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Record W2030333811 · doi:10.2118/170152-ms

The Successful Application of Automated Managed Pressure Drilling (MPD) To Protect Caprock Integrity by Narrow Margin Drilling in SAGD wells

2014· article· en· W2030333811 on OpenAlexaffabout
Nadine Osayande, Elvin Mammadov, Sheldon Sephton, Vincent Boucher

Bibliographic record

VenueSPE Heavy Oil Conference-Canada · 2014
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsSuncor Energy (Canada)
Fundersnot available
KeywordsDrillingCaprockPetroleum engineeringUnderbalanced drillingMeasurement while drillingDirectional drillingSteam-assisted gravity drainageDrillWellboreWell controlDrilling fluidGeologyMining engineeringEngineeringAsphaltOil sandsMechanical engineering

Abstract

fetched live from OpenAlex

Abstract The Canadian SAGD (Steam Assisted Gravity Drainage) industry continues to grow with more wells being drilled in Alberta that cannot be mined. Drilling issues addressing low reservoir pressure, tight well spacing, highly pressurized steam injection, cap rock integrity and tight drilling windows are currently some of the challenges that are being faced today. As the industry looks for a way to improve and mitigate both drilling and environmental issues, this paper presents a drilling solution on applying an automated MPD (Managed Pressure Drilling) technique proven to identify and react to the actual wellbore pressures. This can detect and control any gain and losses within liters, while still having the ability to maintain a CBHP (Constant bottom hole Pressure) while drilling through tight windows. This document demonstrates the successful application of advanced automated MPD technologies on Suncor's Dover well close to Fort McMurray, Alberta and will also elaborate on recommended operational procedures, equipment set up and to process flow diagrams along with the analyzed graphical data. The results demonstrated a successful re-drill of a producer well within an over pressured reservoir induced by steam injection, by using near water density mud weights, minimizing mud losses, preventing any steam or bitumen intrusion by monitoring return density to surface while maintaining bottom hole pressure within a narrow operating window.

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 categoriesMeta-epidemiology (narrow)
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 score0.178
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.005
GPT teacher head0.183
Teacher spread0.178 · 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.

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
Published2014
Admission routes2
Has abstractyes

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