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Record W1979582799 · doi:10.2523/iptc-17675-ms

Improving Gas Relative Permeability in Tight Gas Formations by Using Microemulsions

2014· article· en· W1979582799 on OpenAlexaff
Ameneh Rostami, D. T. Nguyen, H. A. Nasr El Din

Bibliographic record

VenueInternational Petroleum Technology Conference · 2014
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsNalco (Canada)
FundersCrisman Institute for Petroleum Research, Texas A and M UniversityTexas A and M University
KeywordsMicroemulsionPetroleum engineeringRelative permeabilitySurface tensionPulmonary surfactantPermeability (electromagnetism)Capillary pressureCapillary actionAdsorptionTight gasChemical engineeringFracturing fluidChemistryHydraulic fracturingEnhanced oil recoveryMaterials scienceGeologyComposite materialPorous mediumThermodynamicsOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract One of the challenges in slickwater fracturing of tight sand gas reservoirs is post-treatment fluid recovery. More than 60% of the injected fluid remains in the critical near wellbore area and has a significant negative impact on the relative permeability to gas and well productivity. The trapped water could be due to capillary forces around the vicinity of the fractured formation. For strongly water-wet tight gas reservoirs, capillary forces promote the retention of injected fluids in pore spaces. Commonly available surfactants are added to slickwater to reduce surface tension between the treating fluids and gas. The problem with surfactants is that upon exposure to the formation, they adsorb on the surface of the rock. The addition of microemulsion to the fracturing fluid can result in lowering the pressure needed to displace injected fluids and/or condensate from low permeability core samples. This alteration of the fracturing fluid effectively lowers the capillary forces in low permeability reservoirs. This will result in removal of water and condensate blocks, the mitigation of phase trapping, and therefore an increase in permeability to gas. This paper examines the effectiveness of microemulsions in the improvement of fracturing fluid recovery. Coreflood runs using 20 in. Bandera sandstone cores with residual condensate and water showed that the percentage of permeability regained due to treatment with microemulsion solutions was up to 150% depending on type of microemulsion. An environment-friendly microemulsion formulated with a blend of a novel anionic surfactant, nonionic surfactant, short chain alcohol and water showed very good results in lowering interfacial tension between water and oil, when compared with competitive technologies. The performance of this microemulsion was excellent in high salinity fluid as well as low salinity fluid. It was excellent for solubilizing liquid condensates which can be found in wet gas wells. Contact angle of 63.45 degrees makes this microemulsion an optimal solution for cleanup of the near wellbore area. The resulting capillary pressure for a frac fluid treated with 0.25 wt% of this chemical in 2 wt% KCl is nearly 300 times lower than untreated fluid and 30 times lower than a fluid treated with competitive technologies. Introduction Condensate-banking has become an important source of damage and reducing the well productivity. The effective permeability to gas reduces dramatically as a result of accumulated condensate near the wellbore and subsequently decreases the productivity of the well. In gas reservoirs, the use of water-based fluid creates fluid retention problems and becomes more pronounced, as the combination introduces an additional phase to the reservoir, including an additional reduction in the effective permeability to the gas phase (Franco et al. 2013). Large quantities of fluid that has been trapped in the near wellbore area in the reservoir and in the case of fracturing, the fluid that have been trapped in the area surrounding the fracture and within the fracture itself, have detrimental effects on the relative permeability, the effective flow area, and effective fracture length, and impairs well productivity (Penny et al. 2005).

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)0.000
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 score0.422
Threshold uncertainty score0.662

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.224
Teacher spread0.217 · 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 designSimulation or modeling
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

Citations3
Published2014
Admission routes1
Has abstractyes

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