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Record W2030165799 · doi:10.2118/168116-ms

Application of In-House Prepared Nanoparticles as Filtration Control Additive to Reduce Formation Damage

2014· article· en· W2030165799 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSPE International Symposium and Exhibition on Formation Damage Control · 2014
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFiltration (mathematics)GraphiteNanoparticleMaterials sciencePorosityCeramicChemical engineeringPorous mediumPermeability (electromagnetism)Composite materialNanotechnologyChemistryMembrane

Abstract

fetched live from OpenAlex

Abstract Nanoparticles (NPs) are currently being studied as a drilling fluid additives especially for application in very low-permeability formations such as shales. Application for conventional permeable rocks is still a subject of discussion. In this work, successful application of in-house prepared iron-based nanoparticles (NP1) and calcium-based nanoparticles (NP2) to reduce filtration loss in conventional permeable media has been experimentally quantified for oil-based mud (OBM) utilizing the high-pressure high-temperature (HPHT) filter press at 500 psi and 250°F. Ceramic discs were used as the filtration medium in this application to test the performance of the NPs and glide graphite as a conventional lost circulation material (LCM) for porous media. These experiments were carried out in the presence of graphite at low and high concentrations. Filtration reduction trends were observed and a reduction up to 76% was achieved. API filter press was also used to investigate the behavior of NPs and graphite under low pressure and temperature conditions (LPLT). NP1 and NP2 at the two graphite concentrations showed a reduction up to 100%. NP1 gave higher reduction especially at low concentrations under HPHT conditions, while NP2 yielded significant reduction at high concentration under HPHT. These trends were reversed under LPLT, giving a new insight on NPs performance under different pressure and temperature conditions. At HPHT and LPLT, the effect of graphite as a filtrate reduction agent is less significant as the NPs concentration increases. High graphite level had a positive effect on filtration reduction in combination with NP1 at HPHT and LPLT. This was not the case for the blends containing NP2 at HPHT. The effect of NPs and graphite was tested individually showing a different performance compared to the combination of them. Impact of NPs and graphite on rheology was also quantified allowing identification of the more sensitive parameters in the blends. It is concluded from this study that blends containing NPs and graphite can be successfully implemented in OBM to minimize formation damage in porous media.

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.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.471
Threshold uncertainty score0.753

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.004
GPT teacher head0.207
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