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Record W2533251241 · doi:10.2118/1115-0104-jpt

Technology Focus: Offshore Production and Flow Assurance

2015· article· en· W2533251241 on OpenAlexaff
Sally A. Thomas

Bibliographic record

VenueJournal of Petroleum Technology · 2015
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsConocoPhillips (Canada)
Fundersnot available
KeywordsDowntimeProductivityWorkoverCash flowProduction (economics)Petroleum industryPetroleum engineeringEngineeringEnvironmental scienceBusinessEnvironmental engineeringReliability engineeringEconomicsFinance

Abstract

fetched live from OpenAlex

Technology Focus The oil- and gas-production industry has experienced dramatic price swings over the past year. Crude-oil (spot) prices have varied from more than USD 120/bbl to less than USD 40/bbl since the third quarter of 2014. In high-price environments, technology can be developed economically to exploit additional resources. In low-price environments, technology must be deployed selectively to optimize production. New technology may still be developed and applied, but it is likely to be very focused on reducing operating costs; on the other hand, existing technologies may be revisited, to be applied more effectively. At all times, industry seeks to improve well productivity. Well productivity is critical during low-price cycles; it is directly related to cash flow and to reserves calculations. Major advances in inflow-control-device (ICD) design and installation have occurred over the past few years. These devices generally are used to manage water breakthrough or to improve the production profile across a large interval. The goal is to minimize workover requirements while optimizing hydrocarbon production costs. Paper SPE 171836 discussed results of an extensive study to compare actual well performance to design with ICDs. The study includes results of failure analyses, which showed that material selection is important for a device to reach its design life. System operation affects field productivity. Slugging flow in subsea flowlines and risers, leading to pressure surges and excessive liquid levels in the first stage separator, is a major contributor to downtime and suboptimal performance in offshore production facilities. Production facilities operate most efficiently when flows are fairly stable. Slug-suppression technology has been applied historically in shallow water and onshore facilities. Paper SPE 170731 described the successful use of active slug-suppression technology in a deepwater facility. This is an example of integrating existing technologies— slug suppression and process-simulation/ process-control theory. The number of subsea wells is continuing to increase. These wells typically are designed to higher standards than onshore or platform wells because interventions are difficult and costly. However, lower-cost options are needed in the event that an intervention is required. Paper SPE 173647 described a case study in which a contingency riserless (light) intervention plan was implemented. Although this technology may not be applicable to all subsea well- intervention needs, it demonstrates that lighter (lower-cost) options should be considered on the basis of an engineering assessment. The oil and gas industry is a cyclical business. Technology progress can be made during the high- or the low-price phase. As engineers, we have the responsibility to identify and apply appropriate technologies throughout all phases of the business cycle. Let us rise to the challenge. JPT Recommended additional reading at OnePetro: www.onepetro.org. SPE 170780 Autonomous Inflow-Control Valves—Their Modeling and Added Value by Eltazy Eltaher, Heriot-Watt University, et al. SPE 172776 Integrated Approach to Liquid Management for Deepwater Gasfield Developments by S. Ahmed, BG Group, et al. OTC 25656 Pipeline Repair Using Epoxy Technology by Kenneth Bryson, Subsea 7, et al.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.859
Threshold uncertainty score0.555

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.216
Teacher spread0.208 · 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 designOther design
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

Citations0
Published2015
Admission routes1
Has abstractyes

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