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Record W2273801783 · doi:10.2523/iptc-18533-ms

Fracture Characterization by Integrating Seismic-Derived Attributes including Anisotropy and Diffraction Imaging with Borehole Fracture Data in an Offshore Carbonate Field, United Arab Emirates

2015· article· en· W2273801783 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Petroleum Technology Conference · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsExxonMobil (Canada)
FundersAbu Dhabi National Oil Company
KeywordsGeologyBoreholeOverburdenSeismologyAnisotropyFracture (geology)PetrophysicsGemologySubmarine pipelinePetrologyEconomic geologyReservoir modelingEngineering geologyGeotechnical engineeringTectonics

Abstract

fetched live from OpenAlex

Abstract In this paper, we present a case study of fracture characterization by integrating borehole data with a variety of seismic attributes in a carbonate reservoir from a giant offshore field, United Arab Emirates. The objectives are to determine to what extent seismic data may be confidently used for mapping spatial distributions of subtle faults and fracture corridors in the reservoirs and to better understand the distribution of overburden anomalies (karsts, high impedance channels) for field development planning. Borehole data used in our study include information from core descriptions (fracture density and orientations), image logs, cross-dipole shear-wave anisotropy analysis, and dynamic data (well testing, PLT, tracer, and mud-loss). The seismic attributes include standard and advanced post-stack geometrical attributes; pre-stack seismic azimuthal AVO attributes, and recently developed pre-stack diffraction imaging. We find that there are common features that can be identified in different attributes, and the differences may indicate different scales of fractures. We also observe a qualitative correlation in the area of history match challenges and high anisotropy magnitude, where seismic anisotropy can identify relatively high fracture intensity regions/zones instead of pinpointing individual fractures and complements other attributes as differences do exist between seismically identified fracture zones and well data due to overburden anisotropy, resolution and sampling issues (which are addressed using the synthetic modeling approach). Diffraction attributes have revealed more detailed geological features in overburden (e.g. karsts) and reservoirs (e.g. lineaments) than in reflection data and a comparison with mud loss data in the shallow zones looks promising with a good correlation between mud loss and collapsed features. This work has provided an improved understanding of the applicability of the using multi-seismic attributes for fracture characterizations in carbonate reservoirs.

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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.923
Threshold uncertainty score0.770

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.257
Teacher spread0.232 · 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