Hydraulic Dilation Stimulation to Improve Steam Injectivity and Conformance in Thermal Heavy Oil Production
Bibliographic record
Abstract
Summary In thermal heavy-oil production, steam is injected to reduce oil viscosity and promote the less viscous oil flowing to the production wells. Steam injectivity and its conformance in the reservoir greatly impacts oil production and project economics. It is found that hydraulic dilation stimulation of heavy-oil reservoirs before steam injection can create a large and targeted stimulated reservoir volume for the steam to contact the heavy-oil phase. As a result, steam injectivity increases and steam conformance improves. These eventually translate to increased oil production and reduced steam/oil ratio, which has been proven in hundreds of wells worldwide. This paper describes relevant fundamental mechanisms and field performance. As a major novelty, the hydraulic stimulation avoids fracturing the reservoir, but seeks to cause dilation. If the reservoir is fractured, a linear conduit is created. Steam can easily break through to neighbouring wells and the steam conformance is poor. When dilation takes place, however, additional pore space is created in the rock matrix. This results in truly volumetric stimulation, which is helpful to increase the steam injectivity while ideal thermal conformance is also achieved. This paper illustrates these theoretical bases and their resultant positive field performance in assisting thermal heavy-oil production.
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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 | 0.000 |
| 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) | 0.002 | 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".