Rock Typing — Keys to Understanding Productivity in Tight Gas Sands
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".