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Record W2916337404 · doi:10.2118/0318-0087-jpt

Technology Focus: Seismic Applications (March 2018)

2018· article· en· W2916337404 on OpenAlexaboutno aff
Mark S. Egan

Bibliographic record

VenueJournal of Petroleum Technology · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological Modeling and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsAzimuthFocus (optics)Computer scienceHydrophoneAccelerationInterpolation (computer graphics)Sampling (signal processing)AliasingGeologyAcousticsMarine engineeringAerospace engineeringTelecommunicationsComputer graphics (images)EngineeringPhysicsOpticsAnimation

Abstract

fetched live from OpenAlex

Technology Focus Many innovative advances in the seismic method have been introduced over recent years. In this discussion, I will focus on the topic of sampling. A key example is the azimuthal sampling in full-azimuth 3D surveys—surveys that are needed, for instance, to characterize fractures. Full-azimuth geometries typically call for an expensive explosion in the amount of data needing to be acquired. A way to reduce the acquisition cost is to rely instead on interpolation, but aliasing issues limit the spatial frequencies that can be recovered. In the marine world, an innovation to address this has been to collect crossline-oriented, particle-acceleration measurements in addition to the usual pressure measurements (made by hydrophones). Particle acceleration is related directly to the spatial gradient of the pressure. Knowing both the pressure and the pressure derivative extends the ability for successful interpolation. Another innovation in use today is to abandon the traditional inline/crossline field geometry by shooting in circular tracks instead. Regardless of whether straight lines or circular tracks are used, platforms and other obstructions block access to towed streamers. Placing receiver nodes on the seabed is a very popular way around this problem when corals are not present. Otherwise, wave- and solar-powered unmanned surface vehicles provide a new, tantalizing alternative innovation. Each of these bathtub-sized crafts independently tows a hydrophone array. In the onshore world, one of the big advances for addressing increased sampling requirements has been to go to 24-hour shooting with continuous recording of simultaneously sweeping vibrators. This has been enabled by innovations in both acquisition and processing. In acquisition, the introduction of vibrator command-and-control systems means that the vibrator drivers no longer must wait for the start signal from the recording truck. In processing, the development of deblending algorithms enables the overlapping field records to be separated. These advances dramatically increase the number of records that can be acquired each day—especially in desert regions. An innovation for improving this efficiency even further is to skip over some of the source and receiver positions in a carefully specified, random-looking fashion adopted from the science of compressive sampling. (By relying on sparsity in an appropriate transform domain, data processing can reconstruct the seismic signal adequately from the reduced data set.) Finally, yet another form of sampling of interest today is the time interval between consecutive monitor surveys in 4D programs. Recent innovations enable frequent, lower-cost monitor surveys to see small, rapid changes in deepwater reservoirs. For more information, see the featured papers. Recommended additional reading at OnePetro: www.onepetro.org. SPE 187203 Look-Ahead Geosteering By Means of Real-Time Integration of Logging-While-Drilling Measurements With Surface Seismic by F. Arata, Eni, et al. SPE 184029 Seismic Airborne TEM Joint Inversion and Surface Consistent Refraction Analysis: New Technologies for Complex Near-Surface Corrections by Daniele Colombo, Saudi Aramco, et al. URTeC 2670158 The Use of Time-Lapse Seismic Attributes for Characterizing Hydraulic Fractures in a Tight Siltstone Reservoir by N. Riazi, University of Calgary, et al.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.667
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.011
GPT teacher head0.229
Teacher spread0.218 · 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 designOther design
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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Citations0
Published2018
Admission routes1
Has abstractyes

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