Exxonmobil Upstream Research Company

Exxonmobil Technology and Engineering Company

Michael P Matheney

Reeshidev Bansal

Kyle J Basler-Reeder

Spyridon K Lazaratos

Mehdi Aharchaou

Vijay Khare

Eugene C Trantham

Sergey Fomel

Alexander A Martinez

Erik R Neumann

Joe B Molyneux

Edward B Donaldson

A method for learning and applying a similarity measure between geophysical objects is provided. Similarity measures may be used for a variety of geophysics applications, including inverse problems. For example, an inverse problem may seek to minimize or maximize an associated objective function, which summarizes the degree of similarity between observed data and simulated data. However, when comparing between two or more geophysical objects in the context of the inverse problem, it is difficult to determine whether the observed difference between the two or more geophysical objects is due to noise or intrinsic dissimilarity between the objects. In this regard, an application-specific similarity measure, which may be tailored to the specific application, such as the specific inverse problem, may be generated and applied in order to better solve the inverse problem.

Methodology for learning a similarity measure between geophysical objects

A method, including: determining, with a computer, a reflectivity spectrum; generating, with a computer, a target spectrum that is a convolution of a roll-off function and the reflectivity spectrum; and filtering, with a computer, seismic data to cause the seismic data to have the target spectrum.

Method for generating optimized seismic target spectrum

A method, including: calibrating a linear rock physics model to well log properties; generating a plurality of pseudo-well models for a subsurface region using a Monte Carlo approach; generating synthetic seismic traces from each of the plurality of pseudo-well models; computing top and base isochron from the synthetic seismic traces; computing seismic attributes in an interval specified by the top and base isochron on the synthetic seismic traces; correlating the seismic attributes to rock properties; and transforming seismic data into low-side, most-likely, and high-side estimates of rock properties.

Method for rapid calibration of seismic interval attributes to rock properties for assessment uncertainty

The present disclosure provides a system and method for estimating fracture density within a subsurface formation from S-wave seismic data. In one embodiment, the S-wave seismic data is separated into fast ('S') and slow ('S') data. A computer is used to compute local similarity of the Sand Sdata and to compute a cumulative time-difference by which the Sdata lags the Sdata from the local similarity. Based on the computed cumulative time-difference, the fracture density of a subsurface formation is estimated.

S-wave anisotropy estimate by automated image registration

The present disclosure provides a system and method for inferring one or more physical property parameters of a subsurface media by inverting converted wave data acquired during a seismic survey. Composite seismic traces are generated at a plurality of survey azimuths (step). These composite traces are composed such that their amplitudes are free of effects of subsurface anisotropy. At least one of the generated composite seismic traces is then inverted by isotropic inversion to determine a property parameter of the subsurface media (step).

Removal of fracture-induced anisotropy from converted-wave seismic amplitudes

Method for aligning converted wave seismic reflection data (PS data) with conventional PP seismic reflection data so that both data types may be used to more accurately image the subsurface for hydrocarbon exploration or field development. Amplitude vs. angle (AVA) or amplitude vs. offset (AVO) attributes of PP and PS seismic data are identified and defined, which attributes are theoretically expected to be in phase and optimize seismic resolution in the data. In one embodiment of the invention, such attributes are calculated (), then the same horizons are identified in a series of PP attributes and in a series of PS attributes, then the second series is aligned with the first at the horizon locations (), then a time transfer function is generated and applied to the PS mode data (), and the aligned joint-mode data are inverted () using, for example, AVA attributes.

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The Woodlands, TX, United States of America

Michael P Matheney