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Record W1996647171 · doi:10.2118/80455-ms

Under-Balanced Drilling Experience in a Shallow Clastic Oil Field, Offshore Sabah, South China Sea

2003· article· en· W1996647171 on OpenAlexaboutno aff
Gilles David Bourgeois, J. Chu, Jan Hendrik Terwogt, Awang Kasumajaya Mahran, Alberthnego Wisnugroho

Bibliographic record

VenueSPE Asia Pacific Oil and Gas Conference and Exhibition · 2003
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsSubmarine pipelineClastic rockPetroleum engineeringIncentiveOffshore drillingGeologyEngineeringRisk analysis (engineering)Geotechnical engineeringBusinessPaleontology

Abstract

fetched live from OpenAlex

Abstract Following encouraging results of Under-Balanced Drilling ("UBD") applications in the North Sea and onshore Canada, Shell Malaysia Exploration and Production ("SM-EP") embarked upon the application of this technology in a shallow clastic oil field in offshore Sabah. Relatively little information is available in the public domain regarding the effectiveness of UBD in clastic oil reservoirs. This paper provides factual evidence relevant to the assessment of UBD-induced added value. The paper also provides a comparison between the cost and benefit of UBD with that of Under-Balanced Perforating ("UBP") in a two-well trial. Every pressure regime, reservoir fluid type, geological circumstance, etc, requires a specific operational configuration custom made for the situation at hand. This is a major source of UBD-induced incremental cost. Different reservoirs require different UBD configurations so analogies that assist the business case of a UBD project are currently limited and are difficult to find. This was the first offshore application of crude oil and hydrocarbon gas injection as drilling fluid in jointed pipe operations. A description of the incentives and risks of our specific geological circumstances is provided. Detail will be provided regarding our UBD design, main operational-risk mitigation measures, and the strategy used to appraise the added value of UBD. The complexities, incentives, and pitfalls of our UBD strategy will be chronicled. The initial results of this first-ever UBD trial within SM_EP will be published, along with the lessons that we have learned.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.710
Threshold uncertainty score0.858

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 integrity0.0000.000
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 head0.013
GPT teacher head0.207
Teacher spread0.193 · 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

Citations4
Published2003
Admission routes1
Has abstractyes

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