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Record W2895851436 · doi:10.2118/192330-ms

Smart Water Flooding: An Economic Evaluation and Optimization

2018· article· en· W2895851436 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAll Days · 2018
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsFlooding (psychology)Environmental scienceComputer scienceWater resource management

Abstract

fetched live from OpenAlex

Abstract Smart water (or low salinity water) flooding has been an emerging technology in the petroleum industry since last two decades. Low capital cost and operating expenses of this flooding make it attractive for the petroleum industry. This paper examines the economic feasibility of the injection of smart water and compares with other conventional water flooding techniques. Optimization has also been done with different dilution schemes through particle swarm optimization. This study analyzes the effect of smart water and sequential dilution of injected sea water through reservoir modeling. A three-dimensional black oil reservoir model is developed by using ECLIPSE 100. In addition, this study presents the economic feasibility of the injection of smart water and compares with other conventional water flooding techniques. The study is divided into four cases: i) oil is produced without water flooding, ii) formation water is injected in the reservoir, iii) sea water is injected in the reservoir, and iv) water injection is taken place by sequential dilution of high salinity water. In each case, economic evaluation is completed by calculating the costs and revenues generated by water injection, and oil prod uction. The results show that sea water injection did not give additional oil recovery compared to formation water injection for our case. However, additional results show that sequential dilution flood recovers more oil than sea water and formation water injection. Moreover, five main parameters are optimized such as number of cycles of different salinities, duration of various cycles, salinity values for different cycles, injection rate and production rate. Optimization results show even better results than sequential dilution. The optimization also shows that the additional oil recovery is achieved when the dilution sequence is altered. This outcome illustrates that increased oil recovery is not only dependent on step wise reduction of sea water salinity but also with the variation of dilution pattern. This paper presents a novel technique for the reservoir engineers to study smart water flooding with different perspective. Sequential dilution has been an acceptable technique for increasing oil recovery. However, change of the dilution pattern could be a good alternative and thus provides a cost-effective technique as compared to sequential dilution.

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.

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 score0.076
Threshold uncertainty score0.271

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.018
GPT teacher head0.260
Teacher spread0.242 · 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