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Record W2062639043 · doi:10.2118/170130-ms

Quantifying the Uncertainty Associated With Caprock Integrity during SAGD Using Coupled Geomechanics Thermal Reservoir Simulation

2014· article· en· W2062639043 on OpenAlexaboutno aff
Varun Pathak, David Tran, Anjani Kumar

Bibliographic record

VenueSPE Heavy Oil Conference-Canada · 2014
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsCaprockGeomechanicsGeologyPetroleum engineeringGeotechnical engineeringDeformation (meteorology)

Abstract

fetched live from OpenAlex

Abstract With a growing emphasis on safety of thermal operations in the Canadian oilsands operations, the focus of the industry on caprock integrity during SAGD has been increasing. A SAGD operation may create a high degree of deformation in the reservoir rock because of the stresses induced by pressure and temperature. This might yield to a failure of both the reservoir rock as well as the caprock. Thus, there is a need to understand the impact of various geomechanical and operational parameters on overall safety of a SAGD operation. In this paper, caprock failure during SAGD was modeled using a coupled simulator to integrate reservoir flow with geomechanical deformation. Representative Canadian oilsands properties were used for the simulation. Both shear and tensile failures were modeled and the surface heave/deformation was recorded. Sensitivity analyses and uncertainty assessments were performed to understand the effect of various geomechanical properties on caprock integrity and surface heave, including Young's modulus, Poisson's ratio and thermal pore pressure. At the same time, operating conditions such as well spacing and maximum injection pressure were also altered to see their effect on caprock integrity. This was done for different geological scenarios and different reservoir depths. Based on this study, we attempt to develop a generic workflow for quantifying the uncertainty associated with caprock integrity and minimizing the risk associated with caprock failures in any SAGD project.

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 score0.076
Threshold uncertainty score0.945

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 studies0.0010.000
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 head0.033
GPT teacher head0.237
Teacher spread0.204 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

Explore more

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