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Record W2012794251 · doi:10.2118/0105-0039-jpt

Rebirth of a Mature Carbonate Gas Pool

2005· article· en· W2012794251 on OpenAlexaboutno aff
Karen Bybee

Bibliographic record

VenueJournal of Petroleum Technology · 2005
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsWorkoverAbu dhabiFossil fuelProfitability indexConsolidation (business)Successor cardinalPetroleumEnvironmental scienceGeologyGeographyEngineeringBusinessPetroleum engineeringArchaeologyMathematicsWaste managementPaleontology

Abstract

fetched live from OpenAlex

This article, written by Assistant Technology Editor Karen Bybee, contains highlights of paper SPE 88773, "Rebirth of a Mature Carbonate Gas Pool," by K.C. Carr, SPE, and M. Fiori, BP Canada Energy Co., prepared for the 2004 Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, UAE, 10-13 October. Operation of a mature pool requires increasing cost control and infrastructure optimization and consolidation to maintain profitability. The decision to initiate this type of reservoir-management plan, sometimes described as “harvest,” is driven in part by reservoir-performance analysis and in part by commodity prices. The full-length paper presents the account of a 3.7-Tcf sour-gas pool in Alberta, Canada, and the technical impact of harvest on field operations, reservoir performance, and ultimate recovery. The combination of new production-decline analysis and a well workover with surprising results led to a complete change in reservoir interpretation. Introduction Management of mature pools is not a new challenge. In the Western Canadian basin, the surge of exploration and development activity in the 1950s and 1960s has left a legacy of very mature pools, with many of these 40- to 50-year-old pools still producing. Records show that of the 30,000 gas pools discovered in Alberta, 2,000 were discovered before 1970. In the aggregate, these pools represent 15 Tcf of remaining recoverable reserves. Each of these pools was at some point declared mature on the basis of the science of the day and treated as a harvest candidate (i.e., a pool with very limited upside where the best strategy is to control cost and produce what is left without further investment).

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score0.398

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
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 head0.006
GPT teacher head0.234
Teacher spread0.228 · 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
Published2005
Admission routes1
Has abstractyes

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