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Record W4323306665 · doi:10.2118/211461-pa

Evaluation of ATBS Polymers for Mangala Polymer Flood

2023· article· en· W4323306665 on OpenAlexaff
V. Gouri Shankar, Robert Zagitov, Sunit Shekhar, Abhishek Kumar Gupta, M. Suresh Kumar, Ritesh Kumar, Santhosh Veerbhadrappa, Petro Nakutnyy

Bibliographic record

VenueSPE Reservoir Evaluation & Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsSaskatchewan Research Council (Canada)
Fundersnot available
KeywordsPolymerPolyacrylamidePolymer degradationChemical engineeringHydrolysisRheologyChemistryEnvironmental scienceMaterials scienceComposite materialOrganic chemistryPolymer chemistry

Abstract

fetched live from OpenAlex

Summary Mangala field has been under polymer flood since 2015. The polymer flood has been more successful in accelerating recovery compared to waterflood. As the flood matured, field performance indicated that part of the injected polymer was degrading in the reservoir. Laboratory studies and polymer samples collected from the reservoir suggest that the most likely reason for the degradation is increased hydrolysis due to thermal aging. This degradation compels higher dosing of polymer to make up for the lost viscosity and increases operating costs. Polymer precipitation in the reservoir may also lead to loss of reservoir permeability. Literature surveys and preliminary laboratory studies showed that polymers with acrylamide-tertiary-butyl-sulfonic acid monomer units (referred to as ATBS polymers) could be a suitable option for Mangala. To evaluate the hypothesis, the team did a series of laboratory and coreflood studies. The studies include accelerated thermal ageing, rheology, dynamic adsorption, injectivity, waterflood with fresh and degraded samples, and compatibility studies with topside chemicals. Two hydrolyzed polyacrylamide (HPAM) polymers with different degrees of hydrolysis (DOH) and two ATBS polymers were evaluated. The selected ATBS polymer was then tested for compatibility with surface topside chemicals. The studies show that the classic 20 to 25% DOH HPAM suffers viscosity degradation and possible precipitation in Mangala reservoir conditions. ATBS polymers and a lower DOH HPAM provide superior results to the incumbent HPAM with an acrylamide (AM) (86)-ATBS (14) copolymer providing the best results. ATBS polymers were especially resistant to cloudpoint lowering and provide some superiority in shear degradation. The ATBS monomer was resistant to hydrolysis during the period of testing. Contrary to the published literature, ATBS polymers showed higher adsorption and their propagation through cores required a higher pressure drop. ATBS polymer seemed to plug a low-permeability section of the core stack. All polymers reach their peak viscosity at 30 to 40% hydrolysis and decline sharply after 40%, but viscosity and cloudpoints measured during accelerated aging are possibly conservative. A large-scale pilot of ATBS injection in Mangala is under way to validate the laboratory test results. ATBS polymer can be a suitable polymer for some layers of Mangala with a high residence time and permeability. The choice is driven by the economics of the incremental cost of ATBS for the benefits it offers. In some sands with shorter interwell spacing, a lower DOH HPAM may be a more cost-effective solution. The study results in this paper provide insights to operators to understand the reservoir performance of existing polymer floods and plan for future polymer floods.

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 head0.049
GPT teacher head0.317
Teacher spread0.268 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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