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Record W2060157876 · doi:10.2118/162792-ms

Factors That Impact the Performance of Resin Coated Proppant in Low Temperature Reservoirs

2012· article· en· W2060157876 on OpenAlexaboutno aff
Lyle H. Burke, Dana Roney, Tyler Elgar, Andrea Hersey

Bibliographic record

VenueSPE Canadian Unconventional Resources Conference · 2012
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsPetroleum engineeringGeologyTight oilFracture (geology)Closure (psychology)CarbonateFossil fuelCompletion (oil and gas wells)Oil in placeEnvironmental scienceGeotechnical engineeringPetroleumMaterials scienceEngineeringWaste managementPaleontology

Abstract

fetched live from OpenAlex

Abstract Pumping a tail stage of resin coat proppant (RCP) is well documented method to control proppant flowback in a wide variety of oil and gas wells. The performance of RCP can be impacted by reservoir temperature and closure stress, as well as fluid and fracture placement parameters. RCP systems were originally designed for higher temperature applications although the use of lower temperature curable resins in shallow lower stress reservoirs has been discussed since at least mid-2005. 1,2,3 Strong oil prices and relatively weak gas prices have recently (2012) been driving the development of relatively shallow tight oil reservoirs within the Western Canadian Sedimentary Basin (WCSB) including the Slave Point, Cardium, Bakken, Viking and others. These reservoirs typically have relatively low reservoir temperatures and closure stresses which highlight the importance of looking beyond the reservoir temperature with a holistic evaluation of the fracture fluid interactions and fracture placement efficiency that can significantly impact RCP placement and performance; existing SPE publications also provide good background to the impact of these contributing factors.4,5 Most RCP literature focuses on clastic applications. In conjunction with our proppant supplier and pumping service partner, Lone Pine Resources Canada Ltd (LPR) has recently conducted laboratory testing designed to optimize the use of RCP within the Slave Point carbonate reservoir that LPR is successfully developing with multi-fractured horizontal wells (MFHZ). The Slave Point reservoir presents a challenge to RCP performance with a combination of a cool reservoir temperature of 40°C (100°F) and low closure pressures of roughly 20 MPa (2,900 psi). During Q1/Q2 2012 LPR identified a proppant flowback that was inhibiting production and increasing workover expenditures. Study of the problem identified proppant mixing and a lack of RCP bonding as root cause issues. A holistic review of the LPR Slave Point fracture program resulted in significant changes to the fracture treatment design and execution including the RCP type, resin activation parameters and base fluid changes. LPR has been able to minimize proppant flowback to the point that bottomhole pump failures as a result of proppant production have been significantly reduced, if not eliminated, and no incremental proppant clean-out operations have been required since the implementation of an optimized fracturing program that includes higher viscosity fracturing fluid and an optimized RCP-LT and activator program. In addition, no measurable proppant flowback volumes have been recovered during initial CT clean-out from the last four July 2012 fractured wells. These are very positive indicators that the RCP-LT and activator changes, improved fracture fluid viscosity and proppant placement has solved the proppant inflow problem.

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)0.003
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 score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.027
GPT teacher head0.230
Teacher spread0.203 · 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

Citations4
Published2012
Admission routes1
Has abstractyes

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