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Record W2160514170 · doi:10.2118/04-09-ge

Workover Strategies in CHOPS Wells

2004· article· en· W2160514170 on OpenAlexafffundabout
Maurice B. Dusseault, Kirby Hayes, Michael Kremer, Chris Wallin

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2004
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsHusky Energy (Canada)University of Waterloo
FundersUniversity of Waterloo
KeywordsWorkoverOil productionWork (physics)Petroleum engineeringPetroleumEngineeringPetroleum industryEnvironmental scienceGeologyEnvironmental engineeringMechanical engineering

Abstract

fetched live from OpenAlex

Abstract The most fruitful area for continued OPEX (Operating Expense) reductions in CHOPS (Cold Heavy Oil Production with Sand) operations now appears to be well workovers to solve mechanical and reservoir problems, and to improve production rates. Workovers (exclusive of water-blocking methods) were the subject of a one-day workshop organized by the Lloydminster and District Heavy Oil Section of the Petroleum Society in Lloydminster, March 15, 2000. Approximately 35 attendees participated in a wide-ranging discussion of various workover methods. Participants discussed well problems requiring workovers, and evaluated technologies for re-establishing production under various conditions. Attendees represented companies and agencies such as Petrovera, Husky, ExxonMobil, Anadarko, Nexen, the Alberta Research Council, and the Universities of Waterloo (Ontario) and Alberta (Edmonton). The full information package that was developed for the workshop may be accessed at the website http://www.lloydminsterheavyoil.com/completionscience.htm. Development and assessment of workover techniques is a "work-in-progress"; these charts and slides are designed only as a guide to technical advances and to workover assessment. We feel strongly that the time has come for a joint industry project on the economic assessment of various workover approaches to optimize workover planning. Introduction CHOPS (Cold Heavy Oil Production with Sand) has been successfully implemented in many Canadian heavy oil fields (Figure 1), ranging in viscosity from 300 to 55,000 cP. CHOPS involves massive continuous sand influx, from <0.5﹪/vol to as high as 10﹪/vol during the steady-state phase of oil production. This sand influx, and the necessity to maintain it to sustain economical oil production, generates an unusual set of operational demands and operators are gradually developing better production methods, waste disposal methods, and workover methods. A typical CHOPS well produces 5 - 25 m3/d (30 - 150 bbl/day) of oil, and, for a good well in viscous oil, perhaps as much as 500 - 800 tonnes of sand in a year. Most CHOPS wells now use progressing cavity pumps with surface drives, as these give good oil rates while handing large sand volumes and eliminating rod fall problems. Workovers are required during the life of a well to change r repair equipment, and to maintain or re-initiate sand and fluid influx. The basic operational goal in the petroleum industry is to produce maximum oil with minimum OPEX over an optimal time to maximize netback. In the period between 1990 - 1998, the heavy oil industry in the Lloydminster area reduced OPEX in CHOPS wells from about $75/m3 to $44/m3 ($12.00/bbl to $7.00/bbl). This was achieved in large part through conversion to progressing cavity pumps, but also because of many micro-engineering advances in handling and disposing of sand, in developing better workover equipment and methods, and in refining details of well design. FIGURE 1: Canadian heavy oil deposits. Available in Full Paper. Currently, over 100,000 m3/d (600,000 bbl/d) of heavy oil are produced in Canada using CHOPS technology, comprising over 20﹪ of total Canadian oil production. This production level could easily be doubled in 24 months if the upgrading capacity in North America were expanded accordingly.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.919
Threshold uncertainty score0.995

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 integrity0.0000.001
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 head0.007
GPT teacher head0.222
Teacher spread0.215 · 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

Citations2
Published2004
Admission routes3
Has abstractyes

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