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Record W1977663436 · doi:10.2118/2006-135

Sequencing Technologies to Maximize Recovery

2006· article· en· W1977663436 on OpenAlexaff
M. B. Dusseault

Bibliographic record

VenueCanadian International Petroleum Conference · 2006
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Abstract Maximizing recovery factors requires careful assessment of many candidate technologies applied together (hybrid or mixed) or in a series of extraction phases (sequenced). Two major issues arise in sequencing technologies: first, understanding the physics and screening criteria of the options; second, understanding and exploiting the changes that the reservoir has experienced during previous phases. Sequencing of extraction technologies takes advantage of improvements in transport properties caused by different technologies. CHOPS increases the reservoir permeability, porosity and compressibility, as well as favorably altering the stress fields and shortening the flow path. These major improvements mean that thermal or gravity methods will be far more effective if applied after CHOPS, rather than as a first extraction phase. High-pressure thermal approaches such as SF, CSS or HCS generate a great deal of reservoir dilation, as well as viscosity reduction (T) and breaching of shaley flow barriers. These effects mean that gravity methods such as IGI and VAPEX have greater chances of success and will achieve increased extraction efficiency if used after these high-pressure steam methods. Once interwell communication is established by an approach such as CSS, conversion to drive methods or combined drive and gravity drainage approaches is feasible. Also, massive dilation during cyclic injection phases means that recompaction drive mechanisms can be counted on to improve recovery rates and recovery factors. ISC (combustion) in long or short flow path configurations could have far greater chances of success if used as a final "stripping" technology once a reservoir has been dilated, heated, and depleted by other methods. A possible vertical well sequence is CHOPS _ CSS _ SF _ IGI, perhaps even terminating with a ISC phase. A horizontal well sequence of CP _ HCS _ SAGD _ IGI _ ISC may be feasible. At the very least, a phase of CHOPS, if it can be achieved, will improve reservoir transport properties for most other production technologies. Because CHOPS is limited to vertical wells, this leads to ideas involving combinations of vertical and horizontal well arrays, such as CHOPS and HCS executed simultaneously, followed by conversion to SAGD once communication is established, then IGI and perhaps ISC as stripper phases. In the presence of active water or gas zones, options are more limited because of channeling and coning, but there still remain sequencing possibilities for low Δp (gravity drainage) approaches exploiting reservoir changes. Planning for sequencing of production options from the beginning of a project will reduce costs in many ways, as well as prolonging the asset life and increasing the ultimate recovery factor. The impact on technically recoverable reserves estimates in heavy oil will be huge if sequencing is properly implemented. Recoverable reserves increases exceeding a trillion barrels are expected. Introduction The Viscous Oil Resource Since the early 1980's, a number of new production technologies has been introduced to the oil industry. Under the right set of reservoir conditions and fluid properties, each of these technologies can bring additional value by increasing the Recovery Factor (RF), reducing costs, or accelerating production rate.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.005

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 head0.199
GPT teacher head0.345
Teacher spread0.146 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
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

Citations10
Published2006
Admission routes1
Has abstractyes

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