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Rock physics modelling of siliciclastic shallow-marine clinoforms on the New Jersey continental shelf using 3D seismic data and well logs

2020· article· en· W3042802961 on OpenAlexaff
Masoud Aali, M. R. Nedimović, Craig S. Fulthorpe, Gregory S. Mountain, Bill Richards, James A. Austin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsDalhousie University
Fundersnot available
KeywordsSiliciclasticGeologyPetrophysicsContinental marginPaleontologyBoreholeSedimentary rockContinental shelfSedimentary depositional environmentTectonicsPassive marginSedimentGeomorphologyPetrologyOceanographyRiftGeotechnical engineering

Abstract

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Heterogeneities within clinoforms, which can be in the form of variations in internal geometry and/or change in sediment distribution, may act as fluid flow barriers or conduits for hydrocarbon or freshwater reservoirs. These heterogeneities can lead to considerable uncertainty in estimating pore-fluid recovery factors by up to 35%. And yet, variations in sediment petrophysical characteristics within clinoforms have been poorly documented. Understanding the rock physics of clinoform heterogeneities along continental margins is a key to reducing the uncertainties in predicting the dynamics and the volume of recoverable pore fluids within these structures. The Miocene sedimentary record of the New Jersey continental margin is a prime candidate for studying continental-margin clinoform structures and the variation in their petrophysical properties. The margin has experienced a stable tectonic history, smooth and gradual thermal subsidence, and continuous sediment loading in the Miocene, resulting in deposition of well-developed siliciclastic clinoformal sequences. We use data from three IODP Expedition 313 boreholes and an encompassing high-resolution 3D multichannel seismic volume, collected in 2015 by the R/V M.G. Langseth on the New Jersey continental shelf, to predict shallow-marine sedimentological properties of the Miocene clinoforms at a significantly higher resolution (~5 m laterally) than previously achieved (~100s of m). We identify 76 system tracts and 22 sequences spanning ~8 m.y. of the Miocene. The results of our 3D stratigraphic analysis provide a detailed structural framework for analyses of the Miocene deposits to: 1) define the sedimentary structure in terms of stratal packages and 2) estimate the internal clinoform heterogeneity associated with phases of known mid-Cenozoic sea-level change. Our statistical analysis of the estimated elastic properties, including P-wave velocity (Vp), density and clay volume, reveals repeating spatial patterns in the internal rock physics properties of the Miocene clinoforms. We show that diagenesis and sediment compaction within the dipping parts of clinoforms cause a continuous increase of Vp in the seaward direction, with a magnitude that decreases from top to bottom. Our results also suggest that lithofacies change in clinoforms imposes a stronger influence on density, as lateral changes in lithofacies are more pronounced in sediment density than in Vp. In the Miocene sedimentary record, the transgressive system tracts show a seaward coarsening trend in grain size and a 3%-5% increase in density from clinoform topsets to bottomsets. Highstand and lowstand system tracts show a fining trend basinward, with a ~8% and 5% reduction in density, respectively. We further demonstrate that the identified trends can provide a standard model allowing incorporation of clinoforms in reservoir characterization techniques, such as model-based seismic inversion, and enable setting of guidelines on how the petrophysical properties change regionally in shallow-marine siliciclastic environments of continental margins.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.493

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.067
GPT teacher head0.236
Teacher spread0.169 · 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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Citations0
Published2020
Admission routes1
Has abstractyes

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