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Record W1990779003 · doi:10.2118/00-04-tn

Water Shut-off Treatments-Reduce Water and Accelerate Oil Production

2000· article· en· W1990779003 on OpenAlexaboutno aff
F.B. Thomas, D.B. Bennion, G.E. Anderson, B.T. Meldrum, W.J. Heaven

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2000
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsWater cutPetroleum engineeringMicroscale chemistryProduced waterProduction (economics)Oil productionEnvironmental scienceOil fieldGeology

Abstract

fetched live from OpenAlex

Abstract Gel treatment applications have been used for production well WOR reduction. There have been a number of cases where conformance was very poor and, by invocation of gel treatment strategies, WOR was significantly reduced. This paper discusses both the characteristics of reservoirs and wells which result in high WOR, as well as characteristics of gel treatments which need to be designed in order to effectively minimize the produced water from a reservoir or an oil field. Examples are provided in this paper whereby, through the use of gel treatments in production wells, WOR and oil production was increased. The paper concludes that there is a significant upside to gel treatments for reservoir optimization and production well revitalization. Introduction A serious problem in oil-producing reservoirs is water production. As with most things in nature, fluids also tend to follow paths of least resistance which, in reservoirs, are often created by the heterogeneous nature of the rock. There are two levels to this heterogeneity. The first is microscale heterogeneity which could be represented as a simple porous feature distribution, and the second is macroscale heterogeneity which includes layering, natural or induced fractures, and high vertical and horizontal permeabilities. Both can lead to poor conformance and, therefore, need to be controlled. If conduits for water flow are available then they need to be blocked in order for production wells to continue operation. In terms of water disposal costs, approximately $1 billion is spent in Alberta alone each year. The macroscale heterogeneities are more commonly understood and more intuitive. It is commonly known that, in some instances where fracturing operations have been misapplied or resulted in unfortunate connections to bottom water sources, the fracture permeability (100 to 1,000 times greater than the permeability of the rest of the rock) has resulted in very quick water breakthrough and very low recovery of the hydrocarbons in the reservoir. A similar response can also be observed where high permeability layers are present in certain porous media. Nevertheless, their effect is that much of the rock remains unswept. Another form of macroscale heterogeneity, which contributes to very poor conformance is the case where poor cementing operations are present. In such cases, in order to produce anything from the well, near wellbore fluid profile modification must occur. The same applies for injection wells. For microscale conformance difficulties, often simple laboratory tests can identify problems associated with exploitation strategies. For instance, the recovery efficiency associated with a waterflood is often based on analogous reservoirs or past experience. In some cases, subtle changes in the structure of the rock can result in vast changes in the sweep associated with the flow unit even though the porosity remains about the same value. Many examples exist in the literature where permeability and porosity of reservoirs have been sufficiently high to motivate operating companies to full developmental strategies only to find out that, upon implementation, the sweep through the homogeneous flow unit is much less than the average literature numbers would have indicated.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.733
Threshold uncertainty score0.546

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
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 head0.006
GPT teacher head0.198
Teacher spread0.191 · 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 designBench or experimental
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

Citations38
Published2000
Admission routes1
Has abstractyes

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