Passive Seismic Data Management and Processing to Monitor Heavy Oil Steaming Operations
Bibliographic record
Abstract
Abstract Cyclic Steam Stimulation (CSS) is a cost-effective means to produce heavy oil at the Cold Lake field in Alberta, Canada. The high viscosity of bitumen is the main obstacle to economic production, but the bitumen viscosity decreases significantly with temperature. Steam is injected at fracturing conditions, resulting in complex interactions of reservoir expansion (dilation) and contraction (recompaction) that propagate stress and strain fields in the overburden. The mechanical loads on wells resulting from this production process are an important design consideration. To enhance operational integrity, a dedicated passive seismic monitoring well is installed on new development pads to provide early detection of casing failures and possible fracturing of the formation overburden. There is now an installed base of almost 90 such acoustic monitoring wells in the operator's field. With data acquisition of 15 to 30 geophones per system, recording continuously at 2000 or 3000 samples per second, the data management issues for this monitoring network are challenging. Several classes of acoustic events have been identified, including those due to casing failure, formation heave, near-wellbore cement cracking, and production rod pump background noise, in addition to "Continuous Microseismic Radiation" (CMR) that resembles harmonic tremors. Most casing failures are detected by observation of singular events. The detection of fracturing of the overburden, which may include the presence of bitumen and/or produced water that has migrated out of zone, is a more complex process that requires distinguishing shear events and CMR events from normal formation heave and other environmental noise. The operator has stewarded the development of a cost-effective system that includes local pad data acquisition, uploading of selected data to a server with data archiving facilities, and downloading data to dedicated analysts. This paper will present a summary of the data management and processing technologies developed to address the challenge of managing this data-intensive problem. Fig. 1 CSS is a three-step process consisting of injection at fracturing conditions, soak, and 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".