scholarly journals Geostatistical sediment body modeling: Principles, methods and linkage to sedimentology

2021 ◽  
Vol 79 (2) ◽  
pp. 71-84
Author(s):  
Osamu Takano
Keyword(s):  
Modeling Principles

Using variability modeling principles to capture architectural knowledge

2006 ◽  
Vol 31 (5) ◽  
pp. 5 ◽  
Author(s):  
Marco Sinnema ◽  
Jan Salvador van der Ven ◽  
Sybren Deelstra
Keyword(s):  
Variability Modeling ◽  
Architectural Knowledge ◽  
Modeling Principles

Caveats and Pitfalls of Production Forecast Uncertainty Analysis Using Design of Experiments

2021 ◽  
Author(s):  
Boxiao Li ◽  
Hemant Phale ◽  
Yanfen Zhang ◽  
Timothy Tokar ◽  
Xian-Huan Wen
Keyword(s):  
Design Of Experiments ◽  
Uncertainty Analysis ◽  
History Matching ◽  
Petroleum Industry ◽  
Production Forecast ◽  
Forecast Uncertainty ◽  
Field Development ◽  
Development Alternatives ◽  
Modeling Principles ◽  
Made In

Abstract Design of Experiments (DoE) is one of the most commonly employed techniques in the petroleum industry for Assisted History Matching (AHM) and uncertainty analysis of reservoir production forecasts. Although conceptually straightforward, DoE is often misused by practitioners because many of its statistical and modeling principles are not carefully followed. Our earlier paper (Li et al. 2019) detailed the best practices in DoE-based AHM for brownfields. However, to our best knowledge, there is a lack of studies that summarize the common caveats and pitfalls in DoE-based production forecast uncertainty analysis for greenfields and history-matched brownfields. Our objective here is to summarize these caveats and pitfalls to help practitioners apply the correct principles for DoE-based production forecast uncertainty analysis. Over 60 common pitfalls in all stages of a DoE workflow are summarized. Special attention is paid to the following critical project transitions: (1) the transition from static earth modeling to dynamic reservoir simulation; (2) from AHM to production forecast; and (3) from analyzing subsurface uncertainties to analyzing field-development alternatives. Most pitfalls can be avoided by consistently following the statistical and modeling principles. Some pitfalls, however, can trap experienced engineers. For example, mistakes made in handling the three abovementioned transitions can yield strongly unreliable proxy and sensitivity analysis. For the representative examples we study, they can lead to having a proxy R2 of less than 0.2 versus larger than 0.9 if done correctly. Two improved experimental designs are created to resolve this challenge. Besides the technical pitfalls that are avoidable via robust statistical workflows, we also highlight the often more severe non-technical pitfalls that cannot be evaluated by measures like R2. Thoughts are shared on how they can be avoided, especially during project framing and the three critical transition scenarios.

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