Stochastic regridding of geological models for flow simulation
Bibliographic record
Abstract
Research Article| December 01, 2015 Stochastic regridding of geological models for flow simulation Saina Lajevardi; Saina Lajevardi Department of Civil & Environmental Engineering University of Alberta 5-052 Markin/CNRL NREF Edmonton, AB T6G 2W2 Search for other works by this author on: GSW Google Scholar Clayton V. Deutsch Clayton V. Deutsch School of Mining and Petroleum Engineering University of Alberta 3-133 Markin/CNRL NREF Edmonton, AB T6G 2W2 Search for other works by this author on: GSW Google Scholar Author and Article Information Saina Lajevardi Department of Civil & Environmental Engineering University of Alberta 5-052 Markin/CNRL NREF Edmonton, AB T6G 2W2 Clayton V. Deutsch School of Mining and Petroleum Engineering University of Alberta 3-133 Markin/CNRL NREF Edmonton, AB T6G 2W2 Publisher: Canadian Society of Petroleum Geologists Received: 20 Nov 2014 Accepted: 26 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): 374–392. https://doi.org/10.2113/gscpgbull.63.4.374 Article history Received: 20 Nov 2014 Accepted: 26 May 2015 First Online: 13 Jul 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Saina Lajevardi, Clayton V. Deutsch; Stochastic regridding of geological models for flow simulation. Bulletin of Canadian Petroleum Geology 2015;; 63 (4): 374–392. doi: https://doi.org/10.2113/gscpgbull.63.4.374 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 Regridding geological models to a higher resolution for flow simulation is an important problem in geostatistical modeling. For practical reasons, over a large area, models can only be built at a relatively coarse resolution. Subsequently, the resolution of specified regions of interest must be increased before upscaling for flow modeling. The construction of a high-resolution model of the entire reservoir at the beginning of the evaluation may be impractical because of computational and time constraints. It is standard practice to implement nearest neighbor interpolation to increase the resolution of models. Although it is a simple practical solution, nearest neighbor interpolation introduces spatial continuity artifacts that are often unrealistic. This paper proposes an automatic stochastic regridding approach based on simulation. The simulation is conditioned to the initial coarse resolution model/realization. The process includes the extraction of specified regions of interest, definition of corresponding local variography, and implementation of Sequential Gaussian Simulation (SGS) and/or Sequential Indicator Simulation (SIS) to characterize continuous and categorical variables, respectively. In each specified region, the local variography can be defined by either implementing automatic fitting algorithms or assigning the global variography initially used to build the coarse resolution model. The regridding process is automated. The advantage of this approach over the conventional nearest neighbor interpolation is in the improvement in the realistic spatial variability features of small scale geologic heterogeneity. The benefits of obtaining a proper regridded model are discussed in a case study of a fluvial reservoir in the McMurray formation. One of the main reasons for generating high resolution models is in the appropriate characterization of small scale impermeable geobodies such as remnant shales. The coarse resolution models are not able to properly characterize the small scale geologic features of the shales; more amount of information is required to characterize smaller scale features. The metric of performance considered is the effective vertical permeability. The automated stochastic regridding workflow described in this paper is available on a Fortran platform with additional scripting which will be distributed upon request. Note that the terms “regridding” and “stochastic regridding” are used interchangeably and both refer to the proposed workflow of modeling at higher resolution. 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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".