Shale Barrier Failure Strategies in Heterogeneous SAGD Reservoirs: A Case Study
Bibliographic record
Abstract
Abstract In Xinjiang, China, a huge volume of heavy oil deposits is not commercially developed yet due to the high in-situ oil viscosity that is higher than 100,000 to 1,000,000+ cp and the strong reservoir heterogeneity. Two SAGD pilot tests with 11 wellpairs in total have been carried out since 2008 while the extensively distribution of shale barriers result in the poor SAGD steam chamber conformance, low production rate and high SOR. This paper presents the workflow to break shale barriers to improve the steam chamber development along horizontal section, to reduce the SOR and to enhance the economic performance. The first step of the workflow is to select representative core and shale barrier samples and quantify the geomechanic data by high-pressure-high temperature dilation and shale barrier failure experiments. Meanwhile, the geologic model is built taking into account geological uncertainties of the reservoir and the core analysis results. And then, a small set of SAGD wellpair models are selected as the typical wellpair models by classifying different patterns of shale barriers distribution along horizontal section. Finally, the upscaled thermal reservoir models with geomechanics models using the data acquired by geomechanic experiments are investigated to model and optimize operation strategies for dynamic fracturing and shale barrier failure during SAGD phase. The research reveals that the high-pressure CSS is an effective method to break the shale barrier distributed between the horizontal segments of the producer and the injector, which has been verified by field experience and the numerical simulation. It is forecasted that the case without HP-CSS will have the steam chamber growth at 50% percent of horizontal segment, which is 40% less than the case CSS. The ultimate oil recovery factor is 34.54%, which is 16.31% less than the case with HP-CSS; and the cumulative oil/steam ratio is 0.175,, which is 14.5% less than the case with HP-CSS, encouraging SAGD performance after HP-CSS shows a better economic performance, which is worthwhile to carry out and also has a significant guidance for the similar SAGD reservoirs to improve the performance.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".