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Record W4412989953 · doi:10.56952/arma-2025-0689

Does High Performance Water Based Mud Offer Superior Wellbore Strengthening Benefits Compared to Traditional Synthetic Based Muds?

2025· article· en· W4412989953 on OpenAlexaff
Gerard O’Reilly, Perapon Fakcharoenphol, Alvin W. Chan, Seung-Oh Lee, Jonathan J. Brege, Kenneth Willman Oyler, Bob Wiggins

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsShell (Canada)
Fundersnot available
KeywordsWellborePetroleum engineeringWater cutComputer scienceEnvironmental scienceGeology

Abstract

fetched live from OpenAlex

ABSTRACT: Depleted Fracture Gradients have been notoriously challenging for drilling, cementing and completions operations for several decades. Depletion continues to become more severe and we expect to need to deliver successful fluid systems for overbalances of between 12,000 and 14,000 psi within the next few years. Many solutions have been proposed, but the majority of deepwater drilling operations have been limited to Fracture Widths (FW) of between 1000-1400 microns when utilizing background Lost Circulation Material (LCM). The plugging of larger fracture widths have been routinely achieved when an open hole squeeze is performed across depleted sands that are exposed, but the operators experience is that properly calibrated models show that the current real time drilling limit is in this range. Additionally, when losses occur with a traditional SBM fluid system, the downhole pressure quickly falls to the minimum horizontal stress for the loss zone, and recovering full strength is very difficult. This paper will demonstrate how a High-Performance Water Based Mud (HPWBM) can be designed to withstand extreme overbalance (>9000 psi) and successfully inhibit against shales in the section. It will also discuss the execution in the reservoir sections of three deepwater wells where HPWBM fluid systems were successfully deployed. Lost circulation was induced in all three instances and this paper will demonstrate the benefits of using a HPWBM compared to an SBM for drilling through larger fractures induced by greater overbalances and recovering from major lost circulation events.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.174
Teacher spread0.165 · 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 designObservational
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
Published2025
Admission routes1
Has abstractyes

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