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Record W4393009692 · doi:10.56831/psen-04-119

A Doomed Reservoir Surprisingly Became a Mature Reservoir with Potential: Water Dump Flooding Case Study in Boca Field

2024· article· en· W4393009692 on OpenAlexaff
Elias R. Acosta, Carlos Ortega, Gina Luz Vega Riveros, Luis Molina Sánchez, Ernesto Rosales

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsGeomechanica (Canada)
Fundersnot available
KeywordsWater floodingFlooding (psychology)Petroleum engineeringHydrology (agriculture)GeologyEnvironmental scienceField (mathematics)Water reservoirPetrologyGeotechnical engineeringMathematics

Abstract

fetched live from OpenAlex

Water Dump Flooding is less well known for revitalizing mature fields.However, in the Boca Field, specifically Reservoir 95 Y-102, this is exactly what occurred.A periodic review of this mature field suggested abandoning the only producing well in this reservoir, well X-3, because an adjacent well, X-6, located above the dip was known to produce a 99% water cut.Although other wells were produced in the reservoir, only a 12% recovery was achieved.Therefore, an integrated study to re-evaluate the parameters and properties of Reservoir 95 Y-102 began in 2005.During the well analysis, it was found that the water production of well X-6 was the result of the communication behind the casing of the well with the underlying aquifer 101 and not because of the advancement of the oil-water contact, as initially suggested.Recompletion of well X-3 was recommended because an injection process known as dump flooding was underway.In addition, aquifer support for production over the previous seven years strongly indicated that dump flooding would produce the desired production increases.Under sub-optimal conditions, accidental Water Dump Flooding rejuvenated the producing well, increasing production to more than 300 BOPD with an acceptable water cut of 61%.The steps followed for the analysis and understanding of the process that occurred and how we took advantage of this accidental Dump Flooding raised the production from nearly zero to over 100,000 barrels produced in a single year.This lays the foundation for using Dump Flooding as a production and development strategy for other projects in the area.

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 score0.024
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.017
GPT teacher head0.280
Teacher spread0.263 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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