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Record W4403587036 · doi:10.2118/223337-ms

Managed Surface Temperature/Pressure in Real-Time Drilling for Gas Migration and Wellbore Stability Detection in Subsurface

2024· article· en· W4403587036 on OpenAlexaff
Yarlong Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsPetro Geotech (Canada)
Fundersnot available
KeywordsWellborePetroleum engineeringDrillingGeologyStability (learning theory)Computer scienceEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Wellbore stability and gas migration detection from a gas-saturated formation are important for engineer during production and drilling. Erroneous onset of gas migration and wellbore collapse may be predicted if no accurate bottom hole pressure (BHP) is obtained in real time, despite a reliable geomechanics and gas migration models are utilized. Pressure along a tubing-annulus system must be calculated from the surface to the bottom hole, where thermal-hydraulic-mechanical (THM) may be coupled through wellbore wall to those inside the porous formations. A wellbore-reservoir model including tubing-annulus-reservoir system is established so that a pressure profile coupled to the inflow/outflow of the gas-saturated porous formations may be achievable. Coupled to each other inside the tubing-annulus system, thermal-hydraulic (TH) is managed and coupled to those THM in the formation so that wellbore stability and gas influx from the formation and migration along the annulus are forecasted. Along managed pressure drilling (MPD), managed temperature (MT) is also critically important both for BHP calculation so that the wellbore stability and blowout are accurately predicted. Temperature changes in the circulated fluid may affect the BHP calculation and stress concentration at wellbore wall. In an environment of narrow drilling mud window, MT may enlarge the window and reduce the active stress concentration at wellbore wall at the same time. Along a comprehensive reservoir-wellbore model, an engineering procedure with integrated logging-geomechanics model for inputs is proposed. In this procedure, WITSMAL data reading, MEM model buildup, and wellbore collapsing/blowout detection is incorporated. Real-time WITSMAL data is processed and transferred into MEM model so that real-time critical pressure may be predicted in formation ahead of the drilling bit. Both the MEM model and wellbore stability in the drilled section are examined and corrected if erroneous results are detected. A prediction ahead of the drilling bit is forecast and safe mud weight is recommended or adjusted if pre-design mud is not adequate. A novel logging-drilling procedure focusing on geomechanics is proposed. Reading real-time WITSMAL data and modifying MEM model, on which a real-time critical wellbore pressure may be verified, wellbore stability and migrated gas before blowout in drilled section may be identified. All these processes are critically helped by MT and such a real-time modification may allow an accurate forecasting on wellbore instability and blowout risk in the section ahead of drilling bit.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.006
GPT teacher head0.197
Teacher spread0.191 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2024
Admission routes1
Has abstractyes

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