Hydraulic Fracturing of Naturally Fractured Tight Gas Formations
Bibliographic record
Abstract
Abstract A workflow is presented for modeling actual hydraulic fracturing jobs in tight gas formations of the Western Canada Sedimentary Basin (WCSB). Matching of hydraulic fracture length and resulting gas production from past jobs provides a learning curve that allows design optimization of future fracturing jobs. Key parameters needed for modeling the design of hydraulic fracturing jobs in these types of formations are discussed and include properties such as porosity (matrix, natural fracture and isolated or non-connected), permeability (matrix and natural fracture), water saturation (matrix and natural fracture), shear velocities (even when direct measurements are not available), Poissons ratio, shear modulus, Young modulus, Biot constant, overburden, pore pressure, and net stress. The critical evaluation of these properties provides a valid representation of the tight gas formation and what the natural gas production outcome from the hydraulic fracturing job might be. Important recommendations to achieve a correct integration of logs, well testing, production decline analysis, and 3D modeling of the hydraulic fracturing job are also presented; as wells as an understanding of the differences that can arise from each source of information. The paper incorporates petrophysical and new linear-dominated well testing and production decline analysis methods using triple porosity models for matching petrographic work and production decline; and for quantifying rock properties such as natural fractures and slot porosity, intergranular porosity and isolated non-effective porosity. It is concluded that even though each tight gas reservoir is unique and as such, should be considered as a research project by itself, the workflow presented in this study could prove to be of value in other regions of the world where tight gas formations are present.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".