Bibliographic record
Abstract
Since 2008, more than 10,000 horizontal wells have been drilled in Alberta using multistage hydraulic fracturing for tight/shale oil and gas development. Analysis of the existing wells’ performance and development of production enhancement techniques are of critical interest for the improvement of the development efficiency in these formations. Production modelling and analysis have been used in the oil and gas industry for a long time for the purpose of asset evaluation and project design. In this study, a new methodology for complex production profile analysis is first developed and applied to evaluate 344 Northern Montney wells located in Western Canada. A production decline curve model is developed and implemented via Bayesian statistical learning algorithm, and it is compatible with both single and dual flow regimes. This methodology is proven to be efficient and accurate on the production analysis and reserves estimation in the hydraulically fractured tight reservoirs. It can identify and model a dual-regime production profile with both linear and boundary dominated flow. Natural fractures and hydraulically induced fractures, either being propped or unpropped, are critical to the well after-stimulation productivity in the unconventional tight reservoirs. In this study, core samples are collected from the field and fractured in the lab. The rough fracture surfaces are scanned for surface roughness characterization, and then the fractures are reconstructed using numerical methods. Fluid flow in digitally reconstructed fractures are numerically simulated to study the relationship between the fracture conductivity with the fracture surface roughness and proppant properties. The fracture conductivity model is used in the later reservoir simulations for production and recovery enhancement. Next, an approach to improve early-time production by remediating permeability damage caused during stimulation in fracture and near fracture matrix is introduced. The fracturing fluid leak-off can damage the fracture conductivity and near-fracture matrix permeability, and consequently impair the well productivity after stimulation. In order to reduce this damage, this work applied data statistical analysis and numerical simulation approach to study the effects of various types of fracturing fluid on stimulated well productivity in the unconventional tight formation. Finally, an enhanced oil recovery (EOR) technique which combines the advantages of gas flooding and gas huff-n-puff is also brought up and tested. This study introduced and simulated a multi-well adaptive asynchronous CO2 Huff-and-Puff which combines the gas flooding and gas huff-n-puff for well groups which is more effective for the unconventional tight oil reservoirs.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".