Joint Inversion Workflow Research Articles

Imaging Radial Distribution of Water Saturation and Porosity Near the Wellbore by Joint Inversion of Sonic and Resistivity Logging Data

Summary We present a work flow for joint inversion of sonic flexural-wave dispersion data and array-induction resistivity data acquired in a vertical well. The work flow estimates a pixel-based radial distribution of water saturation and porosity extending several feet into the formation at each log depth. Radial changes in saturation and porosity are caused by mud-filtrate invasion and mechanical damage, respectively. The flexural-wave and array-induction data have similar multiple investigation depths extending several feet into the formation. Furthermore, flexural-wave data are sensitive to porosity but have weak sensitivity to saturation, whereas induction data are sensitive to both porosity and saturation. Thus, integration of these data in a joint inversion can help to characterize the formation beyond the altered zone and reduce uncertainty of the interpretation. The work flow is validated on synthetic data for several scenarios of near-wellbore alteration. The work flow is then applied to field data from an offshore well drilled with oil-based mud in a gas-bearing clastic formation. The results are compared with traditional interpretation and core analysis, demonstrating an efficient and accurate inversion-based work flow that can complement traditional formation evaluation in challenging conditions.

Impedance joint inversion of borehole and surface seismic data

The impedance inversion for single surface seismic data is limited by the bandwidth of the seismic data and subject to a large degree of non-uniqueness. Joint inversion with other high frequency geophysical data has great potential to improve the resolution and reduce the ambiguity of the inversion result. The borehole seismic data have the advantages of less attenuation, higher resolution, wider frequency bandwidth and being closer to the reservoir target than the surface seismic data, which is valuable to improve the surface seismic inversion. We built an impedance joint inversion workflow based on the Bayes theorem. The borehole seismic and surface seismic data are integrated together with the likelihood function and the sparse priori distribution of the reflectivity is designed in accordance with the field logging data characteristic. Two practical cases of the impedance joint inversion with the borehole seismic data and the surface seismic data were presented. It is obvious that the impedance joint inversion method provides a substantial improvement with respect to the constrained sparse spike inversion result. Consequently, this joint inversion is a promising technology for reservoir characterization.