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Record W3022128149 · doi:10.2118/0520-0059-jpt

Intelligent Completion Installations Instrumental in Brazilian Presalt Development

2020· article· en· W3022128149 on OpenAlexaboutno aff
Chris Carpenter

Bibliographic record

VenueJournal of Petroleum Technology · 2020
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsCompletion (oil and gas wells)MilestoneSubmarine pipelineProject managementEngineeringOperations researchComputer scienceCivil engineeringEnvironmental sciencePetroleum engineeringGeographySystems engineeringArchaeology

Abstract

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This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 195935, “Hundredth Intelligent Completion Installation: A Milestone in Brazilian Presalt Development,” by Eduardo Schnitzler, SPE, Luciano Ferreira Gonçalez, and Roger Savoldi Roman, Petrobras, et al., prepared for the 2019 SPE Annual Technical Conference and Exhibition, Calgary, 30 September–2 October. The paper has not been peer reviewed. Presalt heterogeneous carbonate reservoirs typically present long net pays, high production and injection rates, and flow-assurance risks. The complete paper presents a discussion of the use of intelligent well completion (IWC) in Santos Basin Presalt Cluster (SBPSC) wells. With the use of this technique and the introduction of several improvements, well-completion time was reduced to less than 50% of that seen in initial wells. Introduction The SBPSC is in ultradeep water—be-tween 1900 and 2400 m—approximately 290 km offshore Rio de Janeiro. From the opening phases of SBPSC development, IWC has been crucial, not only to improve reservoir management but also to provide better capability to deal with reservoir uncertainty. Extensive use of IWC has presented challenges for well-engineering teams because of the complexity associated with this completion design. Minimizing well-construction costs and risks is essential for any project, but, in ultradeep water, it is even more critical. Intelligent Completion Systems Internal risk assessments were conducted to determine optimal solutions for the first production-development project. Direct hydraulic systems were chosen to actuate interval control valves (ICVs) remotely in two- and three-zone wells. This field-proven system was considered the simplest and most reliable for long-term use. However, because the wet tree (WT) designed for SBPSC projects comprises multiplex control systems, addressing these additional integration issues during the planning phase demands exceptional efforts. An extensive integration process had to be conducted to assure that every interface would perform correctly during the production phase. Most projects consider the use of a 95/8- or 97/8-in. production casing set and cemented in front of the reservoir, allowing the use of wider completion equipment, including IWC components. The use of larger-diameter accessories provides the production string the capability to deal with higher mechanical loads, a feature important in the absence of an expansion joint. The full completion string, installed in a single run, has no separated lower completion system. This integral completion design avoids the use of down-hole connections and allows control lines to be run continuously during deployment and the equipment functionality to be tested fully during installation. Dedicated pressure and temperature sensors are located on each interval and inside the completion string. For two-zone production wells, three chemical-injection points are available. Two of them are upstream of the ICVs to prevent scaling formation in the valves by injecting scale inhibitors in the annulus. These chemical-injection points are especially important when producing the well with any of the ICVs in choked positions, leading to higher-pressure drops across the valves. A third chemical-injection system, injecting into the production string, is above the upper packer and may be used for a second chemical injection or as a backup for the main chemical-injection systems.

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 score0.152
Threshold uncertainty score0.409

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.020
GPT teacher head0.256
Teacher spread0.235 · 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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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