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Record W1983308070 · doi:10.2118/71080-ms

Design and Execution of a State-of-the-Art Water Shutoff Treatment in a Powder River Basin Tensleep Producer

2001· article· en· W1983308070 on OpenAlexaff
Leo A. Giangiacomo

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsPetroleum Technology Research Centre
Fundersnot available
KeywordsSchedulePetroleum engineeringEnvironmental scienceAquiferWater treatmentPetroleumComputer scienceEngineeringGeologyEnvironmental engineeringGroundwaterGeotechnical engineering

Abstract

fetched live from OpenAlex

Abstract This paper summarizes the literature research on water shutoff treatment technologies, distills the critical elements for designing a treatment in a naturally fractured Tensleep producer, presents the treatment design process, and shares the field operations experiences and treatment results. The subject well is located in the Teapot Dome Field in the Naval Petroleum Reserve No. 3, Natrona County, Wyoming. The project was funded by the U.S. Department of Energy and the Rocky Mountain Oilfield Testing Center. The literature and interview research illuminated the aspects of the treatment that are scientifically sound, as well as the areas that are in need of additional research. The most important factor in treating wells for excessive water production is the characterization of the water production mechanism. This paper outlines some simple graphical techniques used to identify water flowing through a natural fracture system from an underlying aquifer. Selection of treatment technology, treatment volume, concentration schedule, and quality control issues are discussed. Critical logistics and operational matters are also covered. An unusual approach to the treatment was taken by stimulating the well with a propellant stimulation treatment prior to the water shut-off treatment. This technique is designed to improve the communication with the natural fractures in the near-wellbore area, and allow the treatment to be pumped at lower pressures with less polymer dehydration problems and more effective treatment placement. It is hoped that the improved placement will extend the life of the treatment. The job procedure, cost estimate, schedule, and project economics are presented, and compared to the actual job execution. The treatment had some unexpected pressure behavior during the placement. Ideas are proposed to explain the behavior, and suggestions are made to gather additional data during future treatments to verify the theories and better understand treatment design. The treatment was performed on October 13, 2000 and three months of production data are used to judge its success. Pre-treatment production rate was 14.1 barrels of oil per day (BOPD) (2.24 m3/d) and 7,817 barrels of water per day (BWPD) (1243 m3/d). Post treatment rates after two weeks were 18 BOPD (2.9 m3/d) and 1,880 BWPD (299 m3/d). After three months, production seems to be stabilizing around 12 BOPD (1.9 m3/d) and 3,150 BWPD (496 m3/d). Problems were experienced with ESP pump sizing following the treatment.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.001

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 head0.010
GPT teacher head0.203
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2001
Admission routes1
Has abstractyes

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