In-Situ Catalytic Aquathermolysis Combined with Geomechanical Dilation to Enhance Thermal Heavy-Oil Production
Bibliographic record
Abstract
Summary Steam-assisted gravity drainage (SAGD) has been used to develop the "super heavy" oil reservoirs in PetroChina Xinjiang Oilfield Branch. These reservoirs have a very high oil viscosity that can reach more than 50,000 cp at 50°C. Moreover, owing to their continental deposit origin, these reservoirs have a low porosity and a low permeability, as well as frequent and heterogeneous occurrence of mud/shale stringers within. Because of these challenging reservoir qualities, the conventional steam circulation SAGD startup process takes 10 to 12 months before the SAGD well pair can be switched to production. A geomechanical dilation mechanism is used to startup the SAGD production with outstanding success. As a result, dilation startup has recently become the routine start-up process in Xinjiang's SAGD production. This paper describes further improvement in dilation startup by injecting a unique catalyst to evoke the in-situ catalytic aquathermolysis mechanism. The reservoir is first dilated to form a high-porosity and high-permeability conduit connecting the SAGD well pair. The catalyst is then injected into these newly created pore spaces, contacting the heavy oil in large volume, and helping reduce in-situ oil viscosity. This technology has been applied on more than 10 SAGD well pairs and excellent field results were generated in terms of reduced steam use, shortened steam circulation time, and increased initial oil production. This paper presents this integrated chemical geomechanics technology with relevant laboratory test and field results supporting the description.
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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) | 0.001 | 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".