HOUSTON–Seismic inversion, an essential part of reservoir characterization, has been the accepted methodology for predicting lithofacies between wells for decades. Inversion-based reservoir characterization computes rock properties—including both compressional (P) and shear (S) wave impedance—and density through seismic inversion. The desired reservoir properties (lithology, porosity, and fluids) are obtained through petrophysical inversion that relates rock properties to reservoir properties through rock physics models.
A new method based on machine learning shows promise in producing the same or even enhanced results over traditional advanced seismic inversion methods, which can take several months and require costly fees in software and geoscience services. Fortunately, the alternative ML approach to reservoir characterization does not require data to match physical models. Instead, it relies on an ML process that takes any number of seismic attributes and clusters them in multidimensional space, eliminating the dimensionality roadblock. The new methodology is based on applying self-organized maps (SOM), a form of ML, and correlating the results (“neurons”) to seismic lithofacies from petrophysical logs.
The neurons generated by SOM have some of the characteristics of inverted data; that is, they map intervals, not interfaces, and show the effect without knowledge of the seismic wavelet. Indeed, when the seismic wavelet is more broadband, the ML results are more detailed. Additionally, each sample is classified as a neuron, which provides interpretable data below the wavelet’s resolution limit.
This SOM-lithofacies correlation approach to reservoir characterization can significantly impact project economics, enabling lithofacies prediction between wells in weeks and at a fraction of the cost of sophisticated seismic inversion methods. The methodology was initially developed using synthetic seismic data and evaluated on data from the Niobrara formation in the Denver-Julesburg Basin in Colorado. The new technology is now being expanded and applied to both conventional and unconventional geologic settings.