Imaging the Aquistore reservoir after 36 kilotonnes of CO<sub>2</sub> injection using distributed acoustic sensing
Bibliographic record
Abstract
Aquistore is a geologic [Formula: see text] storage project that is using a deep saline formation as a storage reservoir. From April 2015 to February 2016, approximately 36 kilotonnes of [Formula: see text] were injected into the reservoir at a depth of 3130–3350 m. We have developed an analysis of distributed acoustic sensing (DAS) 3D vertical seismic profiling data acquired in February 2016, marking the first seismic survey since injection began. The VSP data were processed in parallel with baseline preinjection data from a November 2013 survey, with the objective of detecting and characterizing the subsurface [Formula: see text] plume and evaluating the repeatability of DAS in a reservoir monitoring project. A single processing sequence was devised that (1) accurately imaged the reservoir for the baseline and monitor data and (2) attained adequate repeatability to observe time-lapse differences related to the presence of [Formula: see text]. Repeatability was somewhat compromised by the less advanced noise cancellation methodology of the DAS system used for the baseline survey. In the final cross-equalized migrated data volumes, normalized root-mean-square ([Formula: see text]rms) difference values of [Formula: see text] were attained at the reservoir level indicating good repeatability compared with most surface seismic studies. An injection-related amplitude anomaly with maximum [Formula: see text]rms values of approximately 0.7 is apparent in the Deadwood Formation of the reservoir, whereas no significant [Formula: see text]rms anomalies were observed near the injection and monitoring wells in the Black Island Member or above the reservoir.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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) | 0.000 | 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 teacher head, 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".