. R. Ranganathan Library | Dr. S. R. Ranganathan Library | General Stacks | 006.31S86 | 2778 | Books |