Geophysical Monitoring of Ground Surface Deformation Associated with a Confined Aquifer Storage and Recovery Operation
Bibliographic record
Abstract
One important issue in the storage of large volumes of fluids, mainly water and CO 2 , in the deep subsurface is to determine the resulting field-scale-induced displacements and consequences of overpressures on the mechanical integrity of the storage reservoir and surroundings. A quantifiable estimation of displacement can be made by combining the robust, cost-effective, and repeatable geophysical techniques of micro-gravimetry, differential global positioning system (DGPS), and differential synthetic aperture radar interferometry (DInSAR). These techniques were field tested and evaluated for the first time on an active large-volume aquifer storage and recovery (ASR) project in Pendleton, Oregon, USA, where three ASR wells are injecting up to 1.9 million m 3 year −1 into basalt aquifers to a depth of about 150 m. Injection and recovery of water at the wells are accompanied by significant gravity anomalies and vertical deformation of the ground surface localized to the immediate surroundings of the injection wells as evidenced by DGPS and gravity measurements collected in 2011. At a larger scale, and between 2011 and 2013, DInSAR monitoring of the Pendleton area shows sub-centimetric deformation in the western part of the city and close to the injection locations associated with ASR cycle. Deformations are found to be temporally out phased with the injection and recovery events due to complex groundwater flow. A numerical simulation of the effect of the water injection gives results in good agreement with the observations and confirms the validity of the approach, which could be deployed in similar geological contexts to look at the mechanical effects of water and gas injections.
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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) | 0.000 | 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 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".