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Record W2906374857 · doi:10.2118/0119-0025-jpt

Reservoir-on-a-Chip Technology Opens a New Window Into Oilfield Chemistry

2018· article· en· W2906374857 on OpenAlexaboutno aff
Trent Jacobs

Bibliographic record

VenueJournal of Petroleum Technology · 2018
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsPetroleum engineeringPetroleum industryMicrofluidicsOil shalePetroleumGeologyEngineeringNanotechnologyMaterials scienceEnvironmental engineeringWaste managementPaleontology

Abstract

fetched live from OpenAlex

With its ability to replicate little slivers of reservoir rocks, microfluidics is giving the oil and gas industry a new way to capture images of interactions between chemicals and hydrocarbons. The biggest advantage this emerging technology holds over more established research tools is that it can capture the images at all. “You actually see what it looks like when the oil, water, and chemistry all interact, and that means we are making direct measurements of the performance characteristics,” explained Stuart Kinnear, the chief executive officer of Interface Fluidics. The Edmonton, Alberta-based startup of just 3 years is generating increasing interest among operators and suppliers that want independent reviews of whether particular production-enhancing chemicals will work inside a reservoir as advertised. The work places it amid a small group of firms trying to take microfluidics from its native home in the biomedical sector and into the oil field. Interface Fluidics’ innovation is a type of microfluidic chip that is etched from a postage-stamp-sized strip of silicon and glass to form what it calls a “reservoir analogue.” Depending on the inputs, such as data obtained from rock samples or even industry literature, a blank chip can in a matter of a few hours be turned into a tiny duplicate of a porous sandstone from Alberta or the nanodarcy pathways of the Permian Basin’s Wolfcamp Shale. So far, more than 20 different types of formations have been replicated for projects involving heavy-oil extraction, hydraulic fracturing fluid analysis, and enhanced oil recovery (EOR) for shale. “We can also copy-and-paste to make a hundred or a thousand copies if you wanted to,” added Kinnear, highlighting another advantage that microfluidics offers over relying solely on analysis of real rock samples: repeatability. Thanks to the chip-making process, “you know the only thing you are changing run to run is the chemistry.” To simulate reservoir-like conditions, the chips are heated, pressurized, and loaded with samples of the crude and water from the look-alike’s actual field. Videos taken as chemicals are introduced reveal how the medley of fluids mobilize around the artificial pore structures and get oil flowing, or not. Kinnear described this element as the technology’s “killer app.” Because each chip is transparent on one side, a microscope-mounted camera can be used to document experiments with high resolution. Prized by first-adopters, the recordings explain what drives flow behavior, e.g., phase trapping, wettability modification, solids deposition, or emulsion. First Adopters See Wide Applicability For at least one chemical maker, that window into porosity has become a critical tool for validating its own emerging technology, a new line of nanosurfactants tailored for shale reservoirs. “The visual aspect is something we’ve never had before, and that really helps us understand what’s going on,” said Bill O’Neil, the research director at ChemTerra Innovation, the chemical subsidiary of Calgary-based service provider Trican Well Service. Its pilot with Interface Fluidics, details of which were published in March (SPE 189780), resulted in the company moving forward with a significantly larger study that is still underway.

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)0.001
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 score0.467
Threshold uncertainty score0.929

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.011
GPT teacher head0.270
Teacher spread0.258 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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