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Record W2254777629 · doi:10.2110/pec.07.52.0131

Applications of Ichnology to Fluid and Gas Production in Hydrocarbon Reservoirs

2007· book-chapter· en· W2254777629 on OpenAlexaff
Murray K. Gingras, S. George Pemberton, Floyd Henk, James A. MacEachern, C. A. Mendoza, Ben Rostron, Riley O’Hare, Michele Spila, Kurt O. Konhauser

Bibliographic record

VenueSEPM (Society for Sedimentary Geology) eBooks · 2007
Typebook-chapter
Languageen
FieldEarth and Planetary Sciences
TopicGeological formations and processes
Canadian institutionsSimon Fraser UniversityUniversity of Alberta
Fundersnot available
KeywordsIchnologyGeologyPetrophysicsDiagenesisSedimentary rockPetroleum reservoirPaleontologyPetrologyWirelineFaciesClassification of discontinuitiesPetroleum engineeringTrace fossilGeotechnical engineering

Abstract

fetched live from OpenAlex

Abstract Biogenically modified sedimentary flow media can occur as well-defined, highly contrasting permeability fields (i.e., dual-permeability networks), or slightly contrasting permeability fields (i.e., dual-porosity networks). Dual porosity reduces the resource quality of a sedimentary rock, in that although the entire rock contributes to fluid flow, the presence of more than one fluid phase can induce preferential flow along tortuous permeability pathways. Additionally, fluid moves via diffusion and advection, making the pathways difficult to model. Dual-permeability flow media have even poorer resource characteristics because the higher permeability portions of the rock provide the only transmissive conduits, and fluid resources may be absent in the tighter (unburrowed) rock. Secondary recovery attempts in dual permeability media can isolate large parts of the active flow network, which may contain resource fluids or gasses. The presence of a dual porosity versus a dual permeability network, and the stratigraphic configuration of burrow-enhanced permeability are the primary considerations when classifying the type of biogenic flow media encountered. These parameters define the five flow-media types: 1) surface-constrained textural heterogeneities; 2) non-constrained, discretely packaged textural heterogeneities; 3) selectively sorted, weakly defined textural heterogeneities; 4) cryptically bioturbated sandstone; and 5) diagenetic heterogeneities. Other factors that influence the quality and behavior of the flow media are burrow density, burrow connectivity and burrow/matrix permeability contrast, burrow surface area, and burrow architecture. With respect to permeability fabrics, 3-D imaging techniques are an essential component of burrow-fabric analysis. Computer Tomography (CT) scans, Micro-CTscans, and MRI techniques have the most potential in burrow-reservoir analysis. These techniques can be used collaboratively to fully assess the nature of burrow-modified flow media.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.017
GPT teacher head0.231
Teacher spread0.214 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations32
Published2007
Admission routes1
Has abstractyes

Explore more

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