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Record W2085401305 · doi:10.2118/09-02-08-tb

Importance of Drilling Fluids' Rheological and Volumetric Characterization to Plan and Optimize Managed Pressure Drilling Operations

2009· article· en· W2085401305 on OpenAlexaff
Barkim Demirdal, J.C. Cunha

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2009
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsUniversity of Alberta
FundersPetrobrasUniversity of Tulsa
KeywordsDrilling fluidRheologyCasingDrillingGeologyPetroleum engineeringViscosityShear (geology)DrillUnderbalanced drillingMechanicsGeotechnical engineeringMaterials scienceMechanical engineeringEngineeringComposite material

Abstract

fetched live from OpenAlex

Abstract Managed Pressure Drilling (MPD) is an alternative to overbalanced and underbalanced drilling in conditions where pore pressures and fracture gradients are so close to each other (depleted reservoirs, deep and ultra-deep offshore reservoirs) that it is not possible to drill significant depths without setting a casing. While MPD enables an operator to drill longer footages without setting a casing, it requires precise estimation of equivalent circulating density (ECD) during drilling and static bottomhole pressure (SBHP) during non-drilling times. General practice in the drilling industry is to use rheological and volumetric properties of drilling fluids measured at surface to estimate ECD and SBHP. Consequently, ECD and SBHP measured using MWD and LWD tools in the field do not match the theoretical calculations. This study shows the importance of introducing the effect of downhole conditions to hydraulic equations in order to estimate ECD's and SBHP's accurately. Paraffin-based synthetic drilling fluid is used for this purpose. The effect of pressure and temperature on density of fluid is determined using PVT cell experiments. An equation relating the density of the fluid to pressure and temperature is determined using linear and non-linear regression techniques. Rheological characterization of the fluid was obtained on a Fann 75 HPHT rotational viscometer. A Bingham plastic model was used to define shear stress - shear rate relation of the fluid in all pressures and temperatures. The effect of pressure and temperature on plastic viscosity and yield point are determined using linear and non-linear regression techniques, similar to the ones used in PVT analysis. Both onshore and offshore cases are investigated and the effect of incorporating downhole effects to density and rheological parameters on ECD are analyzed. Introduction As a result of the depletion of most of the known reservoirs around the globe, companies are searching for oil and gas in more challenging areas such as deep and ultra-deep offshore locations. In addition, high oil prices motivate the industry to produce the last measure of oil from mature oil fields where the pressure is depleted. The conventional overbalanced drilling technique creates a major drawback to drilling in ultra-deep and depleted reservoirs. In ultra-deep offshore locations, pore pressure and fracture pressure gradients are very close to each other, and with conventional drilling, it is hard (sometimes impossible) to drill a hole up to the target depth(1). In the case of depleted reservoirs, pore pressure is so low that it is not possible to drill without damaging the formation. These challenges create the need for a new technology to drill in such hostile environments. Managed Pressure Drilling allows drilling of longer intervals by drilling overbalanced while maintaining near constant bottomhole pressure, using a combination of drilling fluid density, equivalent circulating density (ECD) and casing back pressure in a closed system(2, 3). While MPD will enable operators to drill longer sections and use light drilling fluids, it does require better wellbore pressure management. Only by managing the wellbore pressure, will it be possible to decide on which type of drilling fluid to use and how deep it can be used.

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 score0.064
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
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 head0.005
GPT teacher head0.172
Teacher spread0.167 · 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

Citations14
Published2009
Admission routes1
Has abstractyes

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