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Selection of the most succesful neural network algorithm for the porpuse of subsurface velocity modeling, example from Sava Depression, Croatia

An accurate time to depth conversion between seismic and well data (velocity modeling) is often a challenge in those hydrocarbon fields which were developed in the second part of the 20th century due to the quantity and quality of well logs. The problem is also apparent in the regional explorations where well data are scarce or spatially far apart. In this study, several neural network types were tested for the purpose of solving the time to depth relations in field with relatively dense well network, selected in the NW part of the Sava Depression, Croatia. A distinctive lithological boundary was determined within wells and it’s surface was interpreted from 3D seismic cube. Input data for the learning process were grid points with seismic two way time (TWT) expressed in ms and the absolute depth (Z) of the lithological borders determined from the well in part of grid points. Maps of selected borders were generated by neural prediction of time to depth relations of TWT values for each grid point. The validation of the approach was tested by comparing the values of surfaces generated by neural networks with ones by kriging and with values from wells which were subtracted from the dataset for learning. Multi-layer neural networks proved to be the most successful with the task of solving the time to depth relationships.

Neural networks; velocity modeling; 3D seismic; Sava Depression; Croatia

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