Application of Integrated Production System Modelling (IPSM) for Long-Term Production Forecasting and Optimization, a Case Study in Deepwater Assets, Malaysia
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".