Restimulation of Unconventional Reservoirs: When are Refracs Beneficial?
Bibliographic record
Abstract
Abstract Restimulation treatments have been attempted in a wide variety of reservoirs, and more than 140 papers have been published documenting the success and failure of these efforts. A database of these published examples has been compiled and analyzed to identify the mechanisms responsible for production improvement following restimulation with propped fractures (refracs). A portion of this database was recently published (Vincent 2010), and specific field examples were highlighted to demonstrate that refrac treatments can improve production by numerous mechanisms including: – Enlarged fracture geometry – Improved pay coverage through increased fracture height in vertical wells – Greater lateral coverage in horizontal wells or initiation of more transverse fractures – Increased fracture conductivity compared to initial frac – Restoration of fracture conductivity loss due to embedment, cyclic stress, proppant degradation, gel damage, scale, asphaltene precipitation, fines plugging, etc. – Increased conductivity in previously unpropped or inadequately propped portions of fracture – Use of more suitable fracturing fluids – Reorientation due to stress field alterations, leading to contact of "new" rock This paper will briefly review restimulation attempts in six Canadian reservoirs of interest to the local audience, and will then present a more detailed review of restimulation of horizontal wells in the unconventional Bakken oil formation. In addition to a summary of published results, this paper will introduce a significant amount of previously unpublished data regarding refrac treatments of horizontal laterals completed in the Middle Bakken. This study will identify several additional concerns and opportunities with restimulating horizontal wellbores that were not previously identified in literature reviews. This organized summary of field results and references will provide significant value to readers evaluating or designing restimulation treatments.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: yes | Other design | high |
| opus | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".