We present a comparative study about the detectability of a hydrocarbon reservoir in a marine environment, using controlledsource electromagnetic (CSEM) methods both in the time and frequency domain. The target is a thin resistive body buried at a certain depth under the sea floor. Depth of the sea, and depth, thickness and resistivity of the reservoir are variable model parameters. For different sets of these parameters we calculated synthetic electromagnetic (EM) responses using a parallel version of the three-dimensional (3D) time-domain finitedifference forward modeling code by Commer and Newman (2004) and the 3D frequency-domain finite-difference code by Newman and Alumbaugh (1995). To compare the responses quantitatively, signal-to-noise ratios (SNR) were calculated as a function of time/frequency and source-receiver separation. SNR was calculated using the scattered field response of the reservoir as signal and the response of the background, in our case a two-layered model, as noise.
Marine controlled-source electromagnetics (CSEM) is a promising yet challenging method in modern geophysical exploration for hydrocarbon reservoirs (MacGregor and Sinha, 2000; Eidesmo et al., 2002; Ellingsrud et al., 2002; Johansen et al., 2005, among others). It is important as a supplementary method to seismic surveys. Recent works on modeling and inversion of marine CSEM data include (Abubakar et al., 2006; Constable and Weiss, 2006; Hoversten et al., 2006; Um and Alumbaugh, 2005, among others). In contrast to time-domain electromagnetic (TDEM) methods and its corresponding data interpretation tools, the development and application of frequency-domain (FDEM) methods for marine surveys has made significant progress. In this paper we investigate the detectability of a 3D hydrocarbon reservoir in a marine environment. The first section presents synthetic responses obtained for different 3D hydrocarbon reservoir models with a time-domain and a frequency-domain finitedifference (FD) forward modeling solutions. Further, the two solutions are compared in their ability to detect a deep hydrocarbon reservoir. This comparison is presented in the form of a signal-to-noise ratio (SNR), contoured as a function of time/frequency and source-receiver offset.
In this study a typical 3D hydrocarbon reservoir model, shown in Figure 1, is employed. The model consists of a thin resistive target buried at a certain depth under the sea floor. Four different seawater depths are considered, h1 = 100, 200, 400 and 1000 m. We also varied the depth (h2 = 200, 400, 1000 m), thickness (h3 = 100, 200, 400 m), and resistivity (r3 = 20, 30, 50, 100 Ohm·m) of the reservoir. The reservoir itself extends 2000 m along the x and y axes, and its center is located at x = 0 m and y = 0 m. The EM responses were simulated using the 3D TDEM finite-difference forward modeling code of Commer and Newman (2004) and the 3D FDEM finitedifference code of Newman and Alumbaugh (1995).
To choose an optimal 3D grid for our simulations, its responses are calculated for a 1D layered model without the resistive target and compared against a quasi-analytical solution by Hoversten and Morrison (1982).
