MétaCan
Menu
Back to cohort
Record W2586337389 · doi:10.2118/182624-ms

Evaluation of Reservoir and Production System Integration in Production Strategy Selection

2017· article· en· W2586337389 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueSPE Reservoir Simulation Conference · 2017
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
FundersUniversidade Estadual de CampinasPetrobrasCMG Reservoir Simulation Foundation
KeywordsProduction (economics)Integrated productionSubmarine pipelineComputer scienceLift (data mining)Benchmark (surveying)Artificial liftGas liftPetroleum engineeringEngineeringGeology

Abstract

fetched live from OpenAlex

Abstract In offshore petroleum field studies, the integration between reservoir and production system simulators may improve production forecasts. Therefore, it is important to check if this integration is necessary and a methodology to do it adequately. In this work, we analyzed the influence of this integration in the development of a petroleum field focusing on the effects on the production strategy parameters for a benchmark model based on an offshore field in Brazil. The methodology is based on a 12-step procedure for closed loop reservoir development and management. The production facilities are integrated with the reservoir in Step 11 and we propose to re-optimize the production strategy using information about pressure gradients inside the satellite well pipes for production forecast, while evaluating net present value as the objective-function. We adapted an assisted optimization workflow to include the optimization of new variables such as, pipe diameters of production system, platform position and artificial lift application, and compared this with the production strategy obtained from the same benchmark in a nonintegrated approach. Comparing nonintegrated and integrated production strategies, we observed changes that indicate the need to integrate reservoir and production systems in the decision making process. (1) Platform position was more affected by economic indicators than production loss due to pressure drop in flowlines. (2) Economic return and field oil recovery were significantly affected by the diameters of the production pipes. Artificial lift application with high gas injection rates enhanced well performance. (3) A re-optimization of the number of wells had little effect on the cumulative oil production but larger influence on net present value, indicating potentially unnecessary wells in a nonintegrated project. (4) The integration demanded further optimization of well placement, significantly impacting both net present value and the recovery factor. The integrating production facilities with the reservoir, generated a significant increase in the net present value, maintained the same oil recovery factor and required less investment.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.038
Threshold uncertainty score0.927

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.116
GPT teacher head0.366
Teacher spread0.250 · 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