As the search for hydrocarbons becomes more challenging, geophysical data of different types beyond seismic reflection are sometimes acquired for exploration. Multiple data types, which are sensitive to different physical properties, can be used to reduce uncertainty in predicting the existence or absence of hydrocarbons. Joint inversion of multiple geophysical data types for subsurface physical properties is an attractive tool for hydrocarbon detection. However, there are challenges in performing the joint inversion of multiple data types simultaneously due to factors such as differences in spatial resolution and sensitivity to different rock properties. In this paper, we describe a hybrid joint inversion method. It inverts low-resolution data types for rock-property models on a coarse scale, and these models are subsequently used as model constraints in the inversion of high-resolution data. This inversion method benefits from the information contained in different data types but with an increased computational efficiency. The methodology and the value of joint inversion for hydrocarbon detection are demonstrated with synthetic data examples. 2011 Society of Exploration Geophysicists.
