The development of micro-fault
systems is one of the main factors determining the existence of complex reservoirs.These types of reservoirs are
characterized by a complex spatial distribution and pronounced heterogeneity.Moreover,in these reservoirs,the
identification of micro-fault systems is difficult.Consequently,detection and comprehensive prediction are the main
research directions in the exploration and development of complex reservoirs.In this study,a novel detection method for
micro-fault systems based on machine learning was proposed.The method accounts for rock-physics anisotropy and the
selection of sensitive attributes.First,an anisotropic rock physics model was built based on the complex reservoir
properties.The curves of elasticity and anisotropy can be estimated from well data.The gradient of anisotropy is a
parameter that is sensitive to micro-fault development.Subsequently,the seismic structural attributes were extracted from
post-stack seismic data,and seismic optimization was carried out using a correlation clustering algorithm,which was used
to select attributes sensitive to faults and fractures.Finally,the attribute set and micro-fault system index factor as
the input data were applied to establish the non-linear mapping using a support vector machine,which is a type of machine
learning algorithm.The micro-fault system index factor,which was used to describe properties of the micro-fault
system,was estimated by nonlinear mapping.A test on actual data from a complex,fault-fractured carbonate reservoir in the
Sichuan Basin,southwestern China,yielded predicted micro-fault system characteristics that were consistent with available
geological information.In addition,the result agreed well with a log interpretation,confirming that the proposed method
can provide support for reliable predictions micro-fault systems on complex reservoirs.
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