MétaCan
Menu
Back to cohort
Record W2025314493 · doi:10.2118/2007-125

New Improvements on Managed Pressure Drilling

2007· article· en· W2025314493 on OpenAlexafffund
Barkim Demirdal, J.C. Cunha

Bibliographic record

VenueCanadian International Petroleum Conference · 2007
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaPetrobrasUniversity of TulsaU.S. Department of Energy
KeywordsDrillingPetroleum engineeringComputer scienceGeologyEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Abstract One of the advantages of Managed Pressure Drilling (MPD) is to determine formation pressures and fracture gradients while drilling. However, in order to determine these pressures accurately, the rheological model of the drilling fluid and pressure losses accruing in annulus should be determined precisely. In addition to the pressure losses in annulus, pressure losses in pipes should be determined accurately in order to determine pump sizing requirements for a successful MPD operation. Un-weighted n-paraffin based drilling fluid system is analyzed in this study. HPHT rotational viscometer is used to determine how rheology of this invert emulsion system changes under down hole conditions. The fluids are tested in the temperature range of 40 – 280 °F and the pressure range of 500 – 12,000 psig. Three rheological models, Bingham Plastic, Power Law and Yield Power Law, widely accepted by the drilling industry, are used to determine rheological characteristics of the drilling fluid and compared with the experiments at various pressures and temperatures. It is found out that, at high shear rates (i.e. > 100 rpm) all models predict shear stresses accurately. However, at low shear rates only shear stresses calculated using Yield Power Law model agrees with the shear stresses measured by the HPHT rotational viscometer data. Pressure losses predictions in pipe and annulus are determined using rheological parameters measured under surface conditions and then compared with pressure losses calculated using Yield Power Law model at actual downhole pressure and temperature conditions. Effects of using surface based rheological parameters and different models on estimating pressure losses in pipe and annulus are shown. Introduction Basically, MPD is a system where wellbore pressure management is obtained by adjusting the pressures along the wellbore using a choke valve at the return line in the annulus. This modern drilling process is preferred over conventional over balance drilling as well as underbalanced drilling in areas where pore pressure and fracture gradients are very close (i.e. deep and ultra deep offshore drilling) or pore pressures are very low. MPD is indicated in situations where conventional drilling techniques are not feasible or non economical1–4. While MPD provides total wellbore pressure management and may also allow real time determination of pore pressure and fracture gradient, the accurate determination of pressure losses in the annulus is essential for the success of the operation. In addition, in the planning phase of an MPD operation, pressure losses inside the drill string should be determined accurately in order to obtain required operating pressures, pump sizing etc. Usual industry practice is to either use Bingham Plastic or Power Law model to define the shear rate – shear stress relation of the drilling fluid considering surface measurements. Similarly, the density of the drilling fluid system is determined at surface and used together with surface conditions' rheological parameters to calculate estimated pressure losses in the well. This methodology will not induce significant errors while drilling shallow onshore reservoirs with water based drilling fluids. However, in deep and ultra deep offshore applications not only the downhole conditions but also the type of fluid being used is different than regular onshore drilling.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.840
Threshold uncertainty score1.000

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 head0.009
GPT teacher head0.202
Teacher spread0.193 · 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

Citations3
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueCanadian International Petroleum ConferenceSame topicDrilling and Well EngineeringFrench-language works237,207