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Record W2009029568 · doi:10.2118/63080-ms

Design, Implementation, and Analysis of Multilayer Pressure Transient Tests in White Rose Field

2000· article· en· W2009029568 on OpenAlexaffabout
Gökhan Coşkuner, Leon B. Ramler, Murray W. Brown, D. Wayne Rancier

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2000
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsPetro-CanadaHusky Energy (Canada)
Fundersnot available
KeywordsStructural basinGeologyOil fieldPetroleum engineeringTransient (computer programming)DrillingFault (geology)Transient analysisWell loggingSeismologyEngineeringTransient responseGeomorphologyElectrical engineeringComputer science

Abstract

fetched live from OpenAlex

Abstract The White Rose field is located in the Jeanne d’Arc Basin, lying some 350 km off the east coast of Newfoundland. The basin has proven to be the only area off the East Coast of North America with established major oil accumulation potential. The discovery well in the south area of the structure, the E-09, was drilled in 1988 encountering some 300 m continuous sand interval with an oil column of 135 m. Although there was a potentially significant amount of oil distributed in several fault blocks, the productive capacity of future development wells was uncertain based on the five conventional pressure transient tests conducted. Following a 3D seismic program, a delineation program commenced in 1999 and three delineation wells were drilled geologically confirming over 600 MMSTB oil in place in the south. The wells were extensively cored and logged. Two sand intervals in the first well, the L-08, were tested using the conventional pressure transient testing technology. However, it was decided that a multilayer test (MLT) could be employed in the second well, the A-17, leading to significant cost savings when compared with conventional testing. The A-17 well was logged to evaluate the formation as well as the cement bond. Four distinct pay intervals were observed and it was decided that the properties of these layers could be measured by conducting an MLT while also measuring the flow profiles with a Production Logging Tool (PLT). The test was successfully conducted and analyzed. Consequently, the rig time to test the well was reduced by at least 16 days, yielding savings of C$8 million. Given the success of the technique in the second well, the third delineation well, the N-30, was also tested using the MLT technique, again with significant cost savings. This paper discusses the design, implementation and the analysis of MLT's conducted in the A-17 and N-30 wells.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.014
GPT teacher head0.270
Teacher spread0.257 · 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

Citations1
Published2000
Admission routes2
Has abstractyes

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