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Record W2008453035 · doi:10.2118/2007-060-ea

Modelling Geomechanics Into Petroleum Reservoir Numerical Simulation: A Coupled Technique in a Water Injection Project

2007· article· en· W2008453035 on OpenAlexafffund
L.G. Rodrigues, L.B. Cunha, Rick Chalaturnyk

Bibliographic record

VenueCanadian International Petroleum Conference · 2007
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsUniversity of Alberta
FundersPetrobrasUniversity of Alberta
KeywordsGeomechanicsPetroleum engineeringReservoir simulationPetroleumGeologyWater injection (oil production)Geotechnical engineering

Abstract

fetched live from OpenAlex

Abstract Once a reservoir has been discovered it is essential to characterize it as accurately as possible in order to determine the most effective way of draining the recoverable oil. Reservoir characterization involves a great amount of data from geology, laboratory and production. An important data aspect in reservoir characterization is whether or not to consider geomechanic effects on the reservoir response. This is true in several exploitation scenarios and in particular when designing water injection projects for high-pressure/high-temperature reservoirs. This work describes the construction and numerical simulation of a reservoir model with incorporation of geomechanics for a real oilfield producing under waterflooding. Firstly, the model boundaries are prolonged to highlight the reservoir geomechanical behavior in the producing area. Secondly, the reservoir model is populated with geomechanical variables like vertical and horizontal stresses and equilibrated. Thirdly, a coupled procedure is implemented that allows the incorporation of geomechanics during numerical simulation of reservoir behavior. In this coupled procedure a commercial numerical flow simulator resolves the fluid flow behavior while a commercial geomechanical package controls the deformation aspects that occur during waterflooding. The technique developed in this work has been applied for reservoir performance forecasting of a giant Brazilian deepwater oilfield producing by water injection. Results obtained in this real case problem revealed important geomechanical features that must be considered in complex oil exploitation project scenarios in which limited information and production uncertainties are present. Introduction There are five critical areas in the process of modeling deepwater reservoirs. These are geological and geophysical modeling, reservoir characterization, reservoir flow modeling, facilities/flow assurance, and uncertainties/risk analyses. This research will focus more in reservoir flow modeling. This work describes a general modeling process that improves reservoir understanding and performance forecasting. These factors are extremely important in a high cost, high-risk deepwater environment, where wrong decisions lead to expensive mistakes and can materially affect a company's financial standing. In this work, a methodology is developed which enables the incorporation of key mechanisms and parameters to solve a numerical reservoir simulation problem that considers geomechanical aspects. The proposed technique utilizes an iterative-coupled reservoir-geomechanical modeling approach to capture the link between flow and in-situ stresses. The main objective of this research is to explore uncertainties of flow through an oil reservoir that contains a major fault and which it is producing under water injection. It also aims on investigating the role of geomechanics in the fault reactivation. The usefulness of the technique developed here is illustrated for reservoir performance forecasting of a real Brazilian deepwater oilfield. The solution achieved to this real case problem will be considered in complex oil exploitation project scenarios in which limited information and production uncertainties are present. This work presents an approach to integrate geomechanics at the early stages of a field development.

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)0.001
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: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.282
Teacher spread0.254 · 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

Citations2
Published2007
Admission routes2
Has abstractyes

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