Prediction of Heavy Oil Production Based on Geomechanical Analysis in Entire Lifecycle of SAGD
Bibliographic record
Abstract
ABSTRACT Steam‐assisted gravity drainage (SAGD) technology is an essential means of efficient development of heavy oil, super heavy oil, oil sands, and other unconventional resources in the world. Accurate prediction and evaluation of heavy oil output during SAGD production is a key step of construction optimization design and economic evaluation. The traditional prediction model of heavy oil production does not fully consider many geomechanical factors, such as rock deformation and permeability dynamic evolution. In this paper, a new mathematical model of crude oil production‐geomechanical coupling was established for three stages of the SAGD life cycle (steam chamber breakthrough stage, rising stage, and lateral dilation stage). The influence of the dynamic evolution of rock porosity and permeability on production was fully considered through the sensitivity coefficient of rock strain and permeability stress. It is found that the gap between the new model and the traditional model is larger when the strain and stress sensitivity of the reservoir body is larger. The value calculated by the conventional model is small when the reservoir dilates and large when the reservoir compresses. For Karamay heavy oil in Xinjiang, China, the steam breakout time predicted by the new model is 0.72, 1.50, 1.37, and 1.44 times the conventional model when the volumetric strain is 6%. The heavy oil production of Karamay, Xinjiang, China, Athabasca, and Cold Lake SAGD production areas in Canada was predicted. In the lateral dilation stage of the steam chamber, the predicted values of the model considering geomechanical factors were 1.44, 1.28, and 1.15 times the traditional model, respectively. This model can help field engineers obtain more accurate production of heavy oil and evaluate the significance of reservoir geomechanics in SAGD production.
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 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 | 0.001 | 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) | 0.001 | 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".