MétaCan
Menu
Back to cohort
Record W2152678570 · doi:10.2118/114164-ms

Rock Typing — Keys to Understanding Productivity in Tight Gas Sands

2008· article· en· W2152678570 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsApache (Canada)
Fundersnot available
KeywordsGeologySedimentary depositional environmentDiagenesisPermeability (electromagnetism)Sedimentary rockTight gasPetrologyTexture (cosmology)MineralogyGeotechnical engineeringHydraulic fracturingGeochemistryGeomorphologyStructural basinArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

Abstract This paper presents a work-flow process to describe and characterize tight gas sands. The ultimate objective of this work-flow is to provide a consistent methodology to systematically integrate both large-scale geologic elements and small-scale rock petrology with the physical rock properties for low-permeability sandstone reservoirs. To that end, our work-flow integrates multiple data evaluation techniques and multiple data scales using a core-based rock typing approach that is designed to capture rock properties characteristic of tight gas sands. Fundamental to this process model are identification and comparison of three different rock types — depositional, petro-graphie, and hydraulic. These rock types are defined as: Depositional — These are rock types that are derived from eore-based descriptions of genetic units which are defined as collections of rocks grouped according to similarities in composition, texture, sedimentary structure, and stratigraphic sequence as influenced by the depositional environment. These rock types represent original large-scale rock properties present at deposition. Petrographie — These are rock types which are also described within the context of the geological framework, but the rock type criteria are based on pore-scale, microscopic imaging of the current pore structure — as well as the rock texture and composition, clay mineralogy, and diagenesis. Hydraulic — These are rock types that are also defined at the pore scale, but in this case we define "hydraulic" rock types as those that quantify the physical flow and storage properties of the rock relative to the native fluid(s) — as controlled by the dimensions, geometry, and distribution of the current pore and pore throat structure. Each rock type represents different physical and chemical processes affecting rock properties during the depositional and paragenetic cycles. Since most tight gas sands have been subjected to post-depositional diagenesis, a comparison of all three rock types will allow us to assess the impact of diagenesis on rock properties. If diagenesis is minor, the depositional environment (and depositional rock types) as well as the expected rock properties derived from those depositional conditions will be good predictors of rock quality. However, if the reservoir rock has been subjected to significant diagenesis, the original rock properties present at deposition will be quite different than the current properties. More specifically, use of the depositional environment and the associated rock types (in isolation) to guide field development activities may result in ineffective exploitation.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.368
Threshold uncertainty score0.327

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.042
GPT teacher head0.230
Teacher spread0.188 · 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