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Record W2073029739 · doi:10.2118/09-03-52

CHOPS Without Sand

2009· article· en· W2073029739 on OpenAlexaboutno aff
Brian Wagg, Yunchao Fang, D. Birkett

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2009
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsProduction (economics)Oil sandsOil productionEnvironmental scienceProduct (mathematics)Process (computing)Waste managementPetroleum engineeringEngineeringComputer scienceGeography

Abstract

fetched live from OpenAlex

Abstract Heavy oil producers in Canada have adopted the primary production strategy of encouraging sand production in a process commonly know as Cold Heavy Oil Production with Sand (CHOPS). While this technique yields economic oil rates, the production of sand introduces many other operating costs and prevents the implementation of technologies, such as gathering lines, that are not compatible with massive sand production. A new concept has been proposed that takes advantage of the reservoir processes of CHOPS but removes most of the extra operating costs and barriers to technology associated with sand production. This paper discusses the new process, how it could benefit heavy oil production operations and the technical challenges that need to be addressed before this concept can be implemented. Introduction The Alberta Energy and Utilities Board reports that Alberta has approximately 260 million cubic metres of remaining established reserves of heavy oil that are accessible by primary production(1). Cold heavy oil production with sand (CHOPS) is currently one of the key production techniques being used to develop these reserves. Heavy oil producers in Western Canada have generally accepted the notion that non-thermal heavy oil production is not economically feasible without allowing, and often promoting, sand production. This operating strategy led to operators producing as much as 500,000 m3 of sand per year(2). Handling and disposal costs for this by-product of oil production normally exceeds C$100/m3. In addition, workovers on producing wells due to sand accumulation in the wellbore and downhole pumps and increased wear due to the presence of sand in the produced fluids routinely accounts for over 25% of heavy oil operating costs (based on information provided by Pengrowth Corporation). Produced sand generally requires special handling facilities, separation equipment and some method of ultimate disposal. Many operators currently dispose of the produced sand in salt caverns. Some of these caverns were initially used for liquid and gaseous hydrocarbon storage, while more recently, some operators and service companies have constructed caverns expressly for produced sand disposal. The current production technique of CHOPS, however, requires the sand to be pumped to the surface, separated in surface tanks, trucked to a central facility, stockpiled and finally injected into the salt cavern. This process is expensive and increases the risks related to safety and environmental mishaps due to increased handling and transportation of sand over potentially long distances and prolonged surface storage. The present method of producing heavy oil uses lease storage tanks for collecting produced fluids from either single or small groups of wells. In most cases, these lease tanks are open to the atmosphere. While significant quantities of solution gas, principally methane, are produced, this gas is generally allowed to vent to the atmosphere. Measurements of the gas vented from heavy oil wells in Western Canada by New Paradigm Engineering(4) indicate that the volume of gas produced is likely on the order of 700 m3/day per well. Reducing this volume of vented 'greenhouse gas' has become one of the more prominent mandates of the oil industry and governments as a whole.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.757
Threshold uncertainty score0.396

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
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 head0.004
GPT teacher head0.192
Teacher spread0.188 · 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 designNot applicable
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

Citations2
Published2009
Admission routes1
Has abstractyes

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