Exxonmobil Technology and Engineering Company

Exxonmobil Upstream Research Company

Erik R Neumann

Junzhe Sun

Valeriy V Brytik

Mehdi Aharchaou

Anatoly I Baumstein

Carey M Marcinkovich

Rongrong Lu

Hong Zhao

Lingxiao Zhang

A methodology for extending bandwidth of geophysical data is disclosed. Geophysical data, obtained via a towed streamer, may have significant noise in a certain band (such as less than 4 Hz), rendering the data in the certain band unreliable. To remedy this, geophysical data, from a band that is reliable, may be extended to the certain band, resulting in bandwidth extension. One manner of bandwidth extension comprises using machine learning to generate a machine learning model. Specifically, because bandwidth may be viewed as a sequence, machine learning configured to identify sequences, such as recurrent neural networks, may be used to generate the machine learning model. In particular, machine learning may use a training dataset acquired via ocean bottom nodes in order to generate the machine learning model. After which, the machine learning model may be used to extend the bandwidth of a test dataset acquired via a towed streamer.

Bandwith extension of geophysical data

A method for approximating an inverse Hessian is provided. One methodology to generate the inverse Hessian is to precondition the gradient, such as by using point-spread function deconvolution, T-power, or source-illumination compensation, prior to using non-stationary matching filters (NMF) to generate the inverse Hessian. Various types of NMF are contemplated, including using filters for different windows in the subsurface or using filters assigned to specific locations in the subsurface. Further, the number of filters for NMF may vary from iteration to iteration. For example, the filters assigned to the specific locations in the subsurface may be generated in a multi-scale manner, in which an initial iteration uses longer scale/longer wavelength features for inversion and subsequent iterations use finer scale/smaller wavelength features for inversion.

Method for hydrocarbon prospecting using an approximated inverse hessian to update a property model

A method for partitioning a search direction when using least squares reverse time migration (LSRTM) is provided. LSRTM may be used iteratively in order to improve imaging accuracy. As part of LSRTM, multiple local line searches may be performed. In particular, image space may be partitioned, such as by using a set of masks. The search direction, such as the gradient, may be partitioned using the set of masks. Local line searches may be performed for each partition of the search direction, resulting in finding respective line search constants. The respective line search constants may then be used for iterating the model in order to improve imaging accuracy.

Method for partitioning a search direction when using least squares reverse time migration

A method for directional Q compensation of seismic data may comprise calculating angle-dependent subsurface travel times; applying directional Q compensation to the prestack seismic data to obtain Q-compensated data in time-space domain, wherein the directional Q compensation is based on the angle-dependent subsurface travel times; and using the Q-compensated data to generate an image of the subsurface. Directional Q compensation may comprise determining an angle-dependent forward E operator and an angle-dependent adjoint E* operator using the angle-dependent subsurface travel times; and applying a sparse inversion algorithm using the angle-dependent operators to obtain a model of Q-compensated data. The angle-dependent operators may be preconditioned by introducing ghost and source effects in a wavelet matrix and a transpose of the wavelet matrix, respectively, such that applying a sparse inversion algorithm using the preconditioned angle-dependent operators is used to obtain a model of Q-compensated, deghosted data without source effects.

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Houston, TX, United States of America

Erik R Neumann