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Record W2974275618 · doi:10.2118/196118-ms

Non-Isothermal Injectivity Considerations for Effective Geological Storage of CO2 at the Aquistore Site, Saskatchewan, Canada

2019· article· en· W2974275618 on OpenAlexaffabout
Alireza Rangriz Shokri, Rick Chalaturnyk, Erik Nickel

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsPetroleum Technology Research CentreUniversity of Alberta
Fundersnot available
KeywordsAquiferIsothermal processInjection wellPetroleum engineeringGeologyWellheadPlumeEnvironmental sciencePetrologyGeotechnical engineeringGroundwaterMeteorologyThermodynamics

Abstract

fetched live from OpenAlex

Abstract Monitoring data of episodic transient heat and flow conditions, caused by intermittent cold CO2 injection in Aquistore, has shown a linkage between injectivity index and downhole injection temperature. Taking leverage access to invaluable field performance data collected from this highly instrumented Canadian CCS demonstration project, the focus of this paper is to understand and quantify the potential non-isothermal mechanisms involved in cold CO2 injection. Understanding this phenomenon is important as it has serious implications on containment, conformance, and injectivity technologies for effective geological CO2 storage. To account for transient heat and fluid transport during cold CO2 injection in Aquistore, a non-isothermal EOS-based fluid flow simulation, of a high-resolution detailed geological model built based on an extensive characterization program, was calibrated with periodic monitoring data of downhole pressure, temperature, and injected mass rate. Due to the possibility of non-isothermal effects on near-wellbore stress fields, local induced fractures, and permeability alterations, in addition to dynamics of CO2-brine interactions, coupled reservoir geomechanical modeling techniques were then employed for further calibration. The uncertainties associated with the subsurface geological modeling, leaking aquifer boundaries, reservoir heterogeneity, rock thermal, petrophysical, and geomechanical properties were considered for both isothermal and non-isothermal conditions. Processing of DTS (Distributed Temperature Sensing) data from both injection and observation wells indicated dynamic perturbations in subsurface temperature due to injection operations. Geological characterization, performed through high-resolution 3D seismic images, core, and log data, and the existence of a leaking aquifer, were found to have significant impacts on CO2 plume evolution. Through history matching process of non-isothermal flow simulation, for both injector and observation wells, the extent of the cold region was estimated, and found to be mainly controlled by rock thermal properties, permeability, and injection rate. Our analysis suggested that cold temperature front was limited to near-wellbore region due to substantial heat loss by conduction, besides radial decay of convective flow. Further non-isothermal coupled simulations indicated a large, but near-wellbore-limited reduction in effective horizontal stresses, induced by cold CO2 injection. Employing different values of thermal expansion coefficients, local potential open-mode fractures were observed; however, fracturing of entire formation was not experienced. This phenomenon was associated with local permeability enhancement, and potential improvement in CO2 injectivity. A comparison of isothermal and non-isothermal analyses on reservoir performance during CO2 injection was lastly provided. Our analysis of subsurface injection and coupled processes in relation to geologic CO2 sequestration delivers critical insights on how and under what conditions these non-isothermal effects are generated. This ultimately provides a predictive tool to better characterize the reservoir behaviour, injectivity issues, and spatial location of a subsurface CO2 plume.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.762
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.010
GPT teacher head0.236
Teacher spread0.226 · 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 teacher head, not a consensus.

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".

Quick stats

Citations21
Published2019
Admission routes2
Has abstractyes

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