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Record W2084807169 · doi:10.2118/84861-ms

Optimizing Oil Recovery of XJG Fields in South China Sea

2003· article· en· W2084807169 on OpenAlexaff
Donghong Luo, Zhiqiang Jiang, JACK DVORKIN MARIO A. GUTIERREZ, Kim A. Schwab, M. Spotkaeff

Bibliographic record

VenueSPE International Improved Oil Recovery Conference in Asia Pacific · 2003
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsConocoPhillips (Canada)
Fundersnot available
KeywordsInfillCasingPetroleum engineeringDrillingSubmarine pipelineGeologyCompletion (oil and gas wells)Oil fieldEnvironmental scienceMining engineeringEngineeringGeotechnical engineeringCivil engineeringMechanical engineering

Abstract

fetched live from OpenAlex

Abstract China National Offshore Oil Corporation (CNOOC), Shell and Phillips China Inc. (PCI) are partners in the development of XJG oilfields located in the South China Sea. The XJG fields are considered to be in the mature phase and challenging PCI (the field operator) with lifting and surface treatment problems due to high water cuts, and the limited amount of slots available in the existing platforms for infill drilling. A sandstone reservoir with average permeability over a Darcy and strong bottom water drive aquifer support is experiencing water breakthroughs from high permeable layers leaving hydrocarbons pockets behind. Typical completions include sand control devices such as gravel packs and Fracpack inside 9–5/8" casing. The following list briefly summarizes the main challenges presented by the XJG fields at this stage: Total production close to the limit of surface treatment facilities: 550,000 BFPD.No more slots available for infill drilling, only 1 left reserved for an ERD well.Sand production aggravated due to wells producing at high water cuts.Overall oil recovery still below expected objectives. PCI created a team including engineers from a service company working toward the common objective of "Maximizing Oil Recovery" in XJG fields making use of state of the art simulators, tools and innovative technologies specially tailored by technology centers to provide reliable answers in South China Sea completions and formation environments. Reservoir modeling enabled the team to successfully identify remaining hydrocarbon areas in the fields. Resistivity measurements confirmed formation fluid saturations through the relatively large casing with high level of confidence. Reservoir modeling and resistivity measurements behind casing along with the innovative Geosteering tools and techniques helped plan and execute drilling new wells or sidetracking existing wells with high water cuts, effectively increasing oil rates and ultimate field oil recovery. Encouraging results from this project along with other actions taken by PCI in the field have achieved an outstanding increase of 28 % in oil production of the new sidetracks while reducing the total amount of water at surface (in excess of 17,000 BWPD), saving PCI major expenditures on fluid treatment and/or upgrading platform facilities; benefiting from teamwork implementing innovative techniques in existing assets maximizing oil field recovery at effective cost.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.258
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.019
GPT teacher head0.256
Teacher spread0.237 · 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.

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

Citations1
Published2003
Admission routes1
Has abstractyes

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