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Record W4239977981 · doi:10.2118/2007-153

Numerical Modelling of Geomechanical Response of Sandy Shale Formation in Oil Sands Reservoir During Steam Injection

2007· article· en· W4239977981 on OpenAlexaff
J. Du, R.C.K. Wong

Bibliographic record

VenueCanadian International Petroleum Conference · 2007
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGeologyOil shaleOil sandsSteam injectionPetroleum engineeringGeomechanicsShale oilUnconventional oilGeotechnical engineeringPetroleum reservoirPetrology

Abstract

fetched live from OpenAlex

Abstract In this study, we conducted numerical simulations of steam injection into a horizontal well. A sandy shale layer with thickness of 1 meter is overlain 2 meters away from the injection well. The numerical simulation aims at studying the variations of the stress state and the permeability in the sandy shale layer during steam injection, and to provide useful effective stress paths for laboratory experiments to follow when testing material properties of the sandy shale. Introduction Sandy shale formations of several meters in thickness frequently exist in oil sands deposit. Their material properties, sedimentary characteristics and distribution in a SAGD reservoir are important factors that determine SAGD performance and strategies of positioning horizontal well pairs. It has been confirmed that sandy shale formation is usually very sandy and discontinuous; therefore, they are expected to be permeable to variable degrees during SAGD operations. This is completely different from the shale interbed which has extremely low permeability and can be considered as impermeable. Analysis of stress state change and anisotropic permeability variation in the sandy shale requires conducting coupled simulation of geomechanics and thermal reservoir flow. Numerical modeling of the coupled processes is historically carried out in the areas of geomechanics modeling and the reservoir simulation. Gutierrez and Lewis(1) extend Biot's theory to multiphase fluid flow in deformable porous media. Based on their formulation, they conclude that the coupling between the geomechanics and the multiphase flow occurs simultaneously. Thus, fully coupled system equations of deformations, multiphase flow and heat transfer should be solved simultaneously. Development of such kinds of fully coupled geomechanics-multiphase flow-heat transfer simulators needs tremendous effort, since the existing FEM geomechanics codes and the FDM reservoir simulators cannot be used. Settari and Mourits(2) present an approach to couple the stress-strain behavior to multiphase flow, heat transfer using porosity as a coupling parameter. The geomechanics module and the thermal reservoir simulator are used in a staggered manner. Pore pressure and temperature changes are calculated from the thermal reservoir simulator and transferred to the geomechanics module. The stress and the displacement changes are then calculated in the geomechanics simulation. An iterative algorithm is used to ensure that the porosity calculated from the geomechanics module is the same as that from the thermal reservoir simulator. The staggered technique employed to solve the coupled system equations allows for the use of the existing geomechanics codes in conjunction with a standard reservoir simulator. Currently, most of the commercial coupled geomechanics-multiphase flow-heat transfer simulators are developed in this way. The disadvantage of these kinds of coupled simulators is that the thermal reservoir module, usually developed using finite difference method FDM) cannot accommodate the full permeability tensor, since they adopt the standard discretization scheme such as 5-spot for 2-D problems and 7-spot for 3-D problems. Much effort has been made in developing a coupled geomechanics-reservoir simulator using finite element methods (FEM) by Du and Wong (3, 4, 5, 6, 7, 8). The developed simulator has the capability of handling full permeability tensor and strain-induced permeability model.

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 score0.044
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.017
GPT teacher head0.226
Teacher spread0.210 · 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

Citations1
Published2007
Admission routes1
Has abstractyes

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