Use of connection constraints for checking and enhancing geological models
Bibliographic record
Abstract
Research Article| December 01, 2015 Use of connection constraints for checking and enhancing geological models Jean-Marc Chautru; Jean-Marc Chautru Geovariances, 49bis avenue Franklin Roosevelt, 77215 Avon, CEDEX, France Search for other works by this author on: GSW Google Scholar Renaud Meunier; Renaud Meunier Geovariances, 49bis avenue Franklin Roosevelt, 77215 Avon, CEDEX, France Search for other works by this author on: GSW Google Scholar Hélène Binet; Hélène Binet Geovariances, 49bis avenue Franklin Roosevelt, 77215 Avon, CEDEX, France Search for other works by this author on: GSW Google Scholar Matthieu Bourges Matthieu Bourges Geovariances, 49bis avenue Franklin Roosevelt, 77215 Avon, CEDEX, France Search for other works by this author on: GSW Google Scholar Author and Article Information Jean-Marc Chautru Geovariances, 49bis avenue Franklin Roosevelt, 77215 Avon, CEDEX, France Renaud Meunier Geovariances, 49bis avenue Franklin Roosevelt, 77215 Avon, CEDEX, France Hélène Binet Geovariances, 49bis avenue Franklin Roosevelt, 77215 Avon, CEDEX, France Matthieu Bourges Geovariances, 49bis avenue Franklin Roosevelt, 77215 Avon, CEDEX, France Publisher: Canadian Energy Geoscience Association Received: 27 Nov 2014 Accepted: 25 May 2015 First Online: 13 Jul 2017 Online ISSN: 2368-0261 Print ISSN: 0007-4802 © the Society of Canadian Petroleum Geologists Bulletin of Canadian Petroleum Geology (2015) 63 (4): 358–373. https://doi.org/10.2113/gscpgbull.63.4.358 Article history Received: 27 Nov 2014 Accepted: 25 May 2015 First Online: 13 Jul 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn Email Permissions Search Site Citation Jean-Marc Chautru, Renaud Meunier, Hélène Binet, Matthieu Bourges; Use of connection constraints for checking and enhancing geological models. Bulletin of Canadian Petroleum Geology 2015;; 63 (4): 358–373. doi: https://doi.org/10.2113/gscpgbull.63.4.358 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyBulletin of Canadian Petroleum Geology Search Advanced Search Abstract To ensure consistency between geological models and dynamic models, it is necessary to constrain geological models to information coming from dynamic synthesis about permeable pathways between some points in the reservoir. The paper presents a methodology which can be fully implemented using commercial software. It is based on the analysis of connected components calculated on geostatistical simulations in a post-processing phase. The analysis of physical connections in a single lithostratigraphic unit is studied. The use of connected components to QC facies or petrophysical properties simulations is detailed and the impact of simulation parameters (facies proportion, variogram range, etc...) on the presence of permeable pathways in the static model is studied. The generalization to structural geological models is described. In this case, successive lithostratigraphic units can be potentially connected through faults when the fault throw is large enough. A two-steps workflow for conditioning simulations to information about connections between points in difficult cases is presented. The first step is the identification of the realizations matching the connection criteria. The second step consists in choosing additional conditioning data for further simulations ensuring that the wells connection constraints are honored, the model’s statistical properties being preserved. The efficiency of this workflow is discussed. A method for integrating faults and fractures patterns in calculations in complex cases is proposed. Once the stochastic realizations of a geostatistical model honor observed connections between selected points, it is interesting to characterize the connection for improving model QC. Some possible ways of using connected components in advanced models QC are suggested. In the end, some ideas for accounting for connection characteristics in geostatistical simulations are proposed. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.006 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".