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Record W2094544862 · doi:10.2118/2007-033

Visual Investigations on the Oil Recovery and Sequestration Potential of CO2 in Naturally Fractured Oil Reservoirs

2007· article· en· W2094544862 on OpenAlexafffund
Vahapcan Er, Tayfun Babadagli

Bibliographic record

VenueCanadian International Petroleum Conference · 2007
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPetroleum engineeringEnhanced oil recoveryEnvironmental scienceCarbon sequestrationGeologyChemistryCarbon dioxide

Abstract

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Abstract CO2 sequestration into geologic formations such as oil reservoirs, coal beds and aquifers is a possible way to reduce the emissions of this anthropogenic gas into the atmosphere. Among these, sequestration into oil reservoirs while enhancing oil recovery is one of the most feasible ways as the additional oil recovery would offset the cost of CO2 sequestration operation. We postulate that the matrix, the main source of oil, could be a good CO2 storage medium. Hence, we focus on the matrix-fracture interaction during CO2 injection into naturally fracture oil reservoirs (the Weyburn and Midale fields are good examples for this case) in this paper. Proper design of this process is essential to maximize both the amount of CO2 sequestered and oil recovered. In this cooptimization process, miscibility, oil viscosity, matrix properties (permeability, porosity, pore characteristics, wettability, etc.), fracture properties (permeability, orientation, connectivity), injection rate, gravity, and the physical state of CO2 play a critical role. Clear understanding of the contributions of these properties on the dynamics of matrixfracture interaction is essential in designing EOR and CO2 sequestration application. In this paper, the dynamics of CO2 injection was studied experimentally. 2-D glass-bead models with a fracture in the middle were prepared and pentane was used as solvent to displace the kerosene or mineral oil to mimic miscible CO2 displacement. The focus was on the displacement patterns and solvent breakthrough controlled by matrix fracture interaction and pore scale behaviour of solvent-oil interaction for different matrix (wettability), fracture and injection conditions (rate, vertical vs. horizontal injection) as well as oil viscosity. Besides the visual investigation, the produced fluid was analyzed to calculate the solvent cut and oil recovery. It is believed that the visual understanding of the process will provide substantial information for further modelling studies. Introduction Miscible Displacement Mechanisms controlling the miscible displacement and factors effective on the efficiency have been focus of many studies over the last four decades. Huang and Tracht (1) studied oil recovery mechanisms during CO2 injection and reported that the dominant controlling mechanisms are CO2 swelling and the CO2 extraction of oil. Bahralolom and Orr(2) supported that through their micro model visualization study. They also suggested that the extraction is more effective than solubility. In general, the most common mechanisms controlling the oil recovery by CO2 injection are (1) oil displacement by the generation of miscibility, (2) oil swelling and (3) reduction in oil viscosity(3). Presence of water can decrease the efficiency of miscible CO2 displacements in water wet systems as the higher saturation of wetting phase decreases the flow fraction of nonwetting phase and consequently decreases the recovery of oil in water wet systems(4). Interaction of phase behaviour with heterogeneities leads to residual oil saturations due to preferential flow paths(5). In case of naturally fractured reservoirs, other parameters such as matrix and fracture properties critically influence the efficiency of displacement. Thompson and Mungan's(6) study eveals the effect of displacement velocity on recovery efficiency. Later, Firoozabadi and Markeset(7) showed that matrix-fracture configurations and fracture ap

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.297
Threshold uncertainty score0.999

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)0.0000.000

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 head0.011
GPT teacher head0.235
Teacher spread0.224 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations4
Published2007
Admission routes2
Has abstractyes

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