Bibliographic record
Abstract
Abstract The performance of water injection wells in deep, warm reservoirs is usually dominated by thermally enhanced fracturing of the formation. Modern pressure and rate monitoring data for these injection wells allows the fracturing performance of the wells to be observed on a continuous basis. A simple analytical model for thermal fracture growth has been developed, by coupling equations for fracture growth and fluid leak-off from the fracture. The empirical equation form allows the observed data to be analyzed to determine the timing and extent of fracture growth, and degree of fracture face plugging. As well, the equation form has lead to development of a fracturing index, which can be used as a surveillance tool to determine the state of fracture growth in an injection well. Once calibrated for a field or area, the model can be used to predict continued extension of fractures with time and injection. An example of analysis of an offshore injection well illustrates application of the model to real-time surveillance. The model has also been used to design new injection wells including the attached thermal fractures, aiding production engineering well equipment design, and reservoir simulation to evaluate well placement for optimum performance. The approach is new, pragmatic, and does not require coupled geo-mechanical and thermal simulation models. SPE Paper 20741 states: "A solution for fracture growth in terms of detailed fracture-volume balance (as is often applied to hydraulic stimulation) is not a natural or feasible approach." The method outlined in this paper shows that it is not only feasible, but leads to development of an empirical equation form which allows improved understanding of well performance and ability to forecast future behaviour.
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.
How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".