A Chemical CO2 Sensor Monitoring CO2 Movement Under Reservoir Conditions
Bibliographic record
Abstract
Abstract A downhole CO2 sensor can continuously collect real-time data about CO2 movement and concentration changes at subsurface conditions. These data are very valuable for better understanding of subsurface uncertainties and quality-controlling theoretical studies such as reaction, transport, and mechanics in oil and gas formations. This paper describes the development of a downhole CO2 sensor tested under high pressure and reservoir conditions to monitor aqueous CO2 concentration change. The CO2 sensor developed is a Severinghaus-type sensor, which includes a metal-oxide electrode, a gas-permeable membrane, a porous steel cup, and a bicarbonate-based internal electrolyte solution. The CO2 sensor thus prepared 0.7 in. in diameter and 1.5 in long. A linear correlation was observed between a change in sensor output potential and dissolved CO2 in water under 1,000 psi pressure. CO2/brine coreflooding tests were performed to simulate the CO2 storage process and the sensor was deployed to monitor CO2 movement. The results indicated that the CO2 sensor could monitor CO2 movement in-situ in CO2 storage processes. Introduction Geologic sequestration of CO2 involves putting CO2 into long-term storage in geologic zones at subsurface conditions [1]. Such sites as deep saline aquifers and unmined coal seams onshore, and depleted oil or gas formations both onshore and offshore have been recommended for further serious consideration [2]. Thus far, in various regions of the world (Pacific Ocean, Gulf of Mexico, North Sea, Chinese East sea, and the Atlantic Ocean), a large part of research studies and pilot projects have looked at the feasibility of geological sequestration of CO2 [3]. The first commercial project occurred in Norway in 1996, in which CO2 was captured from natural gas streams and around 1 million tons of CO2 per year were into the Utsira formation [4] and to provide insight into CO2 migration [8]. All these pilot studies, located in Kansas, Virginia, West Virginia and Canada, are either under consideration or have been initiated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".