MétaCan
Menu
Back to cohort
Record W2050351337 · doi:10.2118/07-07-01

Upscaling Using a Non-Uniform Coarsened Grid With Optimum Power Average

2007· article· en· W2050351337 on OpenAlexaff
Silva Merchán, Sanjay Srinivasan

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2007
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Mathematical Modeling in Engineering
Canadian institutionsNexen (Canada)
Fundersnot available
KeywordsGridComputer scienceScale (ratio)Computational scienceFlow (mathematics)Reservoir simulationProcess (computing)AlgorithmGeologyPetroleum engineeringMathematicsGeometry

Abstract

fetched live from OpenAlex

Abstract Geostatistical modeling techniques are capable of generating high-resolution reservoir models. Since a limited amount of information is available to model the reservoir, geological uncertainty is represented through a suite of equally probable models. Unfortunately, these high-resolution models are often too large to process through numerical flow simulators. Upscaling methods are required that reduce the size of detailed models while preserving the important geological characteristics of the reservoir. Most upscaling methods currently in vogue work with uniform grids. A new upscaling approach based on non-uniform coarsening with optimum power average is presented. The proposed algorithm identifies likely high connectivity regions using streamline simulations, then constructs a non-uniform coarse-scale grid preserving the areas with probable high connectivity and assign equivalent permeability to the coarse grid blocks using an optimum power average technique. The power average exponent is calibrated using the data from a series of single-phase flow simulations. Introduction Upscaling is a procedure that transforms a detailed geological model to a coarse grid simulation model such that the flow behaviour in the two systems is similar. Upscaling is required because fine-scale flow simulation of multiple geostatistical realizations can be CPU (computer processing unit) expensive. Any upscaling procedure involves basically two steps:gridding, to define the new coarse blocks, andaveraging or estimation of properties, to preserve the local geologic details. Numerous upscaling methods have been reported in the literature(1). Nevertheless, efficient and accurate estimation of equivalent rock properties of coarse-scale from geological data at fine-scale remains an active area of research. The simplest numerical procedure for the determination of equivalent permeability involves the solution of the Laplace equation for pressure within the reservoir domain, subject to constant pressure gradient in the direction of flow and no flux perpendicular to it. The limitation of these conditions is that the cross terms of the K tensor (Kxy and Kyx in 2D systems) cannot be determined. Despite this limitation, the approach continues to be used assuming that the diagonal terms of K tensor that are computed are correct and the cross-terms are not important. This is true if the coordinate's direction (i.e. x and y) coincides with the principal directions of the effective permeability tensor. Unfortunately this is not usually the case or known a priori and it can change from one location to another. To overcome this limitation, Durlofsky(2) presented a numerical procedure for the determination of equivalent grid block permeability tensors. The method entails solution of the fine-scale pressure equation, subject to periodic boundary conditions. Symmetric, positive definite equivalent permeability tensors are obtained. A numerical approach to obtain a full tensor consists of using linear boundary conditions(3,4). A pressure gradient is imposed in the flow direction and a linear pressure profile is enforced on the two other opposite faces. This variation results in a non-symmetrical permeability tensor taking into account the cross-flow term. Despite improved representation of flow in heterogeneous media using a permeability tensor, the pressure solver techniques employ approximations such as single-phase flow and simplified boundary conditions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.009
GPT teacher head0.222
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Canadian Petroleum TechnologySame topicAdvanced Mathematical Modeling in EngineeringFrench-language works237,207