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Record W4386122807 · doi:10.1016/j.ptlrs.2023.08.004

Modeling displacement flow inside a full-length casing string for well cementing

2023· article· en· W4386122807 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePetroleum Research · 2023
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsAthabasca UniversityUniversité Laval
FundersMitacsUniversité Laval
KeywordsAnnulus (botany)Displacement (psychology)Computational fluid dynamicsDrill stringCasingFlow (mathematics)Fluid dynamicsMechanicsEngineeringPipe flowMechanical engineeringSimulationComputer scienceTurbulenceDrillMaterials sciencePhysics

Abstract

fetched live from OpenAlex

While computer modeling of annular displacement efficiency is widely applied in cementing engineering, modeling the displacement flow inside a casing or drill string for cementing operations has received less attention. Although predicting displacement efficiency inside a full-length pipe is desired by cementing engineers, the attempt of developing a model with both efficiency and accuracy faces challenges. Access to computer simulators for this purpose is limited. Compared with annular flow, the displacement flow inside pipe, although within a simpler geometry and without eccentricity effect, is not simpler in physics, modelling strategy and predictability, because a variety of flow patterns and flow instabilities can develop to create complicated fluid interfaces. In this paper, we present an integrated numerical model developed to simulate displacement flows inside a full-length pipe, which connects an existing annulus model to enable complete displacement simulations of cementing jobs. The model uses three-dimensional grid to solve fluid concentrations with degrees of mixing, and incorporates flow instability detection and flow regime determination. Applied in cementing, the model accounts for effects of pumping rate, well inclination, pipe rotation, fluid densities, rheological parameters and more. This computationally efficient model does not rely on high-resolution mesh as often required by conventional Computational Fluid Dynamics models, thus it is suitable to be implemented in a cementing software for daily use by well cementing engineers. The methodology of the model is discussed in detail in this paper. To validate the model, we examine simulation results against experimental results obtained in our laboratory tests and CFD simulations; acceptable agreement is found under different testing conditions. We also presented two case studies of real cementing jobs with cement evaluation logs compared to simulation results, showing that the model can predict consistent displacement efficiency results.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.425
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.068
GPT teacher head0.323
Teacher spread0.255 · 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