(1) Reliance Industries Limited, (2) EMGA ASA, Stiklestadveien 1, 7040 Trondheim, Norway sf@emgs.com P-197 A framework for risking analysis with EM data Rajib K. Sinharay (1) , Atul Tyagi (1) , Vidar Furuholt (2) , Stein Fanavoll* (2) , Pradeep K. Anumula (1) , Anil Tyagi (1) , Muralikrishna Akella (1) and Jens E. Danielsen (2) Summary Marine EM is routinely being used for hydrocarbon exploration about a decade but yet to find its way into the mainstream risking workflow of most exploration companies. We have developed a framework for incorporating EM data into a standard risking scheme and tested it for 12 prospects of Reliance blocks over Krishna- Godavari basin. The scheme shows consistent and fairly uniform results for the prospects. Introduction Using geophysical data to assess and compare geological risk of prospects in a portfolio is a central task in hydrocarbon exploration. Although there is no universal standard for this risking process, generally accepted frameworks do exist, for example (Otis, 1997). These frameworks can in principle be used with virtually any type of geophysical data. However, exploration companies have accompanying workflows and procedures which are often geared specifically towards traditional data types such as seismic. In particular, marine electromagnetic (EM) data cannot yet be said to have found its way into the mainstream risking workflow of most exploration companies – even among those that over the years have spent considerable resources on acquiring and processing EM data. Using the acquired EM data actively in risking and decision making is essential in order to obtain full value of information. Failure to do so has in the past all too often led to EM data being used only for internal evaluation purposes or simply shelved. In this paper we propose an extension to a general risking framework, which allows for the inclusion of results from EM surveys. This framework is rigorous and consistent enough to capture the most important risking information which can be extracted from an EM survey, yet sufficiently flexible to be applied to a wide range of geological scenarios and to be tuned to suit the varying needs and preferences of different users. An example is given where the proposed framework is applied to a collection of EM datasets from the Krishna-Godavari basin offshore India. Options for updating the probability of geologic success Geological risk associated with a prospect is commonly quantified as a geological chance of success P g . This probability is broken down into a set of risk factors, pertaining to different geological features that must be present in order for an accumulation of hydrocarbons to exist. The exact number and names of these factors may vary slightly within the industry, but for this study we adopt the definition of P g introduced by (Otis, 1997): The expression for P g states that a petroleum accumulation will occur if and only if all the four play elements source, reservoir, trap and dynamics are present and favorable. It also implicitly states and requires that the play elements’ existences are mutually independent. When we now want to introduce EM data into this or a similar framework, we face three main options: 1. Consult the EM data where relevant in the assessment of the individual geological sub- factors contained in the original framework. 2. Modify or extend the original framework with some EM-specific factors. 3. Construct a separate EM risking framework.