Challenges and solutions in stochastic reservoir modelling : geostatistics, machine learning, uncertainty prediction / Vasily Demyanov, Dan Arnold.
| Author/creator | Demyanov, Vasily author. |
| Other author | Arnold, Dan, author. |
| Other author | European Association of Geoscientists and Engineers, issuing body. |
| Format | Electronic |
| Publication | [Houten] : EAGE, 2018. |
| Description | 1 online resource (263 pages) : illustrations (black and white, and colour) |
| Supplemental Content | https://go.openathens.net/redirector/ecu.edu?url=https://doi.org/10.3997/book9789462822399 |
| Supplemental Content | GeoScienceWorld |
| Subjects |
| Summary | Many advances in stochastic reservoir modelling have been introduced in the past decade. Novel method of data integration & more accurate representation of geology have been developed with the advances in spatial statistics. However, integrated approach for predictive reservoir modelling still attracts continuous effort to manage reservoir decisions under uncertainty & make better use of the increasing amounts of data & domain knowledge accumulated in the field. Many solutions to these challenges lie in the cross-disciplinary vision, where modern rigour of computer science & statistics brought together with core geological & engineering domain expertise & basic physical conceptual thinking. This book aims to bridge across different fields - geostatistics, machine learning, & Bayesian statistics - to demonstrate the common grounds in solving challenging problems. |
| Bibliography note | Includes bibliographical references and index. |
| Spec. audience char. | Specialized. |
| Source of description | Description based on online resource; title from home page (viewed on June 20, 2024). |
| Issued in other form | Print version : 9789462822399 |
Availability
| Library | Location | Call Number | Status | Item Actions |
|---|---|---|---|---|
| Electronic Resources | ✔ Available |