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Record W2041792382 · doi:10.2118/2006-124

A Coupled Model to Simulate the Fluid Flow in the Reservoir and Horizontal Wellbore

2006· article· en· W2041792382 on OpenAlexafffund
Peng Gui, J.C. Cunha, L.B. Cunha

Bibliographic record

VenueCanadian International Petroleum Conference · 2006
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWellborePetroleum engineeringGeologyFlow (mathematics)Reservoir simulationFluid dynamicsMechanicsComputer sciencePhysics

Abstract

fetched live from OpenAlex

Abstract A coupled model is developed to simulate the pressure and flow rate distribution in both reservoir and horizontal wellbore simultaneously. Most conventional analytical and numerical solutions neglected the interaction of the fluid flow between reservoir and wellbore, which usually lead to erroneous predictions of performance behavior. In this paper, the multisegment horizontal wellbore model is coupled with a fully implicit reservoir model to study the effect of finite conductivity on flow in horizontal wellbore and the corresponding effect on the non-uniform influx distribution from the reservoir. In this work, a three-dimensional black-oil reservoir simulator is developed using a hybrid technique (local grid refinement) around the horizontal wellbore. The multi-segment wellbore model is built by gridding the wellbore into segments that interact with the influx from reservoir sandface through the local reservoir hybrid grid (radial region). The wellbore model, which is coupled with the 3-D reservoir model, can deal with different types of pressure losses and the flow properties are updated with real-time local flowing conditions. Therefore, the coupled model reveals the fluid flow in the reservoir near the wellbore and also the realistic characteristics in the horizontal wellbore. The principle of this coupled model is based on the pressure continuity and mass balance at the sandface. Flow in the reservoir is described as a parabolic type partial differential equation, while flow in the horizontal wellbore is a hyperbolic type partial differential equation. All the variables for both reservoir and wellbore domains are obtained by solving the integrated Jacobian matrix simultaneously, so the simulation results can reflect the characteristics and interactions of the fluid flow between the reservoir and wellbore. The corresponding pressure and flow rate around the wellbore also interact with the actual flow velocity in the wellbore. The coupled model has several prospective applications, including horizontal well length optimization, completion design, water/gas breakthrough prediction and well performance prediction. A case study investigating the finite conductivity of horizontal wellbore is presented in this paper. Introduction Horizontal wells are routinely used in many new oil and gas fields as well as in further development of mature fields because of their advantages over conventional wells. Many researchers have studied various aspects of horizontal well production and developed some models to simulate the production behavior for horizontal wells. Although some researchers [1,2,3] studied the pressure drop along a wellbore due to friction, most former developed simulators neglected the interaction of the fluid flow between the reservoir and wellbore, which usually led to erroneous predictions of performance behavior, especially for those with high-permeability and high production field cases. In this work, a coupled model of the reservoir fluid flow and horizontal wellbore hydraulics is developed to reveal the realistic flow behavior in the reservoir near the wellbore and also the flow characteristics in the horizontal wellbore. To take advantage of the radial nature of flow around wellbore, the model adopts a hybrid grid system (local grid refinement) with Cartesian grid for the reservoir region and cylindrical grid for the wellbore region.

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 score0.083
Threshold uncertainty score0.995

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 head0.019
GPT teacher head0.247
Teacher spread0.228 · 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

Citations5
Published2006
Admission routes2
Has abstractyes

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