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Record W2033111616 · doi:10.2118/88988-pa

Subsea Development of Shallow, Low-Pressure Gas Reservoirs With High-Performance Well Designs

2004· article· en· W2033111616 on OpenAlexaff
Robert C. Burton, E. R. Davis, R. M. Hodge, Trey Gilbert, Robert Stomp, N. Abdelmalek, M. Bailey

Bibliographic record

VenueSPE Drilling & Completion · 2004
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsConocoPhillips (Canada)
Fundersnot available
KeywordsSubseaPetroleum engineeringGas pipelinePipeline transportEnvironmental scienceEngineeringMarine engineeringEnvironmental engineering

Abstract

fetched live from OpenAlex

Summary In the summer of 2000, ConocoPhillips began a program to develop a series of shallow, low-pressure gas reservoirs in the Indonesian waters of the west Natuna Sea. The project consists of a series of subsea wells linked by pipeline to a central mobile gas-processing and -compression unit that feeds a sales line to Singapore. The overall project life is 20 years and will require developing eight small fields with reserves on the order of 1 trillion scf gas. Initial project planning used conventional well designs to deliver rates on the order of 20 MMscf/D/well from a number of gas reservoirs. This type of well productivity required 10 wells to meet ConocoPhillips Indonesia's maximum contract supply rate, with several wells allocated to each reservoir. To improve project economics, a reduced well count employing high-performance completion designs was developed. The high-performance completions were designed to provide flow rates on the order of 100 MMscf/D at initial reservoir pressures ranging from 1,250 to 1,900 psi. These flow rates allowed well counts to be reduced to one well per reservoir. This paper will review ConocoPhillips's methodology for design and implementation of the first four high-performance completions in the west Natuna Sea gas project. Well deliverability and initial project results are discussed.

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 score0.209
Threshold uncertainty score0.786

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 head0.026
GPT teacher head0.238
Teacher spread0.212 · 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

Citations1
Published2004
Admission routes1
Has abstractyes

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