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Sealing Quality Analysis of Faults and Formations by Means of Seismic Attributes and Neural Networks
A new and efficient method for fault seal analysis using seismic data is presented. It uses multiple seismic attributes and neural networks to enhance fluid migration pathways, including subtle features that are not detectable using single attributes only. The method may be used as a first estimate of fault seal or to calibrate results from other techniques. The results provide information about which faults and fault segments are sealing or leaking. Fluid flow along individual faults appears to be focused along zones of weakness, and fault seal research should thus be focused on finding such weak locations within fault zones, a task that is best done using three-dimensional (3D) seismic data. Under certain conditions, it is suggested that fluids migrate along fault planes by a diapiric fluid flow mechanism. The results assist in calibrating the bulk hydraulic properties of faults and rock formations and can be used in basin modelling.
Understanding the hydrocarbon migration system in the sub-surface is a key aspect of oil and gas exploration. It is well known that conventional 3D seismic data contains information about hydrocarbon accumulations. Less known is the fact that 3D seismic data also contains information about hydrocarbon migration paths in the form of vertical noise trails. A method has been developed to highlight vertical noise trails in seismic data semi-automatically, using assemblies of directive multi-trace seismic attributes and neural network technology. The results of this detection method yield valuable information about the origin of hydrocarbons, about migration paths from source to prospect and about leakage or spillage from these prospects to shallow gas pockets or to the sea bed. Besides, the results reveal the sealing quality of faults, provide information on overpressure and whether prospects are charged or not. All these aspects are useful information for basin modelling studies and for an increased understanding of the petroleum system.
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