This paper will present a case study of an Uncertainty Analysis recently performed for the Kashagan Field. This is the first full scale uncertainty work performed on the field since it came online in September 2016. The paper will describe unique challenges given Kashagan peculiarities and describe methods and approaches taken to address those.
A multi-scenario modeling workflow has been utilized to fully explore subsurface uncertainties in the decision space. First, uncertainties that were believed to have an impact on field performance based on the available data and learnings from previous studies were identified and ranked. Then, so-called categorical parameters were combined by grouping geologically relatable parameters into several scenarios with differentiating behaviors which were checked by performing screening simulation runs. This was followed by Design of Experiment (DoE) runs (Placket-Burman and Latin Hypercube), proxy modeling and Monte-Carlo analysis to aid selection of high, mid, and low models.
More than 400 simulation runs were generated resulting in a wide range of outcomes for key performance indicators, enabling the selection of high, mid, and low model realizations to evaluate future field development decisions. The approach of combining categorical parameters into multiple scenarios followed by DoE runs offered several advantages including full sampling of the uncertainty space and clearer link between geologic input and dynamic output. It also allowed to get maximum information using the lowest number of simulation runs. Communication techniques such as plumbing diagrams representing geologic scenarios were effective in discussion of study results amongst experts.