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Record W4403284785 · doi:10.2118/221169-ms

Application of Integrated Production System Modelling (IPSM) for Long-Term Production Forecasting and Optimization, a Case Study in Deepwater Assets, Malaysia

2024· article· en· W4403284785 on OpenAlexaff
Nikolay M. Surin, Kees Kok, Nurhidayah Hutamin, Shodiq Khoirur Rofieq, Seyed Mousa MousaviMirkalaei, G. Syah, Mark Edmondson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsVirtual Materials Group (Canada)
Fundersnot available
KeywordsProduction (economics)Term (time)Computer scienceSystems engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract In typical integrated simulation projects involving multiple reservoirs connected to a single producing facility, poor communication between production and reservoir engineers, who use different analysis tools, often leads to unreliable results. CoFlow is an integrated production system modelling (IPSM) tool and platform that helps RE's and PE's overcome these challenges. This work studies how CoFlow was used to provide robust, long-term (10 years plus) production forecasting and optimization for a deepwater oil development in Malaysia. Currently ongoing deepwater projects off the coast of Sabah are critical to sustaining Malaysia's crude oil output. These projects face high costs due to specialized equipment and subsea infrastructure installation, making it a necessity to simulate the complete fluid journey from the subsurface all the way to the oil platforms to ensure engineering design and consistency during forecasting. One such project, henceforth named Field G, has been built as an IPSM model in CoFlow. An IPSM model includes the reservoir model(s), the wellbore models as well as the piping and equipment that form the asset's surface network. Field G IPSM model was used to couple two reservoirs and link them to a complex surface network system, whereby the produced gas was separated and re-injected into the reservoirs using a custom algorithm. The IPSM model was operated using network-level constraints, which mimic the maximum fluid handling capacities of certain equipment on the production platforms. This is a unique and often overlooked capability of IPSM models, and it helps to make sure that the system is not producing beyond the limitations imposed by its surface network. The model was run for simulation times greater than 16 years, enabling forecasts that reach critical junctures in the field life such as the end of a PSC. Furthermore, maximum gas and liquid rate constraints were imposed on the models and various well operating scenarios were assessed to find the most optimum solution. All this could not be captured with just regular reservoir simulation, hence showcasing the value and importance of IPSM for large offshore projects. This was the first time that the G field was collaboratively modelled using IPSM approach and used to simulate forecast periods longer than 10 years. The CoFlow platform provided fast runtimes which allowed the authors to run multiple prediction scenarios and saved many man hours. Moreover, the IPSM model helped capture the complex interactions between facilities and reservoir performance through integration and multi-disciplinary collaboration.

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)0.002
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 score0.040
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
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 head0.038
GPT teacher head0.281
Teacher spread0.243 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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