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Record W2036681929 · doi:10.2118/136697-pa

A 3D Finite Element Model for History Matching Hydraulic Fracturing in Unconsolidated Sands Formation

2010· article· en· W2036681929 on OpenAlexafffund
Bin Xu, R.C.K. Wong

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2010
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Calgary
FundersUniversity of Calgary
KeywordsHydraulic fracturingGeomechanicsGeologyGeotechnical engineeringFinite element methodPermeability (electromagnetism)Petroleum engineeringOil sandsEngineeringMaterials scienceStructural engineering

Abstract

fetched live from OpenAlex

Abstract Field hydraulic fracturing tests data provide valuable information regarding the mechanical and hydraulic behaviours of the reservoir formation. By history matching the field bottomhole pressure vs. time curve from hydraulic fracturing tests, a set of field-calibrated geomechanical models can be obtained, which is an important asset for any further works on utilizing geomechanics to enhance the injection and production performance. This paper presents a 3D finite element model for history matching the complete bottomhole pressure vs. time curve generated during hydraulic fracturing tests, considering the injection rate as input. To simulate the hydraulic fracturing process in unconsolidated sands formation, a poro-elasto-plastic constitutive model, together with a strain-induced anisotropic full permeability model, are formulated and implemented into a 3D finite element geomechanical simulator. Unlike the conventional simulation of hydraulic fracturing in hard rock, hydraulic fracturing in an unconsolidated sands reservoir is simulated as a large area of shear dilated plastic zone, inside of which the effective stresses are low and hydraulic conductivities are high. It is shown that the proposed numerical model can capture the hydraulic fracture initiation and propagation in unconsolidated sands formation and matches the field pressure vs. time curve.

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 score0.194
Threshold uncertainty score0.818

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.199
Teacher spread0.192 · 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

Citations27
Published2010
Admission routes2
Has abstractyes

Explore more

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