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Record W2396582649 · doi:10.2118/180743-ms

Evaluation of Horizontal Liner Installation Loads Using Advanced Numerical Simulation Technique

2016· article· en· W2396582649 on OpenAlexaboutno aff
Gang Tao

Bibliographic record

VenueSPE Canada Heavy Oil Technical Conference · 2016
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsPetroleum engineeringSteam-assisted gravity drainageCompletion (oil and gas wells)Directional drillingSteam injectionEngineeringSubmarine pipelineLead (geology)Well controlRange (aeronautics)DragGeotechnical engineeringGeologyMarine engineeringOil sandsDrillingAsphaltMechanical engineering

Abstract

fetched live from OpenAlex

Abstract Horizontal wells have been widely used to significantly increase reservoir exposure in a wide range of conventional and unconventional oil and gas recovery applications, including tight-rock and multi-stage fracturing, offshore, primary and thermal heavy oil projects. In the heavy oil and bitumen reservoirs of the Western Canadian basin, horizontal wells have been extensively employed in Steam Assisted Gravity Drainage (SAGD) and Cyclic Steam Stimulation (CSS) in-situ recovery projects. In SAGD applications, the lateral sections of these horizontal wells are generally completed with various types of sand control systems, including slotted liners, Wire Wrapped Screens (WWS) and premium screens with a growing percentage incorporating some form of downhole injection or production Flow Control Devices (FCD). Given the relatively shallow depths, low reservoir pressures and high fluid rates of these applications, these horizontal wells are often constructed with relatively high build rates and large diameter tubulars. In an effort to improve project economics, well designs with increasingly longer lateral sections are being pursued. In an effort to reduce potential damage during installation, engineering assessments are commonly conducted to ensure that the structural capacity of these liners exceeds the demand of the combined installation loads caused by liner-wellbore interaction and liner string buoyant weight, often with consideration of a suitable safety margin. To date, given the number of influential parameters, the complex nature of the analysis and corresponding computational demands, methods which employ significant simplifications, such as soft-string torque and drag (T&D) models, have been commonly used to assess liner installation loads. In addition, it appears that the current commercial soft- and stiff-string T&D analysis tools do not consider the nonlinear load capacity envelopes of the sand control liners in the evaluation to assess the potential damage under combined loading during installation. Advanced numerical methods, such as Finite Element Analysis (FEA), have also rarely been employed to evaluate the liner installation loads due to the complex nature of this problem. This paper presents an advanced stiff-string T&D analysis approach developed using the commercial FEA program Abaqus. To demonstrate the application of this approach, several example cases are presented simulating the installation of the slotted liner design into a horizontal SAGD well. In these T&D analyses, wellbore and tubulars were modeled using pipe elements which accurately capture various geometric parameters and associated mechanical responses of the tubulars. Contact interaction and the clearances between the tubulars and the wellbore were modeled. Different friction factor (FF) values were assigned to the cased and open hole sections of the well. By incorporating the load capacity envelopes of the specific slotted liner design into the analysis, this paper demonstrates how this methodology may be applied to assess the load responses and potential damage risks associated with running large diameter liners into high build rate extended-reach horizontal wells. The approach presented in this paper may be expanded to various tubular, completion equipment and drill string running applications.

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: none
Teacher disagreement score0.858
Threshold uncertainty score0.816

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 head0.029
GPT teacher head0.263
Teacher spread0.234 · 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
Published2016
Admission routes1
Has abstractyes

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