Knowledge management and 3D modelling: overview and application to iterative 3D modelling workflows*
S. Tyson A and J. Herweijer BA School of Petroleum Engineering, UNSW.
B ReservoirTeam Ltd.
The APPEA Journal 51(2) 683-683 https://doi.org/10.1071/AJ10063
Published: 2011
Abstract
As 3D reservoir modelling is effectively the centre stage in many multi-disciplinary reservoir management efforts, the need for effective knowledge management is paramount to ensure:
-
Inclusion of pre-existing knowledge is in the model, including absent (or controversial knowledge), reflected in uncertainties.
-
A definition of a modelling process comprehensively covers the reservoirs issues identified while leading to fit for purpose results in a given imposed time span.
-
Storage of knowledge generated and a modelling audit trail during the modelling process to ensure adequate and efficient model updates over time.
-
Creation of a link between the integrated modelling process and the discipline processes that generate the basic data that underpin the model Current 3D modelling methods and tools allow for creation of models and storing model specific workflows (i.e. software-related parameters and processing chains).
In addition, various efforts are undertaken to store modelling best practice, which typically entails general know-how about creating models. During a specific 3D modelling project, however, a considerable body of knowledge is generated about what and what not makes the model work for a given reservoir. Such knowledge, which are essential for systematic uncertainty assessment during an existing modelling effort or subsequent modelling efforts, needs to be managed in order to retain its relevance.
This extended abstract focuses on application of knowledge management in the EP industry, particularly on how process or workflow-based knowledge management approaches add value in the context of 3D modelling projects for multidisciplinary reservoir management.
Stephen Tyson has been working in reservoir characterisation and modelling since 1986. He holds a BSc (physics) from Imperial College and has recently submitted a PhD thesis at UNSW. He has lectured at the UNSW School of Petroleum Engineering and the Australian School of Petroleum in Adelaide. His specialisations are alternative gridding strategies and uncertainty modelling. He is working with OPAC Barata in Jakarta and studying an MA (corporate communications). |
Joost Herweijer has more than 25 years of broad experience in petroleum geology/engineering and hydrogeology. He holds an MSc (geology and geophysics) and a PhD (hydrogeology). He has worked for Shell and Elf Aquitaine on the application of geostatistical and probabilistic geological models and integration of geoscience (interfacing) with reservoir engineering. Subsequently, he worked as a consultant in various roles (technical, business development and project management) on oil reservoir characterisation and environmental projects in Europe, North and South America, Indonesia and Australia. His current position is subsurface director of Sibinga Petroleum working on oil field development. |
References
Chugh, S., Herweijer, J.C., and Kuppe, F., 2000–Analysis of production data to improve characterisation of in-situ megascopic reservoir permeability SPE/CERI Gas Technology Symposium, Calgary, Canada, 3–5 April, SPE paper 59761.Herweijer, J.C., Barley, M., Bainbrigge, P., SPE, and Herries, T., 2006a—SPE Asia Pacific Oil and Gas Conference and Exhibition, Adelaide, Australia, 11–13 September, SPE 101088.
Herweijer, J.C., Yarus, J.M., and Suana, M.J.V., 2006b—Process-based knowledge management: Overview and application of Iterative 3-D modeling workflows. In: Coburn, T.C., Yarus, J.M., and Chambers, R.L. (eds.) Stochastic modeling and geostatistics: principles, methods, and case studies, volume II. AAPG Computer Applications in Geology 5, 313–24.
Ringrose, P.S., 2007—Myths and realities in upscaling reservoir data and models. SPE Europe/EAPG Conference and Exhibition, London, UK, 11–14 June, SPE 106620.
Smith, G.C., Rayfield, M.A., Depledge, D.R., and Gupta, R. (2004). Chinguetti deepwater turbidite field, Mauritania: reserve estimation and field development using uncertainty management and experimental designs for multiple scenario 3D models. APPEA Journal 2004, 521–41.
Taleb, N.N., 2007—The black swan: the Impact of the highly improbable. New York: Random House publishing.
