A review and discussion on laboratory investigations involving supercritical CO2 for storage
Bibliographic record
Abstract
The race is on towards net zero carbon emissions, and upstream research and service laboratories are quickly shifting from enhanced oil recovery laboratory analyses to CO2 injection for storage. For the storage purpose, CO2 is usually injected into geological formations (i.e., saline aquifers and depleted oil and gas reservoirs) as a dense supercritical fluid (Temperature > 31°C, Pressure > 7.38 MPa). The goal of this paper is to review some supercritical concepts and theory regarding CO2 injection and storage, and to highlight laboratory and instrumentation considerations when working with supercritical CO2. Once CO2 dissolves into brine, carbonic acid forms which can interact with various minerals of the host rock, resulting in porosity and permeability changes. Therefore, geological CO2 storage requires understanding of multiphase flow behaviour in porous media to evaluate CO2 injectivity and transport, CO2 residual trapping, and the risk of CO2 leakage. The geochemical reactions occurring during CO2 injection can alter rock pore structure, which further impacts capillary pressure and wetting and non-wetting phase relative permeabilities. This paper will review and discuss pertinent phase behaviour, mass transfer, fluid-fluid and fluid-rock interactions associated with CO2 injection into saline aquifers and waterflooded depleted oil formations as principal targets for geological carbon storage. Depending on mineral composition, temperature, pressure, flow regime, brine composition, multiphase flow of CO2 and water, and initial pore structure, some minerals may dissolve due to the formation of carbonic acid and pH reduction. We highlight challenges in working with supercritical CO2, with liquid-like density and gas-like viscosity, compared to CO2 gas such as measuring pH at in-situ conditions. Stability of clay and carbonate minerals in deep saline formations is strongly affected by pH changes in this region. Usually, pH of the brine samples taken from coreflooding setups during the course of CO2 injection is measured at ambient conditions. However, once brine samples are brought to low-pressure conditions, CO2 is released leading to an increase in pH, which is not representative of the high-pressure high-temperature in-situ conditions. Knowing that pH is important to understand chemistry of the subsurface fluids in the context of geological carbon storage, we review laboratory practices and suggest analytical methods. Considering various factors of rock mineralogy, sub-core heterogeneity, and wettability is crucial for optimizing CO2 storage and ensuring the long-term success of geological carbon sequestration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".