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Record W2082288688 · doi:10.4043/18177-ms

Anisotropy Evaluation Using an Array Walk-Away VSP

2006· article· en· W2082288688 on OpenAlexaffabout
Runnong Zhou, Ayiaz Kaderali

Bibliographic record

VenueOffshore Technology Conference · 2006
Typearticle
Languageen
FieldEngineering
TopicEngineering Applied Research
Canadian institutionsHusky Energy (Canada)
Fundersnot available
KeywordsAnisotropyComputer scienceGeologyPhysicsOptics

Abstract

fetched live from OpenAlex

Abstract VSP surveys provide the best way to measure anisotropic parameters for formations with transverse isotropy. Two types of anisotropic parameters can be determined from a walkaway VSP survey: effective parameters for overburden formations above and below VSP receivers, and local parameters for formations within the depth range of a receiver array. Each type has advantages and disadvantages. We use a case study to demonstrate the determination of these two types of anisotropic parameters and use them to build anisotropic depth profiles. Walkaway VSP data were acquired offshore East Coast Canada. The receiver array was centered at a depth of 2051 m and covered a vertical depth range of 143 m. The survey well was deviated 46 °from vertical at the array and rotated 61 ° from the plane of the shot lines. The maximum sourcereceiver offset was 4000 m. Effective anisotropic parameters were calculated from direct and reflected P-wave traveltimes using traveltime inversion. Local anisotropic parameters were computed from the direct P-wave data using a local phase slowness analysis technique. The estimated effective anisotropic parameters Õand ?were 3.7% and 16.7% for the overburden. The estimated local parameters Õ and e were 5.6% and 20.7% over the receiver depth range, confirmed by the interval parameters computed using traveltime inversion for the same depth range. The effective parameters Õand?were -1.9% and 23.8% for the formations just below the array. Combinations of these parameters, in conjunction with well log and seismic data, were used to build anisotropic profiles. This case study shows that the joint use of local slowness and traveltime inversion methods reliably allows theextraction and maximization of anisotropy information from walkaway VSP data. It provides a means to build anisotropic profiles that best approximate formation anisotropy. Introduction Transverse Isotropy with a Vertical symmetry axis (TIV or VTI) is a relatively common type of anisotropy observed in seismic data. A walkaway VSP (WVSP) has a wide angular aperture and well-determined direct arrivals. By placing receivers within the suspected anisotropic zone, a walkaway VSP offers the best dataset available for in-situ seismic anisotropy measurements. Three parameters define weak P-wave anisotropy in a general VTI medium; the P-wave velocity in the direction of the symmetry axis, V0, and the Thomsen parameters Õand ? 1. For horizontally layered VTI formations, the vertical interval velocity function Vv obtained in a VSP velocity survey is a good approximation for V0. The anisotropic parameters Õand ?can be determined from WVSP traveltime and polarization data using a variety of techniques 2, 3, 4. Three sets of anisotropic parameters can be determined from WVSP; effective parameters for overburden formationsabove VSP receivers, local parameters for formations within the depth range of a receiver array, and effective parameters for formations below the array. In practice, receivers are usually placed in a small depth range in the borehole because of acquisition constraints and economic conditions. In such cases, local anisotropic parameters can be derived for a limited depth range.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.275
Teacher spread0.245 · 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 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".

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Citations4
Published2006
Admission routes2
Has abstractyes

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