Conglomerate masses, which widely developed in the piedmont area of Kuche Depression, Northwestern China, seriously affect the seismic structure imaging and limit the seismic interpretation precision. As a result, in a deep reservoir well in the area, the error between the predicted reservoir depth and the real one even is up to about 1000 m. It impedes the progress of hydrocarbon development in the depression. Gravity, magnetic and electromagnetic (EM) responses, especially EM data, are sensitive to the existence of conglomerate masses. An integrated 3D survey including gravity, magnetic and EM methods is conducted in Kuche Depression. All available geophysical data are jointed together and interpreted on an integrated interpretation platform GeoEast®. With the inverted 3D resistivity data we describe the spatial distribution of lithology and with the seismic data we give a believable structural frame interpretation. Joining the two kinds of data as well as drilling data and outcrops information, the distribution of conglomerate masses and their litholohy and lithofacies are comprehensively predicted. Subsequently, we conduct the joint inversion of gravity, magnetic, EM and seismic data and get the structural model of the intermediate and the shallow layers with which we can describe the distribution of conglomerate masses. The work improves the precision of the deep structural trap prediction. Three wells drilled subsequently shows that the maximum error between the predicted gross thickness of conglomerate masses and the real figure is only 3%. The velocity model is also built and used in the subsequent pre-stacking depth migration. It effectively helps limiting the interpretation error which results from the highvelocity horizons. The statistics of drilling data in the area shows that the general misfit of the predicted structure depth decreases from the original about 300 to 1,000 m to the present 30 to 120 m. After the project, a complete joint interpretation system integrating gravity, magnetic, EM and seismic data is released. The system makes the interpretation of multiple geophysical data changing from the traditionally qualitative comparison job to the presently accurate description based on the multiple geophysical data. The quantitative data output from the platform can be used to build velocity model.


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