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Record W4306153843 · doi:10.2118/210657-ms

A Lab-to-Field Approach and Evaluation of Low-Salinity Waterflooding Process for High-Temperature High-Pressure Carbonate Reservoirs

2022· article· en· W4306153843 on OpenAlexaff
Hemanta Sarma, Navpreet Singh, Ahmed Fatih Belhaj, Adarsh Kumar Jain, Giridhar Gopal, Vivek Srivastava

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsImbibitionBrinePetroleum engineeringWettingSalinityEnhanced oil recoveryCarbonateGeologyOil fieldGeobiologyMineralogyMaterials scienceChemistryGeotechnical engineeringHydrogeologyComposite materialOrganic chemistryMetamorphic petrologyRegional geology

Abstract

fetched live from OpenAlex

Abstract Low-salinity waterflooding (LSWF) process has gained great attention over the years as a promising enhanced oil recovery (EOR) method with its superior performance over high-salinity water waterflooding. This study presents a rigorous and systematic lab-to-field approach involving research, discovery and validation using experimental and simulation components. Impact of various ionic compositions on LSWF was determined including a fundamental understanding of water geochemistry and likely geochemical reactions. The roles of crude oil/brine/rock (COBR) interactions and resulting rock-surface charges were investigated as well. Both experimental and simulation components were treated as complementary to each other. Experimental components included: reservoir-condition high-pressure high-temperature (HPHT) displacement tests in composite cores using brines of different salinities and specially-designed ionic compositions; investigation of wettability alteration - presumably a key LSWF mechanism - in a unique and specifically-designed HPHT imbibition cell; Zeta potentiometric studies were conducted using a Zeta potentiometer capable of more representative evaluation in brine-saturated whole cores rather than with pulverized samples. Simulation involved: proposing likely geochemical reactions during LSWF; incorporating oil/brine/rock interactions, and then, simulation studies linking laboratory data to data from the candidate reservoir on complementary basis. The findings of the coreflooding experiments proved conclusively that LSWF with certain specific ionic composition yield a higher oil recovery. HPHT imbibition tests yielded both visual and quantitative estimations and monitoring of how the wettability alteration took place during LSWF and how it was impacted by the degree and magnitude of both temperature and pressure as the vivid variations in the contact angles were clearly captured. Using a whole reservoir core rather than pulverized samples, Zeta potentiometric studies enabled an investigation of the charging behavior at the rock-water interface at various salinities. A new method to estimate Zeta potential in high-salinity environment was developed and validated, and it conclusively proved that rock-surface charge played a vital, if not a more dominant role, in the LSWF process. The simulation studies included incorporation of experimental data generated during the study, identification of a set of likely geochemical reactions during the process and complementary field data to study the LSWF performance under various conditions and constraints. A conceptual "lab-to-field" approach that can contribute to designing a more efficient LSWF process with optimized ionic chemistry has been proposed based on results and analysis from this study.

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 score0.003

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 head0.018
GPT teacher head0.270
Teacher spread0.252 · 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

Citations15
Published2022
Admission routes1
Has abstractyes

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