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Seismic methods have been used extensively for studying deep water reservoirs.  This is due to the fact that the seismic response is sensitive to changes in the bulk and shear modulus of the matrix or the acoustic properties of the overburden and reservoir rock, but this method’s insensitivity to hydrocarbon/water saturations (due to their similar acoustic properties) create difficulty when attempting to distinguish between economic and non-economic reservoirs.  To better characterize the economic yield of a given reservoir, a controlled source electromagnetic (CSEM) method has been utilized.  As noted by Constable et al., (2007), marine CSEM could be the most important geophysical technique to emerge in many years. CSEM technology offers a great advancement in hydrocarbon exploration due to its sensitivity to resistors (gas saturations) often associated with hydrocarbon reservoirs (Hou et al., 2006) and has even shown potential for hydrocarbon reservoir monitoring as was demonstrated by Orange et al. (2009).  The high resistivity associated with hydrocarbon-filled reservoir rocks (30-5000 Ωm) compared to those filled with saline fluid (.5-2 Ωm) makes EM a better suited tool for estimating and quantifying reservoir hydrocarbon yield (Johansen et al., 2005).  Seismic and CSEM methods can be used individually or jointly to aid in identifying deep water reservoirs, but overburden parameters (i.e. sediment thickness, seawater depth, seawater and overburden sediment conductivity) vary for different reservoir situations, causing irregularities and uncertainties in the geophysical response.  These irregularities can cause a misinterpretation of data and an erroneous estimation of reservoir hydrocarbon saturation. However, few studies have examined the effect that the overburden has on seismic and CSEM responses. 

In this study, we performed sensitivity analyses of geophysical responses to variation in reservoir and overburden parameters, in order to provide general guidance on the application of geophysical methods to different hydrocarbon reservoir and overburden situations. Then, using the significant parameters identified from the forward modeling, we executed an MRE based Bayesian inversion analysis to analyze inversion efficiency and optimize effectiveness. This research assesses the effect of the reservoir and overburden on geophysical responses to aid in determining the optimal geophysical method for deep water hydrocarbon exploration and parameter estimation. 

This study focuses on data gathered from the Troll field site in the North Sea.  The Troll field site is a well explored and highly studied region of the Norwegian Shelf.  The site is divided into two main sections, Troll West and Troll East, where the East section contains a majority of the gas extraction and the West portion is broken into two parts, one focusing on oil extraction and the other focusing on gas extraction.  This study uses data gathered from the West portion, also known as the Troll West Gas Province (TWGP).  The area consists of generally constant seawater conductivity, water depths (~325m), and relatively flat seafloor surface. These conditions and its exploratory history make it ideal for studying the applicability of EM and seismic surveys for hydrocarbon exploration. 

First we generated baseline truth models from well log data, then varied the overburden and reservoir parameters (e.g., target layer thickness, porosity, saturations, etc.) within their physical ranges, producing multiple realistic subsurface models. Next, geophysical (seismic and EM) responses were computed for each model using seismic/EM forward modeling algorithms, and the differences in seismic and EM data between these realistic and baseline models were analyzed. Forward modeling results suggest that EM calculations are sensitive to several factors both in the reservoir and the overburden. Outside the reservoir, EM responses are most sensitive to seawater electrical conductivity, seawater depth and overburden sediment thickness.  While among the parameters/properties in the reservoir, EM responses are most sensitive to gas saturations.  Seismic data show strong responses to reservoir parameters, most notably to reservoir porosity. After identifying the significant parameters, regression analyses using generalized linear models were performed and analyzed.  Subsequent to exploring the sensitivity of seismic and EM responses to changes in overburden and reservoir parameters/attributes, the applicability of using these datasets for inverse modeling (parameter estimation) was evaluated using a Minimum Relative Entropy-based Bayesian stochastic inversion.  Results suggest that estimations of reservoir parameters using high dimensional models can be rather difficult to do with great confidence.  It was found that the more prior information available on the overburden unknowns, the closer the posterior estimation of the reservoir parameters will be to the actual values.  Results also show that seismic methods are effective in estimating reservoir porosity, while EM has the potential for estimating reservoir saturations.  The integration of both, assuming enough information about the priors are known, has the potential to accurately estimate reservoir parameters presuming enough models are run. Informative prior knowledge about the field site, efficient sampling techniques, reduction of the parameter space, and computing capability are all necessary components for successful geophysical characterization of a petroleum reservoir, given the high dimensionality and non-uniqueness of the inverse problem.