- journal_title：Geophysics
- Contributor：Martha Lien
- Publisher：Society of Exploration Geophysicists
- Date：2013-07-01
- Format：text/html
- Language：en
- Identifier：10.1190/geo2012-0188.1
- journal_abbrev：Geophysics
- issn：0016-8033
- volume：78
- issue：4
- firstpage：ID15
- section：Interdisciplinary Studies
I evaluated a simultaneous joint inversion of seismic amplitude-versus-offset (AVO) and controlled-source electromagnetic (CSEM) data for fluid-flow monitoring. A new approach for structure-coupled joint inversion was presented, in which the coupling of the two data types was obtained by allowing for the direct identification of parameter structure that is shared by the different geophysical model parameters. The main idea was to use a composite parameter representation, which enables inversion with respect to the parameter magnitude and parameter structure. In the current application, parameter structure refers to transitions between dominating property values and is represented by the position and shape of the flooding front. Hence, with this approach, the position and shape of the flooding front are inverted for directly, and the coupling between the different data sets is obtained without the inclusion of an additional penalizing term in the objective function. Regularization of the inverse problem is obtained by using a flexible parameterization grid adapted to the resolution power of the available data. This approach is especially suited for problems in which the prior information is limited or highly uncertain. The solution approach is illustrated for two types of coupling: (1) identification of fluid saturation using rock-physics modeling and (2) for structure-coupled joint inversion with respect to P-wave velocity and electric conductivity. Through various synthetic examples in 2D, the proposed approach showed its efficiency for identifying the main features of the fluid distribution within the reservoir. Simultaneously inverting AVO and CSEM data was further seen to give results that were more robust with respect to certain random and more systematic (modeling) errors compared with inverting the data sets separately.