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Record W2159961906 · doi:10.2118/169549-ms

Increased Oil Recovery by Permeability Modification in High Permeability Contrast Slim Tubes

2014· article· en· W2159961906 on OpenAlexaff
Scott C. Jackson, John Fisher, Robert D. Fallon, Joseph Norvell, Edwin R. Hendrickson, Abigail Luckring, Ben D'achille

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsDuPont (Canada)
Fundersnot available
KeywordsPermeability (electromagnetism)Petroleum engineeringMaterials sciencePressure dropMicrobial enhanced oil recoveryRelative permeabilityEnvironmental scienceGeologyComposite materialMechanicsChemistryPorosityPhysicsMicroorganismMembrane

Abstract

fetched live from OpenAlex

Abstract Biofilm and biopolymers produced from microbes have been shown to reduce the permeability of high perm streaks which can result in improved sweep efficiency. The rate of permeability modification is very reproducible but can vary depending on the specific treatments used. Our approach has been to inoculate the reservoir with a microbe that under the optimal nutrient conditions will express a biopolymer as a film, reduce the size of pore throats and reduce the apparent permeability. The microbe and the nutrients are tailored to the conditions of each reservoir thus giving MEOR the greatest chance for success. In this paper we describe the use of a high permeability contrast composite slim tube to demonstrate increased oil recovery using microbes that generate biofilms to reduce the permeability contrast and thus improve sweep efficiency. In this setup, three hydraulically constrained slim tubes – working as very long sand packs – were constructed from different sources of sand with a range of particle size distributions. Absolute and differential pressure transducers were used to monitor the pressure drop across individual slim tubes and across the composite configuration of the three slim tubes. Oil traps were located at the exit of each slim tube to measure the oil production from each tube. The permeabilities of the slim tubes against a high salt injection brine (TDS ∼ 85,000 ppm) were measured to be 45, 4.5 and 2.6 Darcy. This presented a challenging system with a slim tube at a very high permeability that would need to be reduced by the MEOR treatment. Oil (viscosity of ∼58 cp) was pumped independently into each slim tube to assure that each slim tube was at residual water saturation. The slim tubes were allowed to age before the start of the test. Once working in composite mode the flows of the injected brine and MEOR treatments were dictated only by the hydraulics of the composite slim tube. The produced fluids from all three slim tubes were sent to a single back pressure regulator. In the composite mode, oil was readily produced only from the high permeability slim tube during the initial (non-MEOR) flooding sequence. A salt tolerant microbe present in the target oil reservoir capable of producing biofilms and altering the permeability was inoculated into the composite slim tube. The inoculated microbes were batch fed periodically using a protocol that was scaled down from one that would be used in a field test. An increase in the pressure drop and corresponding increase in oil production from the lower permeable slim tubes was observed as a result of the treatment. This resulted in a dramatic increase in the recovery factor from the composite slim tube. This work is a continuation of tests described in earlier papers (SPE129657, SPE146483 and SPE159128 – references 4, 5 and 6).

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)0.000
metaresearch head score (Gemma)0.000
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.208
Teacher spread0.199 · 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

Citations5
Published2014
Admission routes1
Has abstractyes

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