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Record W2079580141 · doi:10.2118/92895-ms

Integrated Approach for Improving Development of a Mature Field

2005· article· en· W2079580141 on OpenAlexaff
Bradford W. Sincock, Bambang Wisnu Handono, Fauzy Achmad Mayanullah

Bibliographic record

VenueSPE Asia Pacific Oil and Gas Conference and Exhibition · 2005
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsPetro-Canada
Fundersnot available
KeywordsPetroleum engineeringWorkoverCompletion (oil and gas wells)Water injection (oil production)DrillingOil fieldGeologyEngineering

Abstract

fetched live from OpenAlex

Abstract Makmur Field, South Sumatra, Indonesia, is a faulted anticlinal rollover with its major mutli-layer reservoir of distributary mouth bar, delta front sheet, and sub-aqueous channel sand. The field reached its production peak at end of 2001. In order to hold back the bottom or peripheral water encroachment, arrest promptly production declining, and ensure the maximum final recovery, integrated reservoir characterization including detailed microfacies and structure mapping, 3D geostatistic modeling, and subsequent reservoir simulation coupled with routine drainage radius analysis have been conducted. Meanwhile, pressure build-up test and production monitoring were optimized and selected performed. The effective reservoir management including development infilling, optimized well sidetracking, dual string well completion, gas lifting, active workover operation, as well as drilling of injector and optimization of facility utilization for water injection enhancement has been implemented, which provided a road map for improving the field development. Application of the integrating reservoir management techniques resulted in substantial added reserve and mitigated pressure dropping. The successful infilling and water shut in workover have obviously arresting production declining rate.

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: none
Teacher disagreement score0.967
Threshold uncertainty score0.339

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 head0.018
GPT teacher head0.236
Teacher spread0.218 · 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

Citations6
Published2005
Admission routes1
Has abstractyes

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