Inversion Algorithms for Marine Controlled-source Electromagnetic Data
Authors: Abubakar Aria , Li Maokun , Habashy Tarek , Liu JIanguo 
Published: November 2008

The marine controlled-source electromagnetic (CSEM) technology has attracted much attention for its capability in directly detecting thin hydrocarbon reservoirs. The approach is based on comparing the electric field amplitude as a function of the source-receiver offset with a similar measurement for a non-hydrocarbon bearing reservoir. The presence of hydrocarbon raises the amplitude of the measured electric field indicating the existence and to some degree determining the horizontal extent of the hydrocarbon zone; however with this approach it is difficult to know the reservoir's depth and shape. A more rigorous approach to address this type of application is the full nonlinear inversion. In this presentation we present the so-called parametric inversion algorithm (PIA), which uses a priori information on the geometry to reduce the number of unknown parameters and to improve the quality of the reconstructed conductivity image. This PIA approach can be also used to refine the conductivity image that we obtained using the pixel-based inversion (PBI) algorithm. The PIA also adopts the Gauss-Newton minimization method. The parameters that govern the location and the shape of an anomaly include the depth and the location of the user-defined nodes for the boundary of the region. The unknown parameter that describes the physical property of the region is the conductivity. We will show some inversion results of synthetic and field data to illustrate the PIA approach. We further show that by combining both the pixel-based inversion algorithm and PIA we arrive at a better interpretation of the controlled-source electromagnetic data.

Introduction

With the advances in sensor and signal processing technologies, the marine controlled-source electromagnetic (CSEM) method has been deployed in the past few years for offshore exploration (MacGregor and Sinha (2000); Ellingsrud et al. (2002); Eidesmo et al. (2002); Constable and Srnka (2007)). The high contrast between resistive oil-bearing reservoirs and the conductive surrounding medium suited this method very well for hydrocarbon explorations. However, the diffusive nature of the electromagnetic field in the conductive environment makes the interpretation of the CSEM data very challenging because most of the information on deep structures are contained in the weak part of the measured signals. Therefore, robust and efficient inversion approaches are in great need. PIA (Smith et al. (1999); Zhang et al. (2007)) reconstructed the shape, location and conductivity of the reservoir based on a priori information. Compared with pixel-based inversion algorithms (Abubakar et al. (2006)), PIA deals with fewer unknown parameters. This helps to achieve a quantitative interpretation from CSEM data. In this abstract, we present several improvements of the model-based inversion scheme of Zhang et al. (2007). These improvements include a new data weighting and a modified regularization scheme, inversion of multiple objects and an adaptive choice of the unknown parameters as the inversion iteration proceeds. All these helped the robust application of PIA in inverting field data.