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Record W2030140892 · doi:10.2118/2003-075

Up-Scaling of Tracer Injection in Naturally Fractured Reservoirs

2003· article· en· W2030140892 on OpenAlexaff
Jiawei Xie, M. Pooladi‐Darvish

Bibliographic record

VenueCanadian International Petroleum Conference · 2003
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTRACERScalingGeologyPetroleum engineeringComputer sciencePhysicsMathematicsGeometry

Abstract

fetched live from OpenAlex

Abstract The objective of this study is to develop an upscaling methodology for simulation of tracer injection in naturally fractured reservoirs. 2-D fine grid models that explicitly incorporate fractures are used to represent the realistic fracture distribution from geostatistical characterization. The objective is to determine equivalent properties of the fractured systems, which, as included in conventional double porosity models, will result in a behavior similar to the fine-grid response. The upscaling methodology is based on flow simulation with constant pressure and constant flux boundary conditions. The upscaling parameters of each equivalent double-porosity grid are determined by matching results of the fine grid simulation. The effects of some important factors such as coarse grid size, injection rate, and well location are examined to show the robustness and accuracy of the upscaling methodology. Introduction Geological characterization of fractured reservoirs has progressed considerably in recent years, allowing for realistic representation of fracture networks. Despite advances in reservoir simulation and computer hardware, such detailed geostatistical results cannot be used directly in the simulation of fractured reservoirs. Up-scaling techniques are needed to translate the geostatistical data into reservoir simulation parameters. There are several upscaling techniques (1–4) used in the simulation of single-porosity reservoirs to reproduce the details of small-scale fluid mechanics and reservoir heterogeneity in coarse-grid models. However, there is no general procedure that is widely accepted for upscaling. The upscaling methods for naturally fractured reservoirs are further behind. Based on the type of fracture system, the upscaling study of naturally fractured reservoirs can be divided into two categories: the single continuum, and the dual continuum approach. For the single continuum approach (5–7), the fractured system is considered as a heterogeneous matrix system with discrete fractures. Various methodologies, such as the boundary element and finite volume methods, are widely used to deal with complex fracture systems. Most single continuum methods consider single-phase upscaling only, and treat the upscaled permeabilities as tensor terms. The dual continuum approach (8–11) is based on the double porosity model of Warren (12) and Root, and its representation in the simulation of fractured reservoirs (13). The dual continuum upscaling therefore requires determination of the properties required for double porosity simulation of the fractured system. Most of upscaling research concentrates on obtaining equivalent properties for a single-porosity simulation of a fractured system. Upscaled single-porosity models do not exhibit some of the important characteristics of naturally fractured reservoirs. In particular, a single-porosity model cannot distinguish between the displacing fluid fronts in the fracture and the matrix. The dual continuum approach, however, includes the physics for modeling unsteady-state multiphase fluid exchange between the rock matrix and fractures. In this work, we will obtain the equivalent double-porosity parameters for a tracer displacement in a fractured porous medium. In the following, the upscaling framework, including fractured systems used in our study, is presented first. The upscaling methodology and results are described in detail.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.224
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.221
Teacher spread0.211 · 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 teacher head, 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
Published2003
Admission routes1
Has abstractyes

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