Exxonmobil Upstream Research Company

Ricoh Company, Ltd.

Ramesh Neelamani

Kathrin Berkner

Martin P Boliek

John E Anderson

Jerome R Krebs

Edward L Schwartz

Max Deffenbaugh

Christine E Krohn

Thomas A Dickens

Anatoly I Baumstein

David L Hinkley

Peter E Hart

Gregory J Wolff

Partha Sarathi Routh

Charlie Jing

Warren S Ross

Peter Traynin

Jerry Krebs

Spyridon K Lazaratos

Sunwoong Lee

Carey M Marcinkovich

Richard T Houck

David R Converse

Charles Jing

Gopalkrishna Palacharla

Spyridon Lazaratos

The invention includes a method for reducing noise in migration of seismic data, particularly advantageous for imaging by simultaneous encoded source reverse-time migration (SS-RTM). One example embodiment includes the steps of obtaining a plurality of initial subsurface images; decomposing each of the initial subsurface images into components; identifying a set of components comprising one of (i) components having at least one substantially similar characteristic across the plurality of initial subsurface images, and (ii) components having substantially dissimilar characteristics across the plurality of initial subsurface images; and generating an enhanced subsurface image using the identified set of components. For SS-RTM, each of the initial subsurface images is generated by migrating several sources simultaneously using a unique random set of encoding functions. Another embodiment of the invention uses SS-RTM for velocity model building.

Seismic data processing

Method for reducing the time needed to perform geophysical inversion by using simultaneous encoded sources in the simulation steps of the inversion process. The geophysical survey data are prepared by encoding () a group of source gathers (), using for each gather a different encoding signature selected from a set () of non-equivalent encoding signatures. Then, the encoded gathers are summed () by summing all traces corresponding to the same receiver from each gather, resulting in a simultaneous encoded gather. (Alternatively, the geophysical data are acquired from simultaneously encoded sources.) The simulation steps needed for inversion are then calculated using a particular assumed velocity (or other physical property) model () and simultaneously activated encoded sources using the same encoding scheme used on the measured data. The result is an updated physical properties model () that may be further updated () by additional iterations.

Iterative inversion of data from simultaneous geophysical sources

Techniques are disclosed for performing time-lapse monitor surveys with sparsely sampled monitor data sets (). An accurate 3D representation (e.g., image) of a target area (e.g., a hydrocarbon bearing subsurface reservoir) is constructed using the sparsely sampled monitor data set (e.g., seismic data set). The sparsely sampled monitor data set may be so limited that it alone is insufficient to generate an accurate 3D representation of the target area, but accuracy is achieved through use of certain external information (). The external information may include predetermined base survey data from a first time that is used () to interpolate data that is not recorded in the sparsely sampled monitor data set to derive a fully sampled monitor data set that can be processed (e.g., using conventional processing techniques) for determining an accurate representation of the target area at a second time.

System and method for reconstruction of time-lapse data

Method for simultaneous full-wavefield inversion of gathers of source (or receiver) encoded geophysical data to determine a physical properties model () for a subsurface region, especially suitable for surveys where fixed receiver geometry conditions were not satisfied in the data acquisition. Simultaneous source separation () is performed to lessen any effect of the measured geophysical data's not satisfying the fixed-receiver assumption. A data processing step () coming after the simultaneous source separation acts to conform model-simulated data () to the measured geophysical data () for source and receiver combinations that are missing in the measured geophysical data.

Simultaneous source encoding and source separation as a practical solution for full wavefield inversion

Method for identifying geologic features, such as hydrocarbon indicators, from geophysical data, such as seismic data, by taking a curvelet transform of the data. After the curvelet representation of the data is computed (), selected geophysical data attributes and their interdependencies are extracted (), from which geological features may be identified (), either from attribute data volumes that are created or directly from the curvelet representation.

Geologic features from curvelet based seismic attributes

The invention discloses a way to recover separated seismograms with reduced interference noise by processing vibroseis data recorded (or computer simulated) with multiple vibrators shaking simultaneously or nearly simultaneously (). A preliminary estimate of the separated seismograms is used to obtain improved seismograms (). The preliminary estimate is convolved with the vibrator signature and then used to update the seismogram. Primary criteria for performing the update include fitting the field data and satisfying typical criteria of noise-free seismograms (). Alternative ways to update are disclosed, including signal extraction, modeled noise extraction, constrained optimization based separation, and penalized least-squares based separation. The method is particularly suited for removing noise caused by separating the combined record into separate records for each vibrator, and is advantageous where the number of sweeps is fewer than the number of vibrators ().

Separation and noise removal for multiple vibratory source seismic data

An exemplary embodiment of the present invention provides a method for interpolating seismic data. The method includes collecting seismic data of two or more types over a field (), determining an approximation to one of the types of the seismic data (), and performing a wave-field transformation on the approximation to form a transformed approximation (), wherein the transformed approximation corresponds to another of the collected types of seismic data. The method may also include setting the transformed approximation to match the measured seismic data of the corresponding types at matching locations (), performing a wave-field transformation on the transformed approximation to form an output approximation (), and using the output approximation to obtain a data representation of a geological layer ().

Method for analyzing multiple geophysical data sets

Method for adapting a template to a target data set. The template may be used to remove noise from, or interpret noise in, the target data set. The target data set is transformed () using a selected complex-valued, directional, multi-resolution transform ('CDMT') satisfying the Hubert transform property at least approximately. An initial template is selected, and it is transformed () using the same CDMT. Then the transformed template is adapted () to the transformed target data by adjusting the template's expansion coefficients within allowed ranges of adjustment so as to better match the expansion coefficients of the target data set. Multiple templates may be simultaneously adapted to better fit the noise or other component of the data that it may be desired to represent by template.

Method to adapt a template dataset to a target dataset by using curvelet representations

A method and apparatus is described for segmenting an image, for adaptively scaling an image, and for automatically scaling and cropping an image based on codestream headers data. In one embodiment, a file that can provide a header that contains multi-scale entropy distribution information on blocks of an image is received. For each block, the block is assigned to a scale from a set of scales that maximizes a cost function. The cost function is a product of a total likelihood and a prior. The total likelihood is a product of likelihoods of the blocks. The image is segmented by grouping together blocks that have been assigned equivalent scales. In one embodiment, the file represents an image in JPEG 2000 format.

Header-based processing of images compressed using multi-scale transforms

A method is disclosed for combining seismic data sets. This method has application in merging data sets of different vintages, merging data sets collected using different acquisition technologies, and merging data sets acquired using different types of sensors, for example merging hydrophone and geophone measurements in ocean bottom seismic data. In one embodiment, a desired data trace is to be determined from a set of measured data traces, and the following steps are applied: (a) model filters are constructed which express the deterministic relationship between the desired data trace and each available measured trace that depends on the desired data trace; (b) the noise properties associated with each measured data trace are determined; (c) a sufficient statistic for the desired data trace is formed by application of an appropriate filter to each measured trace and summing the filter outputs; (d) the sufficient statistic is further processed by a single-input single-output estimator to construct an estimate of the desired data trace from the sufficient statistic.

Method for combining seismic data sets

A method and apparatus are described for performing header-based scaling and cropping of images compressed using multi-scale transforms. In one embodiment, the method for adaptively scaling an image comprises receiving a file containing a header that contains multi-scale entropy distribution information on blocks of an image; for each block, determining that the block retains significance at a scale upon determining that an entropy of a multi-scale coefficient of a block at the scale is greater than a mean entropy of multi-scale coefficients of blocks in at least one coarser scale; and scaling the image to a coarsest scale at which a threshold percentage of the blocks retain significance at the scale.

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Houston, TX, United States of America

Ramesh Neelamani