In order to use 3D seismic data for productivity index prediction of alluvial fan coarse-grained clastic reservoirs with low porosity and low permeability

we chose the Triassic Baikouquan Formation reservoir in Y-region at the northwestern margin of Junggar Basin as a case.Based on traditional reservoir prediction such as thickness and porosity of coarse-grained clastic reservoirs

we analyzed in detail factors influencing permeability and oil-bearing properties

and introduced the average monthly production data at the early stage of development into 3D seismic inversion to predict the productivity index.The following work flow was established:‘finding coarse-grained clastic reservoirs through typical curve inversion

finding high quality coarse-grained clastic reservoirs through porosity inversion

finding permeable coarse-grained clastic reservoirs through spontaneous potential inversion

finding oil-bearing coarse-grained clastic reservoirs through resistivity inversion

and predicting reservoir productivity index with the combination of several methods’.A productivity index cube was finally generated through Neural Network modeling by using the monthly productivity as hard data and wave impedance

porosity

resistivity

spontaneous potential inversion data cube and time domain structure as trai-ning samples.The result shows that there is a positive correlation(R

2

=0.948 7)between the predicted monthly productivity and initial average monthly production.For wells with an initial monthly average production more than 300 ton

the error of prediction is less than 10%.The data cube contains various information controlling hydrocarbon distribution

such as lithology

reservoir property

oil-bearing property and permeability.The oil production of three appraisal wells deployed based on this research in Y-region reached more than 5 ton per day

which verified the accuracy and practicability of this productivity index prediction technology.