Steam Chamber Development In Diatomites: The Role of Microseismic Monitoring in identifying Conformance and Out-of-Zone Fracture Growth
Bibliographic record
Abstract
Abstract The treatment of diatomite reservoirs with cyclic steam is intended to enhance the mobility of the oil in reservoir, thereby stimulating hydrocarbon production. With optimized array geometries, the advanced analysis of the microseismicity resulting from these treatments can yield much more information on these events, such as their failure mechanisms through the process of moment tensor inversion. When these mechanisms suggest that the strain induced by the events is occurring on natural or newly created fractures, their orientations may be gleaned from the moment tensor. In this study, we monitor microseismic events observed during the injection and production stages of a ‘Huff and Puff’ steaming operation in a diatomite. Our focus is on analyzing the growth of two event clusters that occurred during production cycles after steaming. Since the events are located using three downhole, we can invert for the components of failure. One cluster showed significant vertical growth and the mechanisms of the microseisms were consistent with dilatational opening of fractures, closures of the fractures, or double-couple (shear) events. While the trend of the cluster of events is reflected in the fracture orientations of some of the events, many of the events have fractures occurring at defined angles to the overall trend. This observation allowed us to infer that the stress regime under which most of the events were responding is dynamically changing as the strain induced by surrounding events evolves in the reservoir. The second grouping of events was spatially contained and consisted of inflationary or deflationary failure mechanisms, suggesting containment that reflects steam chamber development; further exemplified by the variability in fracture azimuths, suggesting that the local stress conditions played a more significant role in the observed failures. Based on this study, we suggest that advanced microseismic analysis has the potential to outline when steam chamber development occurs within design specifications and when the regional/local stress conditions influence steam chamber development, such as observed in the example of poor containment. In both examples, the occurrence of events during production suggests that failures occur as a result primarily as a result of stress unloading and stress re-distribution locally.
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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".