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Record W2974791547 · doi:10.2118/195982-ms

The Influence of Rock Composition and pH on Reservoir Wettability for Low Salinity Water-CO2 EOR Applications in Brazilian Reservoirs

2019· article· en· W2974791547 on OpenAlexaff
Alana Almeida da Costa, Philip Jaeger, J. A. O. Santos, João B. P. Soares, Japan Trivedi, Marcelo Embiruçu, Gloria Meyberg

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2019
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSalinityBrineEnhanced oil recoveryWettingPetroleum engineeringSurface tensionWater injection (oil production)Contact angleFormation waterGeologyEnvironmental scienceMineralogyChemistryMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Abstract Low salinity waterflooding and CO2 injection are enhanced oil recovery (EOR) methods that are currently growing at a substantial rate worldwide. Linking these two EOR methods appears to be a promising approach in mature fields and for the exploration of post- and pre-salt basins in Brazil. Moreover, the latter reservoirs already have high CO2 content in the gas phase. Interfacial phenomena between fluids and rock in low salinity brine/CO2 environment still remain unclear, particularly the wettability behavior induced by the pH of the medium. In this study, coreflooding experiments, zeta potential, contact angle, interfacial tension (IFT), and pH measurements at ambient and reservoir conditions were performed to investigate the influence of the rock composition and brine/CO2 mixtures at different pH values for low salinity water-CO2 EOR (LSW-CO2 EOR) applications in Brazilian reservoirs. Brazilian light crude oil, pure CO2, and different brine solutions were used to represent the fluids in actual oil reservoirs. The experiments were carried out on Botucatu sandstone samples, with mineralogy determined by energy dispersive X-ray analysis. Coreflooding experiments were conducted by injection of 10 pore volumes of high salinity water followed by low salinity water. Contact angles, IFT and pH measurements at atmospheric and elevated pressures were performed in a high-pressure view cell (Pmax = 10,000 psi, Tmax = 180 °C) by different methods. The contact angle results were compared to those of earlier publications for other rock types. Increased oil recovery was observed in the coreflooding experiments during LSW injection. In addition, the effluent pH during LSW injection increased 0.7-4.3 points more than initial pH in high salinity water injection. Zeta potential measurements confirmed expansion in the water film on Botucatu sandstone surface at low salt concentrations. These observations indicate that during LSW injection solely, an increase in pH would increase water wettability of Botucatu sandstone, as all edges and faces of its surface become negatively charged and may repel polar compounds in crude oil. On the other hand, contact angle experiments reveal that water wettability is further enhanced in LSW when CO2 is dissolved in the water, and the system changes to acidic conditions. It seems that a change in the medium pH enhances interactions with water molecules for which the respective interfacial energy decreases, and hence the contact angle as well. Therefore, low salinity brine/CO2 mixtures may synergistically lead to increased oil recovery by decreasing the contact angle. This study advances the understanding of interfacial properties and wettability behavior in low salinity brine/CO2 environment, facilitating the design of LSW-CO2 EOR applications in Brazilian fields. Moreover, the study provides useful information for oil companies that have acquired mature wells and exploration blocks in Brazil, supporting them in operational and economic decisions.

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)0.001
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 score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.010
GPT teacher head0.261
Teacher spread0.251 · 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".

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Citations14
Published2019
Admission routes1
Has abstractyes

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