Self-Organizing Maps (SOM) is a relatively new approach for seismic interpretation in our industry and should not be confused with seismic inversion or rock modeling.  The descriptions below differentiate SOM, which is a statistical classifier, from seismic inversion.

Seismic Inversion
The purpose of seismic inversion is to transform seismic reflection data into rock and fluid properties.  This is done by trying to convert reflectivity data (interface properties) to layer properties.  If elastic parameters are desired, then the reflectivity from AVO must be performed.  The most basic inversion calculates acoustic impedance (density X velocity) of layers from which predictions about lithology and porosity can be made.  The more advanced inversion methods attempt to discriminate specifically between lithology, porosity, and fluid effects.  Inversions can be grouped into categories: pre-stack vs. post-stack, deterministic vs. geostatistical, or relative vs. absolute.  Necessary for most inversions is the estimation of the wavelet and a calculation of the low frequency trend obtained from well control and velocity information.  Without an accurate calibration of these parameters, the inversion is non-unique.  Inversion requires a stringent set of data conditions from the well logs and seismic.  The accuracy of inversion results are directly related to significant good quality well control, usually requiring numerous wells in the same stratigraphic interval for reasonable results.

SOM Seismic Multi-Attribute Analysis
Self-Organizing Maps (SOM) is a non-linear mathematical approach that classifies data into patterns or clusters.  It is an artificial neural network that employs unsupervised learning.  SOM requires no previous information for training, but evaluates the natural patterns and clusters present in the data.  A seismic multi-attribute approach involves selecting several attributes that potentially reveal aspects of geology and evaluate how these data form natural organizational patterns with SOM.  The results from a SOM analysis are revealed by a 2D color map that identify the patterns present in the multi-attribute data set.  The data for SOM are any type of seismic attribute which is any measurable property of the seismic.  Any type of inversion is an attribute type that can be included in a SOM analysis.  A SOM analysis will reveal geologic features in the data, which is dictated by the type of seismic attributes employed. The SOM classification patterns can relate to defining stratigraphy, seismic facies, direct hydrocarbon indicators, thin beds, aspects of shale plays, such as fault/fracture trends and sweet spots, etc.  The primary considerations for SOM are the sample rate, seismic attributes employed, and seismic data quality.  SOM addresses the issues of evaluating dozens of seismic attribute volumes (Big Data) and understanding how these numerous volumes are inter-related.

Seismic inversion attempts to invert the seismic data into rock and fluid properties predicted by converting seismic data from interface properties into layers.  Numerous wells and good quality well information in the appropriate zone is necessary for successful inversion calculations, otherwise solutions are non-unique.  For successful inversions, wavelet effects must be removed and the low frequency trend must be accurate.

SOM identifies the natural organizational patterns in a multi-attribute classification approach.  Geologic features and geobodies exhibit natural patterns or clusters which can be corroborated with well control if present, but not necessary for the SOM analysis.  For successful SOM analysis the appropriate seismic attributes must be selected.

Patricia Santogrossi

Sr. Geoscientist | Geophysical Insights

Patricia Santogrossi is a geoscientist who has enjoyed 40 years in the oil business. She is currently a Consultant to Geophysical Insights, producer of the Paradise multi-attribute analysis software platform. Formerly, she was a Leading Reservoir Geoscientist and Non-operated Projects Manager with Statoil USA E & P. In this role Ms. Santogrossi was engaged for nearly nine years in Gulf of Mexico business development, corporate integration, prospect maturation, and multiple appraisal projects in the deep and ultra-deepwater Gulf of Mexico. Ms. Santogrossi has previously worked with domestic and international Shell Companies, Marathon Oil Company, and Arco/Vastar Resources in research, exploration, leasehold and field appraisal as well as staff development. She has also been Chief Geologist for Chroma Energy, who possessed proprietary 3D voxel multi-attribute visualization technology, and for Knowledge Reservoir, a reservoir characterization and simulation firm that specialized in Deepwater project evaluations. A longtime member of SEPM, AAPG, GCSSEPM, HGS and SEG, Ms. Santogrossi has held various elected and appointed positions in these industry organizations. She has recently begun her fourth three-year term as a representative to the AAPG House of Delegates from the Houston Geological Society (HGS). In addition, she has been invited to continue her role this fall on the University of Illinois’ Department of Geology Alumni Board. Ms. Santogrossi was born, raised, and educated in Illinois before she headed to Texas to work for Shell after she received her MS in Geology from the University of Illinois, Champaign-Urbana. Her other ‘foreign assignments’ have included New Orleans and London. She resides in Houston with her husband of twenty-four years, Joe Delasko.

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