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Record W4220882835 · doi:10.2118/200144-ms

Field Application of an Associative Polymer Reveals Excellent Polymer Injectivity

2022· article· en· W4220882835 on OpenAlexaff
Hommer Herbert, Roland Reichenbach‐Klinke, Giesbrecht Russell, Prapas Lohateeraparp, George Herman, Mai Kahnery

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsBASF (Canada)
Fundersnot available
KeywordsPolyacrylamidePolymerBrineRheologyOil fieldEnhanced oil recoveryMaterials scienceAdsorptionChemical engineeringSalinitySmart polymerProcess engineeringComputer scienceChemistryOrganic chemistryComposite materialPetroleum engineeringPolymer chemistryGeology

Abstract

fetched live from OpenAlex

Abstract Chemical EOR flooding using hydrolyzed polyacrylamide (HPAM) is considered nowadays a state-of-the-art tertiary recovery process and has been conventionally applied on a full-field scale worldwide. The addition of these standard polymers improves the mobility of the injected fluid and thus maximize sweep; however, application is only limited to mild reservoir temperatures and low brine salinity ranges. Therefore, a more thermally stable and more resistant "associative polymers" were derived, by incorporating specific hydrophobic groups into the HPAM polymer backbone, to offer performance advantages with regards to viscosifying efficiency and salt tolerance when compared to the standard HPAM. However, only a handful of field cases were reported in the literature. Thus, this paper will present the unique application of this associative polymer technology in a field pilot for one of the major E&P companies and discusses the corresponding lab evaluations leading up to the field trial. To confirm the advantages of using associative polymer over of standard HPAM, rheology and filterability measurements were conducted. Moreover, linear coreflood experiments in presence of oil have been performed at target field conditions (low temperature and higher salinity) with various polymer concentrations. The resistance factors measured in the coreflood experiments indicated that 750 and 1,250 ppm of associative polymer and HPAM, respectively, are adequate to deliver the required mobility ratio of 1 and accordingly the oil recovery can be similar for the two different polymers at these concentrations. Moreover, dynamic adsorption measurements conducted at the same polymer concentration reveal a smooth propagation of the associative polymer through the porous medium. Based on these findings, it is concluded that the associative polymer offers a significant performance advantage over the HPAM due to the lower polymer dose required to achieve the target performance. After successful lab evaluations and in preparation for a multi-well pilot, a field injectivity trial was planned accordingly to test the propagation of the synthesized polymer in the reservoir. Subsequently, the selected associative polymer was successfully injected into the reservoir over a period of two months in two injectors at a steady injection rate of 50 and 300 m3/d. The measured well head pressures of the two injection wells was stable for the entire test duration, indicating a good polymer injectivity with no observed formation plugging. This newly developed associative polymer was proposed to the field's operator as a promising alternative solution to unlock additional reserves increase oil recovery and a full-field polymer flood expansion is planned next. To our knowledge, this is one of the few reported field trials with associative polymers and should facilitate field implementation of this technology.

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 score0.005

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 head0.005
GPT teacher head0.232
Teacher spread0.227 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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