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Easy Steps in Probabilistic Dynamic Modelling in FDP


 Adequate implementation of probabilistic dynamic modelling workflow allows to explore full range of solutions and helps in better decision making. Probabilistic modelling workflows comprise of a series of steps. This paper closely scrutinizes three key steps of a probabilistic workflow – sensitivity analysis, experimental design and model selection for decision making. There are different methods and processes practiced in the industry for these key steps. A comparative study of commonly available and practiced techniques is performed, and their pros and cons are demonstrated using open available Brugge field model.
 In the first part the commonly used tornado/one variable at a time (OVAT) is compared against other global sensitivity techniques suchas ANOVA (Analysis of Variance) and multi-regression analysis. The difference among the techniques is explained and its application is demonstrated through field example. It is shown that the global sensitivity analysis is more robust and provides better results but at a higher computational cost.
 Next experimental design techniques are reviewed with focus on the optimum experimental design. Key concepts of experimental design such as aliasing, resolution and confounding pattern are explained. It is shown that an appropriate experimental design can be used to avoid confounding of key parameters. The confounding patterns have been demonstrated for factorial design experiments. The concept is shown with the example of Brugge model.
 Model selection after history matching process is a challenging task. In the last part of the paper two different multi-dimensional visualization techniques PCA (Principal Component Analysis) and t-SNE (t-Stochastic Neighbour Embedding) are explained and utilized to visualize the ensemble of history matched models. These techniques in combination with k-mean clustering has been used to select representative models for prediction. The overall uncertainty captured by the representative models is compared to the full ensemble of history matched models. It is shown that clustering along with visualization provide a robust framework for model selection.
 The paper is intended to provide a good understanding of some of the key steps of the probabilistic modelling workflow. The technical concept behind each technique is briefly described but the main focus is on the practical aspects and implemtation of these techniques.