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Record W1993403473 · doi:10.2118/0605-0070-jpt

Methane Pressure-Cycling Process for Thin Heavy-Oil Reservoirs

2005· article· en· W1993403473 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
KeywordsMethaneOverburdenPetroleum engineeringEnvironmental scienceFossil fuelGeologyUnconventional oilOil productionEnhanced oil recoveryCyclingMining engineeringWaste managementEngineeringChemistryArchaeology

Abstract

fetched live from OpenAlex

This article, written by Assistant Technology Editor Karen Bybee, contains highlights of paper SPE 88500, "Methane Pressure-Cycling Process With Horizontal Wells for Thin Heavy-Oil Reservoirs," by Mingzhe Dong, SPE, PTRC, and Sam Huang, SPE, and Keith Hutchence, SRC, prepared for the 2004 SPE Asia Pacific Oil and Gas Conference and Exhibition, Perth, Australia, 18-20 October. The methane pressure-cycling (MPC) process is an enhanced-oil-recovery (EOR) method used in some heavy-oil reservoirs after termination of primary or waterflood production. The object of the process is restoration of the solution-gas-drive mechanism. This restoration is accomplished by reinjecting an amount of solution gas, mainly methane, and then repressuring the gas into solution by injecting water until the original reservoir pressure is reached. This recreates the primary-production conditions. This recovery technique targets the large portion of heavy oil in thin reservoirs. Introduction Heavy oil in reservoirs thicker than 10 m commonly is produced by use of thermal recovery methods. These methods generally are not suitable for thin reservoirs because of heat losses to the overburden, underburden, and bottomwater zones. Saskatchewan accounts for 62% of Canada’s total heavy-oil resource including 1.7×109 m3 of proven and 3.7×109 m3 of probable reserves. Of the province’s proven initial heavy oil in place, 97% is contained in reservoirs with pay zones less than 10 m thick and 55% is in reservoirs with pay zones less than 5 m thick. Primary and secondary methods recover only approximately 7% of the proven initial oil in place (IOIP). The incentive is strong for the development of EOR techniques that will maximize the recovery of these thin heavy-oil reservoirs. The MPC process is an EOR method intended for application after termination of primary or waterflood production in some thin reservoirs. The objective of the pressure-cycling process is restoration of the solution-gas-drive mechanism that provided primary production. Restoration is accomplished by reinjecting an amount of solution gas and then repressuring the gas back into solution by injecting water until original reservoir pressure is reached. This recreates the primary-production conditions. The process uses infill horizontal production wells located between the existing vertical wells that are used for methane and water injection to pressure up the depleted reservoir. This pressure cycling is repeated until additional economic recovery is no longer feasible. The Saskatchewan Research Council/Petroleum-Technology Research Center has been investigating the pressure-cycling process for the past few years. Experimental Pressure/Volume/Temperature (PVT) Studies. PVT studies were conducted for oil samples collected from four Saskatchewan heavy-oil reservoirs (Senlac, Golden Lake, Plover Lake, and Cactus Lake North) in contact with methane. The purpose of these studies was to investigate the PVT properties of heavy-oil/methane mixtures and to provide input data on the PVT behavior of live oils for numerical simulations. Heavy-Oil Coreflood Studies. The heavy-oil coreflood apparatus consists of a 30.5-cm triaxial-stress core holder, a Ruska dual-cylinder positive-displacement pump, an injection system, a production system, and support units. A backpressure regulator installed at the production end maintained the operating pressure. Oil production was determined on a mass basis.

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 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 score0.166
Threshold uncertainty score0.670

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 integrity0.0000.001
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 head0.019
GPT teacher head0.303
Teacher spread0.285 · 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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