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Record W1967894751 · doi:10.2118/73002-pa

A New Two-Dimensional Model for Acid-Fracturing Design

2001· article· en· W1967894751 on OpenAlexaff
A. Settari, R. B. Sullivan, Chris Hansen

Bibliographic record

VenueSPE Production & Facilities · 2001
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsFracture (geology)DiscretizationDolomiteField (mathematics)Dispersion (optics)Petroleum engineeringTransient (computer programming)MechanicsThermal conductivityGeologyMaterials scienceMathematicsMineralogyGeotechnical engineeringComputer sciencePhysicsMathematical analysisComposite material

Abstract

fetched live from OpenAlex

Summary The paper describes a 2D model of acid fracturing that is more rigorous than current acid-fracturing simulators. The equations are discretized and solved along fracture length and across the width (in x and y directions), as opposed to a lumped formulation in the conventional model in which the y-solution is replaced by a mass transfer coefficient Kg. In the 2D model, correlations for Kg are not needed, and the model uses directly measurable data only. In addition, the model can represent transient effects (Kg is variable along time and fracture length), combined with dispersion and thermal effects. Two models, one for lab experiments and one for field design, were developed. The lab model was validated, with excellent agreement, against the results of Roberts and Guin1 and compared with the 1D approach on data typical for phosphoria dolomite. The results show large differences in regions of high and low leakoff. Acid spending predicted by the 2D model is also higher, even when the Kg values are comparable. Correspondingly, the field-design model predicts larger spending and fracture conductivity. An example of the field design and post-fracture history match from the Cottonwood Creek Unit, Washakie County, Wyoming, is presented. It shows that the new model is in better agreement with field data. Extensions to 3D modeling are also discussed.

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: Methods · Consensus signal: none
Teacher disagreement score0.882
Threshold uncertainty score0.731

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.031
GPT teacher head0.239
Teacher spread0.208 · 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
GenreMethods

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

Citations35
Published2001
Admission routes1
Has abstractyes

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