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Record W2018725460 · doi:10.2118/110926-ms

Updating the Geomechanical Model and Calibrating Pore Pressure From 3D Seismic Using Data From the Gnu–1 Well, Dampier Sub-Basin, Australia

2007· article· en· W2018725460 on OpenAlexaff
Adrian White, Brett McIntyre, David Castillo, Julie Trotta, Marian Magee, C. Ward, P. M. O'Shea

Bibliographic record

VenueAsia Pacific Oil and Gas Conference and Exhibition · 2007
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsApache (Canada)
Fundersnot available
KeywordsGeologyWirelinePore water pressureDrillingBoreholeDrillContext (archaeology)GeomechanicsWell loggingSeismologyPetrologyPetroleum engineeringGeotechnical engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract A postmortem analysis of the Gnu–1 well was conducted to help understand the drilling experiences in the context of the pore pressure and stress profiles. The postmortem involved a review of the drilling experiences, the analysis of CAST image data, wireline log data and the LWD logs. This information was used to refine and verify a geomechanical model (in-situ stress, pore pressure and rock mechanical properties) in the vicinity of the Gnu–1 well. Of prime concern was the verification of the pre-drill pore pressure prediction previously undertaken using 3D seismic velocity data and offset well data. Wellbore failure and natural fracture analysis were an integral part of the postmortem. Wellbore breakouts seen in the image data allowed the pore pressure in the 8½" hole section of Gnu–1 to be constrained. Modelling using image data collected in the Athol Formation indicates that the pore pressure does not increase as rapidly as was estimated in the pre-drill study. Pore pressures in the North Rankin Formation and below were consistent with the pre-drill study. The geomechanical model was able to explain the losses seen in the Athol Formation in Gnu–1 when using the mud weights experienced by the open hole at the time of drilling.

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 score0.578
Threshold uncertainty score0.622

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 head0.038
GPT teacher head0.238
Teacher spread0.200 · 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
Published2007
Admission routes1
Has abstractyes

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