"Knowledge speaks, but wisdom listens “(J.H)” Supervisors: Dr Dwa Desa Warnana and Prof Dr. rer nat Bagus Jaya Santosa, S.U Phone: +628563258231 Address: Geophysics Laboratory, G Building 4th Floor. Physics Department, Institute Technology of Sepuluh Nopember,Surabaya
The development of carbonate reservoir characterization has evolved rapidly. One newmethod that i... more The development of carbonate reservoir characterization has evolved rapidly. One newmethod that is currently in use is Common Contour Binning (CCB). CCB is new technology whointroduced by using the concept of amplitudestacking of seismic 3D post stack data. CCB is usedto detect subtle hydrocarbon-related seismicanomalies and to pinpoint gas-water, gas-oil andoil-water contacts.The method enhances an amplitude anomaly todetect the oil water contact and compares it with a back propagation neural network to determine thedensity distribution. The density distribution fromthe back propagation neural network is used tosupport the results of common contour binning.In this paper, we assume that the oil water contact islocated at the interface of two contrasting densitylayers. In this case, the hydrocarbon trappingmechanism is a fault trap. Initially, the fault wasidentified using a fault enhancement filter, other seismic attributes were used to estimate the probability of an oil water contact in the reservoir.Common contour binning was then applied. Theinput data for the back propagation neural network process are seismic attributes that have a goodlinear correlation with the petrophysical data fromthe target, in this case density data from five wells.The procedure indicates a combination of the twomethods yields good oil water contact predictionsfrom 3D post stack seismic data
The development of carbonate reservoir characterization has evolved rapidly. One newmethod that i... more The development of carbonate reservoir characterization has evolved rapidly. One newmethod that is currently in use is Common Contour Binning (CCB). CCB is new technology whointroduced by using the concept of amplitudestacking of seismic 3D post stack data. CCB is usedto detect subtle hydrocarbon-related seismicanomalies and to pinpoint gas-water, gas-oil andoil-water contacts.The method enhances an amplitude anomaly todetect the oil water contact and compares it with a back propagation neural network to determine thedensity distribution. The density distribution fromthe back propagation neural network is used tosupport the results of common contour binning.In this paper, we assume that the oil water contact islocated at the interface of two contrasting densitylayers. In this case, the hydrocarbon trappingmechanism is a fault trap. Initially, the fault wasidentified using a fault enhancement filter, other seismic attributes were used to estimate the probability of an oil water contact in the reservoir.Common contour binning was then applied. Theinput data for the back propagation neural network process are seismic attributes that have a goodlinear correlation with the petrophysical data fromthe target, in this case density data from five wells.The procedure indicates a combination of the twomethods yields good oil water contact predictionsfrom 3D post stack seismic data
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