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Record W2084847878 · doi:10.2118/00-07-04

Using Underbalanced Drilling to Reduce Invasive Formation Damage and Improve Well Productivity-An Update

2000· article· en· W2084847878 on OpenAlexfundno aff
D.B. Bennion, F.B. Thomas, A.M.M. Jamaluddin, Ting Ma

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2000
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
FundersUniversity of Calgary
KeywordsUnderbalanced drillingDrillingPetroleum engineeringCoiled tubingLead (geology)Environmental scienceEngineeringDrilling fluidGeologyMechanical engineering

Abstract

fetched live from OpenAlex

Abstract Underbalanced drilling (UBD) is becoming increasingly used as a technique to reduce significant invasive formation damage in vertical and horizontal wells to improve production rates of oil and gas, and enhance injectivity in gas and water injection situations. UBD may be a technically demanding procedure to execute in certain reservoirs and much of the benefit with respect to mitigation of formation damage may be compromised if the underbalanced drilling operation is not screened, designed and conducted in an appropriate fashion. This paper reviews common formation damage mechanisms which may occur in reservoirs and how, in certain situations, these types of damage may be reduced or eliminated through the appropriate use of underbalanced drilling technology. Various situations in which underbalanced drilling technology may result in potential significant formation damage are also discussed. Introduction Underbalanced drilling is utilized worldwide for the drilling of horizontally and vertically oriented wells to increase rates of penetration, reduce invasive formation damage and reduce problems with drilling due to lost circulation and differential sticking. Many successful descriptions of underbalanced drilling are present in the literature(1–8). However, underbalanced drilling is not a panacea for all formation damage problems in that inappropriately designed underbalanced drilling jobs may actually result in more formation damage than if a well-designed and contemplated overbalanced job had been used in the same circumstances. This paper reviews some of the technology in use at the present time in underbalanced drilling, and illustrates some of the points which operators should be aware of before embarking on an underbalanced drilling operation. What Is Underbalanced Drilling (UBD)? A rigorous definition of UBD is a condition where the circulating pressure of the drilling fluid in contact with the formation is less than the effective pore pressure in the adjacent matrix. The desirable course of action is to have this occur along the entire exposed section of the productive pay of the reservoir, resulting in an inflow of oil, water or gas (which may be contained in the matrix) into the wellbore. The produced fluids are returned to the surface along with the circulating drilling fluid. A number of descriptions of UBD exist in the literature. They can be described as follows: Overbalanced Drilling A situation in which the equivalent circulating density of the drilling mud is sufficient that, at bottomhole conditions, the drilling fluid pressure is greater than the formation pressure, resulting in an effectively "killed" state (where no inflow of formation of fluids occurs). This has been the most common technique utilized to drill wells in the past and is still the dominant technology currently used to drill many wells. Low Head Drilling Low head drilling refers to a situation where an overbalance pressure condition, similar to that described above, is maintained but with the use of lower density oil-based fluids or possibly gasified fluids to reduce the magnitude of the overbalance pressure exerted on the formation. The prime motivation is to reduce formation damage and the potential for severe invasive losses and to increase ROP. Low head drilling is still classified as a form of overbalanced 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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.306
Threshold uncertainty score0.675

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 head0.009
GPT teacher head0.204
Teacher spread0.195 · 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

Citations12
Published2000
Admission routes1
Has abstractyes

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