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Record W1998569699 · doi:10.2118/171080-ms

New Technology for Flow Assurance in an Extra Heavy Oil Field: Case Study in the Akacias Field

2014· article· en· W1998569699 on OpenAlexaff
Andres Javier Chaustre Ruiz, John Jairo Plazas, Elkin Alberto Ramirez

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsNalco (Canada)
FundersEcopetrol
KeywordsNaphthaDiluentWellheadViscosityDilutionFlow assurancePetroleum engineeringRheologyFlow (mathematics)RheometerSurface tensionProcess engineeringMaterials scienceChemistryPulp and paper industryChemical engineeringEngineeringMechanicsComposite materialCatalysisThermodynamicsOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract This paper has the objective of presenting new technology, ensuring flow in the extra heavy crude field. The idea of this paper is to outline a successful pilot study carried out in the Colombian Eastern Llanos region where the effect of chemical product (flow improver) is exposed during the production of fluids from the pilot study well and the reduction of naphtha injected into the wellhead (diluent used for the movement of the surface crude). As the main fundamental, the modification of the rheology properties of the crude through change in apparent viscosity due to the effect of loads and interfacial molecular tension within the fluid, enabling improved mobility of fluids from the reservoir to production facilities via surfactant resins that interact with the colloidal particles in the crude thus reducing its viscosity. The pilot is divided into two phases, the first phase consists of determining which of the two flow improvers produces the best performance, and the second phase consists of the application of the selected flow improver to reduce the dilution of naphtha, that is used during the processing of crude to reduce its viscosity. Within the study, a financial evaluation is presented for each of the pilot phases, including a revision of the ESP pumps efficiency within different scenarios. Finally one of the most telling conclusions is mentioned, as the flow improver enables the possible reduction of naphtha injection by dilution in values close to -30%, increasing well production to close to 40%.

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)0.001
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 score0.351
Threshold uncertainty score0.327

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.026
GPT teacher head0.318
Teacher spread0.292 · 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

Citations6
Published2014
Admission routes1
Has abstractyes

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