1887

Abstract

Summary

The efficient management of subsurface operations depends on the asset team’s technical excellence. Skilled practitioners in various domains interpret, model and predict reservoir performance. This, however, leads to silos between domains, each with its own language, methods and technologies, resulting in: a) An overly long cycle time for passing through domains, analyzing data and delivering key information that helps management decisions. b) The challenge of creating consistency between specialists and building confidence in the results. This presentation demonstrates a workflow that improves collaboration by helping practitioners work synchronously, based on fully automated reservoir modeling technologies allied with agile collaborative methods. The technology driver is a workflow orchestrator, which runs atop optimized reservoir modeling and simulation software packages and scripts. A cognitive approach is used: initial models are built from available data, and then refined iteratively as new information arrives. Updates are automatically propagated through domains and deliver new results. Consistency across domains is preserved and models are evergreen. Continuous alignment is guaranteed, and results reflect the asset’s needs. This solution, called Big Loop™, is software-agnostic, customizable to the needs of the individual organization, and has been shown to significantly improve asset development and management efficiency.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.202032024
2020-11-30
2024-04-25
Loading full text...

Full text loading...

References

  1. Aarnes, I., Midtveit, K., and Skorstad, A.
    [2015] Evergreen workflows that capture uncertainty – the benefits of an unlocked structure. First Break, 33(10), 89–92.
    [Google Scholar]
  2. Abd-AllahM., WaliaS., WalshS. and TopdemirS.
    [2017] Protecting Return on Investment through Automated Ensemble-based Quantification of Risk - Norwegian Offshore Field Case Study. 79th EAGE Conference & Exhibition, Extended Abstracts.
    [Google Scholar]
  3. J.Frette and J.Vonnet
    [2017] Using Big Loop and ensemble-based methods for more reliable reservoir predictions: applications on fractured reservoirs. First Break, 35, 65–68.
    [Google Scholar]
  4. Hegstad, B.K., SæstromJ.
    , [2014] Using Multiple Realizations from an Integrated Uncertainty Analysis to Make More Robust Decisions in Field Development, Abu Dhabi International Petroleum Exhibition and Conference.
    [Google Scholar]
  5. PettanC. and StromsvikJon F.
    [2013] The Peregrino Challenge: How to Keep Reliable Models While Drilling Eight Wells per Year, Offshore Technology Conference Brasil. OTC 24522.
    [Google Scholar]
  6. Taha. T et Al.
    [2019] History Matching using 4D Seismic in an Integrated Multi-disciplinary Automated Workflow, 7th SPE Reservoir Characterization and Simulation Conference and Exhibition. SPE-196680-MS.
    [Google Scholar]
  7. WaliaS., TopdemirS., Abd-AllahM., JohannessenA., and LindangerM.
    [2016] The Big Loop - An Integrated and Automated Approach for Reservoir Management and Ensemble-based Production Optimization. Third EAGE Integrated Reservoir Modelling Conference, Extended Abstracts.
    [Google Scholar]
  8. WilluweitM., Bin KhairulAzmi M., SilahaliE.
    [2015] Application of Big Loop Uncertainty Analysis to Assist History Matching and Optimize Development of Waterflood Field. SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition. SPE-176464-MS.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.202032024
Loading
/content/papers/10.3997/2214-4609.202032024
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error