Applications of Ichnology to Fluid and Gas Production in Hydrocarbon Reservoirs
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".