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Record W2026392031 · doi:10.2118/2009-176

Parameter Evaluation of CO2 Sequestration Capacity in Depleted Oil Reservoirs - Coreflood Tests and Numerical Simulation

2009· article· en· W2026392031 on OpenAlexafffundabout
S. Wang, Mingzhe Dong, Zhong Li

Bibliographic record

VenueCanadian International Petroleum Conference · 2009
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of ReginaUniversity of Calgary
FundersNatural Resources CanadaPetroleum Technology Research Centre
KeywordsPetroleum engineeringEnvironmental scienceEnhanced oil recoveryEngineering

Abstract

fetched live from OpenAlex

Abstract CO2 sequestration in depleted oil reservoirs provides an appealing option for reducing CO2 emissions in the atmosphere. However, the available space in most depleted oil reservoirs for storing CO2 is quite limited because a large portion of the reservoir is occupied by the remaining water and residual oil. It is important to answer the question how to remove the remaining water and residual oil, thus maximize the capacity of CO2 storage in depleted oil reservoirs. In this paper, a comprehensive and systematic approach was taken to study the parameters affecting CO2 sequestration capacity in depleted oil reservoirs. Two groups of laboratory CO2 sequestration experiments were conducted at 59 °C and 5,500 kPa to study the effect of gravity on the CO2 sequestration process and provide history matching data for core-scale simulation. Orthogonal experiment analysis based on corescale simulation indicates that the three most important parameters for CO2 storage capacity are fluid flow direction, capillary pressure and reservoir pressure. Numerous simulation runs were conducted to investigate the detailed influence of fluid flow direction, capillary pressure, reservoir pressure, temperature, production rate, and injection timing. Simulation results indicate that CO2 should flow vertically and the roles of capillary pressure are different in cases of different fluid flow directions. Introduction CO2 sequestration in depleted oil reservoirs is among the most appealing options for reducing CO2 concentration in the atmosphere. Many reservoirs in Canada are potential candidates for CO2 sequestration[1–3]. However, the available space in most depleted oil reservoirs for storing CO2 is limited because the major portion of the reservoir is occupied by the water either injected during the recovery processes or invaded as a result of reservoir pressure decrease. The CO2 storage capacity in water as dissolved gas is much less than that of CO2 as supercritical gas. Meanwhile, the residual oil saturation after most tertiary recovery methods is still as high as 30%[4]. The remaining water and residual oil restrict the CO2 storage capacity in the depleted oil reservoirs. The objective of this paper was to evaluate the parameters affecting the storage capacity of CO2 from core scale experiments and simulations, thus to seek for methods to efficiently displace and produce water and oil retained in reservoirs after an enhanced oil recovery process. Many previous studies about CO2 injection are related to enhancing oil recovery rather than improving CO2 storage capacity[5–8]. Orr et al.[5] had a detailed discussion about mechanism of CO2 flooding for enhanced oil recovery, including swelling effect, viscosity reduction effect, and multiple-contact-miscible mechanism. The aim of those studies is to enhance oil recovery with the use of as little amount of CO2 as possible. However, CO2 sequestration is to sequestrate as much CO2 as possible safely in underground formations in a geological time frame. This process involves the balance of gravity force, viscous force and capillary pressure[9,10]. The methodology developed in this paper is important for both CO2 storage site selection and CO2 storage injection strategy in a candidate depleted oil reservoir.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.984

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 head0.055
GPT teacher head0.286
Teacher spread0.231 · 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 designSimulation or modeling
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
Published2009
Admission routes3
Has abstractyes

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