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Record W2483902342 · doi:10.2118/00-10-05

Application of Multiphase Flow Methods to Horizontal Underbalanced Drilling

2000· article· en· W2483902342 on OpenAlexaboutno aff
S. Smith, G.A. Gregory, N Munro, Muhammad Muqeem

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2000
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsUnderbalanced drillingPetroleum engineeringDrillingDrilling fluidDirectional drillingMultiphase flowLost circulationCoiled tubingTrippingCompletion (oil and gas wells)Well drillingGeologyEngineeringMechanical engineeringMechanics

Abstract

fetched live from OpenAlex

Abstract Multiphase flow can be present in all aspects of underbalanced drilling. This paper outlines the ways in which multiphase flow pressure loss calculations can be used by drilling engineers in the design and optimization of underbalanced drilling operations. Then, detailed field measurements for several horizontal wells drilled underbalanced with coiled tubing are used to evaluate the application of existing pressure loss calculation methods to this unique application. Introduction Underbalanced drilling (UBD) is rapidly gaining popularity in the oil and gas industry. The Alberta Energy and Utilities Board ID94–3(1) defines underbalanced drilling as follows: "When the hydrostatic head of a drilling fluid is intentionally designed to be lower than the pressure of the formation being drilled, the operation will be considered underbalanced drilling. The hydrostatic head of the drilling fluid may be naturally less than the formation pressure or it can be induced. The induced state may be created by adding natural gas, nitrogen, or air to the liquid phase of the drilling fluid. Whether induced or natural, this may result in an influx of formation fluid which must be circulated from the well and controlled at surface." Benefits of Underbalanced Drilling Maintaining the pressure in the wellbore below the reservoir pressure allows reservoir fluids to enter the wellbore while UBD operations proceed, thus preventing flow of drilling fluids (and associated solids) into the formation, thereby minimizing or even eliminating formation damage. This is of particular importance in the drilling of horizontal wells as the formation is exposed to the drilling fluids for an extended period of time. Although formation damage reduction is the most widely recognized benefit of underbalanced drilling, several additional benefits are outlined below: Increased Penetration Rates Underbalanced drilling can achieve higher rates of penetration due to reduced "chip holdown" and decreased hydrostatic pressure at the bit face. Reduction in "chip holdown" refers to easier removal of drilled solids from the vicinity of the drill bit due to the flow of drilling and reservoir fluids, thus allowing the bit to drill into fresh rock continuously. The decreased hydrostatic pressure at the bit face reduces stress in the rock being drilled, allowing it to fail more easily. The experience of drilling engineers(2) familiar with UBD is that the rate of penetration can be increased by between three and ten times that of conventional drilling. Minimal Lost Circulation Underbalanced drilling gives better control in situations where fractured, low pressure, or high permeability formations may lead to the loss of drilling fluids and the associated problems that can cause. Evaluation While Drilling Data acquired in real time during underbalanced drilling operations allow for both the short term on site optimization of the UBD operation, and the longer term assessment of the well's potential. On site, the data acquired can be used to optimize drilling parameters such as the well's horizontal length, vertical depth, and orientation. Other data that can be obtained, useful both on site and long term, include fluid properties, productivity, and geological interpretations of the formation.

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 score0.674
Threshold uncertainty score0.560

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 head0.004
GPT teacher head0.224
Teacher spread0.220 · 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

Citations17
Published2000
Admission routes1
Has abstractyes

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