Effect of Natural Fracture Density on Production Variability of Individual Wells in the Tight Gas Nikanassin Formation
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 A statistical model with a strong correlation has been developed to determine average fracture density on the basis of production variability of 271 individual wells producing exclusively from the Nikanassin and equivalent formations in a very large area of more than 15,000 km2 in the Western Canada Sedimentary basin (WCSB), Alberta and British Columbia, Canada. Fractional production variability plots published by Nelson (2001) have been used successfully in the past in many naturally fractured reservoirs around the world. Up to now, the generation of such graphs has been based on empirical observations from field production data. The variability of the graphs is qualitatively interpreted as a measure of reservoir heterogeneities. This paper presents a sequential methodology to reproduce the empirical fractional variability plots of the Nikanassin tight gas formation using real data, an empirical variability distribution model (VDM) and dual-porosity numerical simulation. Different simulation approaches and multiple sensitivities were generated from the simplest to the most detailed cases to understand what causes the curvature of the fractional production variability plot (FPVP) in Nikanassin wells. The base simulation case is a homogeneous dual porosity model where porosity and permeability are set constant for matrix and fractures. A second case accounts for a heterogeneous dual porosity model generated through statistical distributions of porosity and permeability. Finally, Discrete Fracture Network (DFN) methodology is used to generate multiple fracture models from where stochastic fracture properties are generated for the simulation model. It is concluded that curvature of the variability plot is mainly affected by the occurrence of natural fracture density. This finding permits estimating fracture density approximately parallel to the northwest/southeast-trending thrust belt of the Canadian Rocky Mountains in both the west and east side of the deformation wedge. An unexpected result is that for a significant change in fracture permeability and porosity with constant fracture density, the fractional production variability curve is not affected significantly in the case of gas reservoirs. Although the method is applied specifically to the Nikanassin tight gas formation, the theory is developed in detail in such a way that the methodology can be applied in other tight gas reservoirs around the world. Findings from this work are in good agreement with the geological description of the Nikanassin formation and with a previous production analysis performed in the six study areas based on the cumulative number of wells versus yearly cumulative gas production.
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