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Record W1999130264 · doi:10.2118/04-04-05

When Is It Important to Consider Geomechanics in SAGD Operations?

2004· article· en· W1999130264 on OpenAlexafffundabout
Richard J. Chalaturnyk, P. Li

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2004
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGeomechanicsPetroleum engineeringSteam injectionGeologyWater injection (oil production)Pore water pressureOil fieldSteam-assisted gravity drainageEnhanced oil recoveryReservoir engineeringGeotechnical engineeringOil sandsMaterials sciencePetroleum

Abstract

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Abstract This paper generalizes the typical reservoir conditions for which SAGD is being implemented or considered in order to parametrically analyze the influence of geomechanical factors on the startup and production phases of SAGD projects. Numerical simulation of the SAGD process using a thermal reservoir program and a geomechanical program is used to assess the relative influence geomechanics may have on SAGD operations. While variations to the initial dual well SAGD process are becoming numerous, this study presents analysis results for only dual well SAGD geometries. A primary focus of this research is to clearly define the role of pore volume change (compressibility or shear-induced) on the basis of fundamental geomechanical parameters and correct an ongoing misconception that formation dilation can be simulated based on injection pressure alone. Dilation is a complicated process controlled by significantly more parameters than just injection pressure. Clear, definable guidelines are presented to aid SAGD project designers in determining the relative importance of the geomechanical response of their particular reservoir. The major geomechanical/ reservoir factors studied include:initial in situ effective stress state;initial pore pressure;steam injection pressure and temperature; and,process geometry variables, such as well spacing and wellpair spacing. Introduction The geomechanical response of an oil sands/heavy oil reservoir is complex, reacting to both near and far field temperatures and pore pressures. To aid in elucidating fundamental geomechanical principles affecting the steam assisted gravity drainage (SAGD) rocess and to gain insight into a reservoir's response to thermal loading and pore pressure change, a parametric analysis of the SAGD process within three separate but similar reservoir settings was completed. Since the objective of this paper is to highlight how the geomechanical response affects the SAGD process, twodimensional analyses of reservoir cross sections with basic treatment of the inherent complex geology have been conducted. Thenalysis results presented herein are not intended to portray a history match of any particular SAGD operation. Fully coupled thermal-stress-fluid flow analyses are extremely difficult to conduct. While fully coupled mathematical formulations exist(1–3), the computational effort in their solution is onerous and continues to be an area of active research. Coupled solutions that consider single phase flow only have become common(4, 5) and are routinely utilized in both advanced reservoir simulations and geotechnical/hydrogeological simulations. Consequently, a decoupled approach was adopted for the analyses presented herein. The decoupled approach consisted of conducting a reservoir simulation of SAGD using STARS (an advanced process and hermal reservoir simulator developed by CMG in Calgary) and utilizing the temperatures and pore pressures as input to an effective stress geomechanical simulation of the formation response to SAGD. In agreement with Tortike(3), that while the removal of "feedback" to the fluid flow model would not allow conclusions to be drawn regarding the fluid solution, this decoupled approach would permit conclusions to be drawn and inferences to be made concerning the likely response of the formation to the SAGD process.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.939
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.223
Teacher spread0.214 · 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

Citations42
Published2004
Admission routes3
Has abstractyes

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