Beyond Decline Curves: Life-Cycle Reserves Appraisal Using an Integrated Work-Flow Process for Tight Gas Sands
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.
Bibliographic record
Abstract
Abstract Decline curve analysis is often either the only or the primary tool used for reserve evaluations in tight gas sands. However, the flow and storage properties characteristic of low-permeability sands often preclude accurate assessments using only or primarily decline curve analysis, especially early in the productive life. The most accurate reserve estimates incorporate multiple data sources and the appropriate evaluation techniques. Therefore, this paper presents a reserves appraisal work-flow process that complements traditional decline curve analyses with comprehensive and systematic data acquisition and evaluation programs that integrate both static and dynamic data. Our approach—which has been developed specifically to incorporate the production characteristics of tight gas sands— is an adaptive process that allows continuous but reasonable reserve adjustments over the entire field development and production life cycle. Implementing this process will prevent unrealistic (either too low or high) reserve bookings. Although it is applicable during any field development phase, our work-flow process is most beneficial during early stages before true boundary-dominated flow conditions have been reached and when reserve evaluation errors are most likely.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it