Attribute-guided well-log interpolation applied to low-frequency impedance estimation

Abstract

Several approaches exist to use trends in 3D seismic data, in the form of seismic attributes, to interpolate sparsely sampled well-log measurements between well locations. Kriging and neural networks are two such approaches. We have applied a method that finds a relation between seismic attributes ͑such as two-way times, interval velocities, reflector rough-ness͒ and rock properties ͑in this case, acoustic impedance͒ from information at well locations. The relation is designed for optimum prediction of acoustic impedances away from well sites, and this is accomplished through a combination of cross validation and the Tikhonov-regularized least-squares method. The method is fast, works well even for highly underdetermined problems, and has general applicability. We apply it to two case studies in which we estimate 3D cubes of low-frequency impedance, which is essential for producing good porosity models. We show that the method is superior to traditional least squares: Numerous blind tests show that estimated low-frequency impedance away from well locations can be determined with an accuracy very close to estimations obtained at well locations.

Cooperative joint inversion of 3D seismic and magnetotelluric data: With application in a mineral province

“…The impedance inversion must therefore access a starting model that provides the missing low-frequency information. This is generally achieved by well correlation such as factorial kriging (e.g., Hansen et al, 2008), which necessitates knowledge of geology combined with good interpretive skills, unless many control wells are available. Also, velocity from refraction tomography and/or from reflection processing (e.g., NMO velocities) can be combined with factorial kriging (Nivlet, 2004).…”

Prediction of Favorable Carbonate Reservoirs under Extremely Thick Salts via Poststack Facies-Controlled and Prestack Zoeppritz Equation Inversions in the Santos Basin of Brazil

“…Large error in the low-frequency model can deviate the inverted model far away from the true solution. Omitting the low frequency of the seismic data is often compensated by interpolating the well log data [14,15]. In deepwater carbonate reservoirs under extremely thick salt settings with sparse wells, this lowfrequency impedance model may be inaccurate.…”

Reduced Inverse Distance Weighting Interpolation for Painterly Rendering

“…This problem arises in several fields of science, such as geophysics [1], oceanography [2], meteorology [3], super-resolution [4], or video coding [5]. A large amount of different techniques have been proposed in the literature for this task such as polynomial spline [6], wavelets [4], or variational approaches [3].…”