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Quantitative CO2 monitoring at the CaMI Field Research Station (CaMI.FRS), Canada, using a hybrid structural-petrophysical joint inversion

2020· article· en· W3172537280 on OpenAlexaffabout
Dennis Rippe, Michael C. Jordan, Marie Macquet, Don C. Lawton, Anouar Romdhane, Peder Eliasson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsCMC Research InstitutesUniversity of Calgary
Fundersnot available
KeywordsChemistryAmperePhysics

Abstract

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A key requirement by the European CCS directive for the safe operation of geological CO2 storage is the operator's responsibility to demonstrate containment of the injected CO2 and conformance between its actual and modelled behavior. Understanding the subsurface behavior and long-term fate of the injected CO2 requires the quantification of key reservoir parameters (e.g. pore pressure, CO2 saturation and strain in the overburden). Reliable quantification of these parameters and distinction between them pose a challenge for conventional monitoring techniques, which could be overcome by combining advanced multi-disciplinary and multi-method monitoring techniques in a joint inversion. Within the aCQurate project, we aim to develop a new technology for accurate CO2 monitoring using Quantitative joint inversion for large-scale on-shore and off-shore storage applications. In previous applications of joint inversion to CO2 monitoring, we successfully combined the strengths and advantages of different geophysical monitoring techniques (i.e. seismics with its high spatial resolution and geoelectrics with its high sensitivity to changes in CO2 saturation), using a cross-gradient approach to achieve structural similarity between the different models. While this structural joint inversion provides a robust link between models of different geophysical monitoring techniques, it lacks a quantitative calibration of the model parameters based on valid rock-physics models. This limitation is addressed by extending the previously developed structural joint inversion method into a hybrid structural-petrophysical joint inversion, which allows integration of cross-property relations, e.g. derived from well logs. The hybrid structural-petrophysical joint inversion integrates relevant geophysical monitoring techniques in a modular way, including seismic, electric and potential field methods (FWI, CSEM, ERT, MMR and gravity). It is implemented using a Bayes formulation, which allows proper weighting of the different models and data sets, as well as the relevant structural and petrophysical joint inversion constraints during the joint inversion. The hybrid joint inversion is designed for on-shore and off-shore CO2 storage applications and will be demonstrated using synthetic data from the CaMI Field Research Station (CaMI.FRS) in Canada. CaMI.FRS is operated by the Containment and Monitoring Institute (CaMI) of CMC Research Institutes, Inc., and provides an ideal platform for the development and deployment of advanced CO2 monitoring technologies. CO2 injection occurs at 300 m depth into the Basal Belly River sandstone formation, which is monitored using a large variety of geophysical and geochemical monitoring techniques. In preparation for the application to real monitoring data, we present the application of the joint inversion to synthetic full waveform inversion (FWI) and electrical resistivity tomography (ERT) data, derived for a geostatic model with dynamic fluid flow simulations. In addition to obtaining a better understanding of the subsurface behavior of the injected CO2 at CaMI.FRS, our goal is to mature the joint inversion technology further towards large-scale CO2 storage applications, e.g. on the Norwegian Continental Shelf. Acknowledgements Funding is provided by the Norwegian CLIMIT program (project number 616067), Equinor ASA, CMC Research Institutes, Inc., University of Calgary, Lawrence Berkeley National Laboratory (LBNL), Institut national de la recherche scientifique (INRS), Quad Geometrics Norway AS and GFZ German Research Centre For Geosciences (GFZ).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score0.320

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.130
GPT teacher head0.353
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2020
Admission routes2
Has abstractyes

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