A Framework for Offshore Load Environment Modeling1

Abstract

This paper presents a novel decision analytical framework for systems modeling in the context of the risk-informed integrity management of offshore facilities. Our focus concerns the development of system models representing environmental loads associated with storm events. Appreciating that system models in general serve to facilitate the optimal ranking of decision alternatives, we formulate the problem of systems modeling as an optimization problem to be solved jointly with the ranking of integrity management decision alternatives. Taking offset in recent developments in structure learning and Bayesian regression techniques, a generic approach for the modeling of environmental loads is established, which accommodates for a joint utilization of phenomenological understanding and knowledge contained in databases of observations. In this manner, we provide a framework and corresponding techniques supporting the combination of bottom-up and top-down modeling. Moreover, since phenomenological understanding and analysis of databases may lead to the identification of several competing system models, we include these in the formulation of the optimization problem. The proposed framework and utilized techniques are illustrated in an example. The example considers systems modeling and decision optimization in the context of a possible evacuation of an offshore facility in the face of an emerging storm event.

Faith and fakes – dealing with critical information in decision analysis

“…In Section 5 we will discuss generic cases enveloping theses situations in more detail, and also draw parallels to situations encountered in practical decision making. However, before this we first briefly introduce the Bayesian decision analysis framework for ranking of decision alternatives subject to uncertainty originally introduced by Faber and Maes (2005) and later elaborated in Glavind and Faber (2018).…”

“…As outlined in Glavind and Faber (2018) the identification and probabilistic representation of possible systems may take basis in bottom-up, i.e. phenomenological models where the whole (the system) is established by combining its parts (constituents) or top-down systems modelling approaches, where the parts may be derived from information concerning the performance of the whole, or combinations of the two.…”

“…A starting point for the identification of relevant databases as well as covariates may be developed on the basis of engineering understanding and/or bottom-up phenomenological models. In this light it is immediately realized, as emphasised in Glavind and Faber (2018), that so-called data driven modelling techniques (top-down) are equally subjective as traditional phenomenological approaches (bottom-up). In addition to the identification of the different scenarios a next step is to identify different decision alternatives of relevance for management of the systems of consideration.…”

“…This is of crucial importance in the context of model optimization, where an optimal trade-off between complexity (in terms of system, constituents, and parameter models), and the associated statistical uncertainties must be identified. Decision rules relating observations to repair/maintenance Following Glavind and Faber (2018), a model () a M is a relationship between input and output as a function of a decision a . In general the performance of the system is associated with uncertainty, which is why the output of the system with which we associate utility () Ua in the following is random.…”

“…In Glavind and Faber (2018) the approach outlined in the foregoing concerning the joint identification of system representation and optimal risk management options is illustrated on an example considering evacuation planning of offshore oil and gas production facilities in the event of an emerging storm. The reader is referred to this example to obtain more detailed information concerning how multiple systems and various types of uncertainties are modelled and treated in the decision making process.…”