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Record W2020987554 · doi:10.2523/iptc-18224-ms

Bream Field (Phase 4) - EOR and Late Field Life Management

2014· article· en· W2020987554 on OpenAlexaff
T. M. Snow, Ian McKay, Drew Irwin

Bibliographic record

VenueInternational Petroleum Technology Conference · 2014
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsImperial Oil (Canada)
FundersBHP Billiton
KeywordsPetroleum engineeringNatural gas fieldEnvironmental scienceEnhanced oil recoveryResidual oilNatural gasFossil fuelBoiler blowdownGeologyWaste managementEngineering

Abstract

fetched live from OpenAlex

Abstract Bream Field, in Australia's Gippsland Basin, was originally developed in 1987 as a thin oil column development with gas cap re-injection. A satellite platform was installed in 1996 to capture resource not reachable from the original platform. After achieving 68% recovery, oil rates had fallen to the point that gas cap blowdown was commenced. Given the extremely strong natural pressure support seen in the Gippsland basin, a controlled blowdown was carried out to maximize oil recovery during gas export. As the water level approached the top of the structure, an assessment was carried out to determine the best use of the field infrastructure. The result of this effort was a decision to utilize the field for gas storage while simultaneously achieving enhanced recovery. By refilling the Bream reservoir with dry gas from the Longford gas plant during the summer months, high liquid yield fields are able to be produced consistently throughout the year. Additionally, Bream gas deliverability capacity is increased in the high gas demand periods. As the dry gas moves through the reservoir, it contacts both residual oil and rich gas, becoming re-saturated with gas liquids. When this gas is re-produced, it will yield more liquids, raising the overall recovery of the oil by approximately 1%. Re-injection into the Bream reservoir began in 2013 and is proceeding as planned. This paper will discuss the history of the Bream development, highlighting the analysis and planning that led to the recently implemented project. The management of this field demonstrates a number of techniques for maximizing hydrocarbon recovery over the life of a field, as well as considerations for maximizing economic value of the infrastructure. Background Bream Field, in the southwestern part of the Gippsland Basin (Australia), was originally discovered in 1969 and first developed in 1987. It is produced by the Gippsland Basin Joint Venture (between Esso Australia Resources Pty Ltd and BHPBilliton Petroleum (Bass Strait) Pty Ltd), with EARPL as the operator. GBJV offshore infrastructure includes 19 platforms, 4 subsea installations, and a network of pipelines feeding the Longford Gas Plant (see Figure 1). Approximately 98% of the production from the basin has come from the GBJV, since start-up in 1969. Bream Field and its development have been described in several publications, including:Shaw (Shaw, 1991) described the mechanical aspects of the initial horizontal drilling program;Titus (Titus, 1997) described the design and installation of a second satellite platform;McKerron et. al. (McKerron, 1998) described the development process and results from that second platform;Fish and Zajdlewicz (Fish, 2004) described the strategy for maximizing recovery during blowdown. The Bream development history, along with the forward plan described here, provide an interesting case study that demonstrates how available technology and the market demands drive development decisions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0180.002

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 head0.009
GPT teacher head0.267
Teacher spread0.257 · 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 source (direct Gemma or distilled Codex), 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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Citations0
Published2014
Admission routes1
Has abstractyes

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