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Record W4415880963 · doi:10.2118/230058-ms

Start-Up Optimization in Deep-Water Developments: Strategies for Managing Wax, Hydrates and Slugging

2025· article· W4415880963 on OpenAlexaff
Shubham Gupta, Boyina V. Ramana Prasad, Samit Pradhan, Anoop Yadav, Santoosh Kumar Gubbala, Vivek Singh, Md Imtiaz, Shivam Porwal

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicOffshore Engineering and Technologies
Canadian institutionsCarbon Engineering (Canada)
Fundersnot available
KeywordsSluggingFlow assurancePiggingPipeline (software)WaxFlow (mathematics)Submarine pipelinePipeline transportCabin pressurizationHead (geology)

Abstract

fetched live from OpenAlex

Abstract Objectives/Scope The XYZ-Field, located at Eastern offshore India, at water depth of 600 meters, features deep-water oil wells tied back to a centrally located FPSO via dual 10″ pipe-in-pipe (PIP) flow lines. Ensuring uninterrupted flow during the initial start-up phase is crucial due to challenges such as low ambient temperatures, slugging, wax deposition, hydrate formation and flow instabilities. Apart from these, well-tested results indicated a lower GOR, further complicating things. This paper presents a comprehensive set of flow assurance strategies designed to mitigate these risks and ensure a smooth production ramp-up. Methods, Procedures, Process Flow assurance simulations using OLGA and PIPESIM were conducted to evaluate transient start-up behaviour, wax deposition potential and slugging tendencies. The analysis identified several key challenges, including very low fluid temperatures at the time of well ramp-up which creating challenges like hydrate formation, wax deposition and crude gelation another issue was low flow rates at the time of wells ramp-up which leads to low thermal mass so fast decline in fluid temperature and slugging issue in pipeline which also helping in making cold spot in pipeline and leading to more wax deposition. To counter these challenges, an innovative start-up strategy was developed, which includes preheating of the line, increasing fluid flow at the time of start-up, choking at FPSO topside and optimising the start-up sequence of wells. Results, Observations, Conclusions With the help of this strategy we were able to maintain the temperature of pipeline throughout startup period above Wax appearance temperature which helped in avoiding wax deposition, crude gelation and also helped in avoiding hydrate formation another thing we eliminated the slugging using FPSO topside choke and by increasing flowrate which helped in getting stable flow at FPSO and no issue to equipment at the topside and this also stop making cold spot in riser which is more prone to wax deposition so resulted in less wax deposition. This strategy also helped in long-term operation of the field by minimizing the slugging and wax deposition, resulting in decreased frequency of mitigation measures to remove wax in the flowline. Novel/Additive Information These findings provide a robust framework for enhancing the reliability of deep-water oil field start-ups, reducing production downtime, and ensuring long-term flow assurance. The proposed innovative strategy for reducing the frequency of wax mitigation measures presents a significant improvement in operational efficiency while maintaining flow line integrity.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.865
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
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.007
GPT teacher head0.209
Teacher spread0.202 · 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.

Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations0
Published2025
Admission routes1
Has abstractyes

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