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Record W4390059997 · doi:10.5419/bjpg2023-0012

APPLICATION OF WATER FLOODING AND WATER ALTERNATIVE GAS (WAG) FLOODING TECHNIQUES IN A CARBONATE RESERVOIR: INTEGRATION OF RESERVOIR AND PRODUCTION SYSTEMS FOR DECISION MAKING

2023· article· en· W4390059997 on OpenAlexfundno aff
João Carlos von Hohendorff Filho, I. R. S. Victorino, Alireza Bigdeli, Denis José Schiozer

Bibliographic record

VenueBrazilian Journal of Petroleum and Gas · 2023
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
FundersUniversidade Estadual de CampinasPetrobrasComputer Modelling GroupEnergi Simulation
KeywordsProduction (economics)Petroleum engineeringReservoir simulationBenchmark (surveying)Work (physics)Flooding (psychology)Function (biology)Environmental scienceComputer scienceEngineeringGeology

Abstract

fetched live from OpenAlex

The objective of this work is to evaluate the impact of integration between reservoir and production systems on the decision making for field production development. The authors demonstrated, in a benchmark case, the applicability of water injection (WI) and water alternating gas injection (WAG) techniques for various production systems by proposing a novel methodology. This work explores three optimization approaches: (1) based on the complete model considering integrated systems, (2) for production system based solely on reservoir model and followed by the integration and optimization of production system, and (3) derived from (2) considering subsequent integration and optimization for complete model. In the implementation step, production strategies are applied in a reference model. This work compares production strategies, reservoir performance forecast, and the net present value (NPV) objective function. The integrated models yeild similar objective-function values by utilizing a production system that does not alter the bottom-hole conditions significantly, thereby replicating the behavior observed in the non-integrated model. The results of non-integrated reservoir optimizations should be used with caution for decision-making purposes, as the subsequent integration may cause the changes to the the production forecasts. The differences in reservoir behaviors can be attributed to the changes in the dynamics (movement) of fluids from the reservoir to the wells and the type of recovery mechanism affected by well positioning. The implementation of production strategies in the reference model resulted in lower values of NPV (20% for WI and 60% for WAG) than those obtained in the optimization step. The findings demand caution in the application of closed-loop procedures to prevent biased or inaccurate assessments of decisions made solely based on reservoir models. The application of this work can be considered an import study for Carbon Capture Utilization and Storage (CCUS), as well as for energy transition based on WAG optimization.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.290
Teacher spread0.272 · 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 designSimulation or modeling
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
Published2023
Admission routes1
Has abstractyes

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