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Record W4387229564 · doi:10.2118/216852-ms

Impact of Change in Mud Weight & Offset Well Injection Pressure on Cement Sheath Integrity; Case Study for UAE Offshore Field.

2023· article· en· W4387229564 on OpenAlexaff
Azza Elhassan, Patrick Manga, Mohamed Samir Abdellatif, Ahmedagha Hamidzada, Kerron Andrews, Takahiro Takahiro, M. El-Sayed Sherif

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsHaliburton Forest & Wild Life Reserve
Fundersnot available
KeywordsCementPetroleum engineeringAnnulus (botany)Oil wellDrillingCasingGeotechnical engineeringSubmarine pipelineInjection wellPore water pressureDrilling fluidGeologyMaterials scienceEngineeringComposite materialMechanical engineering

Abstract

fetched live from OpenAlex

Abstract As drilling horizontal wells became more complex with multiple production drains from various reservoirs, zonal isolation has become a key challenge to be achieved. Many studies have shown the impact of change in mud weight during drilling activities on wellbore stability, without considering the consequences on the set cement. This case study will be focusing on drilling activities on offset wells, evaluating & exploring how mud weight changes can affect wellbore instability and damage the cement sheath despite the fact that the cement evaluation logs showed good results days after the cement job and before the mud weight reduction. The wells being studied have been analyzed using Finite Element Analysis (FEA) modeling after the cement was set and exposed to a change in mud weight as well as being exposed to the offset injection well. FEA computer simulations have enabled this modelling to determine the optimal mechanical properties required for the cement slurries to withstand those load limits (pressure testing, pressure reduction due to change of mud weight or an increase of the pore pressure due to an offset injection well). After opening the window and drilling ahead through the production drain wellbore, the wells have the same tendency of instability and require an increase in mud weight to control the inflow. This paper presents the impacts of the offset wells and the change of mud weight on the cement sheath failure in debonding, cracking, or sheath deterioration leading to gas channeling due to a micro annulus. The considered wells present the same behavior that has been highlighted above. The question to ask is why did a well that has been cemented and recorded a good cement bond fail to provide proper zonal isolation? The injection pressure of the offset well was evaluated and then used in the simulations, considering the conventional cement used during the primary cement job. The analysis has shown a complete failure of the cement integrity: cement debonding from the casing or from the formation; cement cracking and sheath failure. The cement slurry with improved mechanical properties was employed for the study under the same pressure conditions from an offset well and mud weight change. The model results demonstrate that the new cement's ability to withstand stresses has improved on the wells cemented after this study. The modelling effort presented in this paper allows for a barrier to be designed that has enhanced mechanical properties compared to the already placed barrier on the offset wells in the same field. Aside from improving the set cement mechanical properties, the mud weight change design has been improved in such a way that the mud weight must be anticipated, decreasing its impact on the barrier.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.058
GPT teacher head0.318
Teacher spread0.260 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations2
Published2023
Admission routes1
Has abstractyes

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