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Record W2931236503 · doi:10.2118/193818-ms

A Compositional Model for Gas Injection IOR/EOR in Tight Oil Reservoirs Under Coupled Nanopore Confinement and Geomechanics Effects

2019· article· en· W2931236503 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueSPE Reservoir Simulation Conference · 2019
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsnot available
FundersSINOPEC Petroleum Exploration and Production Research InstituteEnergi Simulation
KeywordsGeomechanicsMechanicsPetroleum engineeringReservoir simulationEnhanced oil recoveryCompactionTight gasFlow (mathematics)DiscretizationGeologyGeotechnical engineeringPhysicsMathematicsHydraulic fracturing

Abstract

fetched live from OpenAlex

Abstract Gas injection has become the top choice for IOR/EOR pilots in tight oil reservoirs because of its high injectivity. The effects of nanoconfinement and geomechanics are generally considered as non-negligible, but its coupled effects and resulting flow and displacement are still not well understood for gas injection. We hence present a general compositional model and simulator to investigate the complicated multiphase and multicomponent behaviors during gas injection in tight oil reservoirs. This compositional model is able to account for vital physics in unconventional reservoirs, including nanopore confinement, molecular diffusion, rock-compaction, and non-Darcy flow. The MINC method is implemented to handle fractured media. The nanopore confinement effect is modeled by including capillarity in VLE calculations. The rock compaction effect is represented by solving the mean stress from a governing geomechanical equation which is fully coupled with the mass balance equations to ensure the numerical stability as well as a physically correct solution. The equations are discretized with integral finite difference method and then solved numerically by Newton's method. The simulator is validated against a commercial compositional software (CMG-GEM) before it is applied to simulate gas injection. Huff-n-puff with dry gas in Eagle Ford is investigated. The simulation result shows that if the reservoir pressure is much higher than the bubble point pressure, the nanopore confinement effect will have a minimal impact on the recovery factor (RF) for both the depletion and the first few cycles of gas huff-n-puff. Geomechanics is found to be an influencing factor on RF but not always in a detrimental way, as enhanced rock compaction drive could offset the reduction of permeability in certain scenarios. Gas huff-n-puff would improve the RF of each component compared with the depletion. The heavy component would first have a higher recovery than the light component at the first few cycles of huff-n-puff, but its RF will be outpaced by the light component when the gas saturation in the matrix surpasses the critical gas saturation. Lastly, considering the nanopore confinement effects would slightly reduce the RF of the light component but increase the RF of the heavy component after huff-n-puff when combined with the critical gas saturation effect in the matrix. This study presents a 3D multiphase, multicomponent simulator which is a practical tool for accurately modeling of primary depletion as well as gas injection IOR/EOR processes in unconventional oil reservoirs. This simulator is not only of great importance for assisting researchers to understand complex multiphase and multicomponent behaviors in tight oil production but also of great use for engineers to optimize gas injection parameters in field applications.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.326
Threshold uncertainty score1.000

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 head0.021
GPT teacher head0.257
Teacher spread0.236 · 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