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Record W1993255074 · doi:10.2118/2009-019

A Mathematical Solution to Consider Geomechanics in SAGD Process

2009· article· en· W1993255074 on OpenAlexaffabout
Abul Kalam Azad, Richard J. Chalaturnyk

Bibliographic record

VenueCanadian International Petroleum Conference · 2009
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGeomechanicsProcess (computing)Computer sciencePetroleum engineeringGeologyGeotechnical engineering

Abstract

fetched live from OpenAlex

Abstract Steam Assisted Gravity Drainage (SAGD) process is a steam drive technique which is predominantly used for Alberta's unconventional reservoirs. Butler was the first researcher who proposed this technique and developed a theory to predict SAGD's performance and production. Although his theory is fast enough in terms of calculation speed for primary estimation of SAGD projects, it over-estimates the production and does not consider geomechanics. Many studies have proven now that geomechanics is inevitably a part of physics in this process. In this study, a classical theory of geotechnical engineering is employed to support the geomechanical part of a coupled simulator. Butler theory is also modified using model of slices to be a more realistic drainage model as a flow simulator. Combining these two models is able to solve fully coupled analytical analysis which has been presented in this paper. The solver is a fast and realistic proxy and can be used in any fast history matching and handling real-time data in future SAGD fields. Introduction Analytical solution has been always a favorable tool for engineers. They use it as a fast calculator for primary estimations and to handle the major problems of an engineering project. For SAGD, Butler and his colleagues1 proposed the first analytical solution that was able to predict oil production rate based on simple assumptions. The model, which is usually known as Butler theory, was firstly used as a tool to approve SAGD concept and showed the capability of such a technique in thermal oil extraction. Although the theory developed later by Butler and his team from time to time to consider more aspects of SAGD, the foundation of the theory in which geomechanics has been ignored didn't change. Other than Butler, more researchers such as Reis2, Akin3, Liang4, and Nukhaev et al.5have worked on different techniques and have proposed their models, but they do follow the same approach as Butler did. By other words, none of the models consider geomechanics as an important aspect of SAGD physics. Analytical solutions are not only helpful when there is no accurate method available, but they can also be a powerful tool when numerical-based models are time consuming. In future digital fields that real-time data flows into engineers' computers, data processing and history matching with numerical methods (i.e. using numerical-based solvers and simulators) is almost impossible. Hence, any other technique such as a robust analytical model would be a good local replacement. In the next sections a mathematical methodology has been developed to improve the current models and to make a geomechanical coupling analysis possible in an analytical theme. Based on Butler and Reis theory, a drainage model that is called in this paper as the model of slices is presented. To consider geomechanics, limit equilibrium analysis method has been adopted from geotechnical engineering. These two models together with a coupling technique are able to solve a SAGD problem fast, reasonably robust and physics-based. The proposed models are then validated with some laboratory and numerical data.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.027
GPT teacher head0.287
Teacher spread0.260 · 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 source (direct Gemma or distilled Codex), 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
Published2009
Admission routes2
Has abstractyes

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