Dallas Geophysical Society
March 2020 Luncheon

Sponsored by Emerson

Date: Tuesday, March 10, 2020

Time: 11:30 am - 1:00 pm

Location: Brookhaven College Geotechnology Institute, 3939 Valley View Ln, Farmers Branch, TX 75244

Topic: Automatic Geological Feature Detection and Classification in the Imaged Directivity Domain

Speaker: Elive Menyoli

Abstract: 

The recorded seismic dataset is a composite of many wavefields. The standard seismic image volume is dominated by the high-energy specular data associated principally with reflectors and fault planes. Consequently, lower energy wavefields associated with stratigraphic pinchouts, reefs, karst edges and small faults are often lost in the standard processing and imaging process.

This presentation shows an evolution of full-azimuth imaging technology, performed in the Local Angle Domain, for characterizing subsurface features from migrated seismic data. The system decomposes the recorded seismic wavefield, in-situ at the subsurface image points, into full-azimuth reflectivity and directivity components comprised of thousands of dips and azimuths. In the directivity gathers, different traces may contain energy from different features in the subsurface. We will demonstrate the use of Principal Component Analysis (PCA) with its inherent data reduction, to derive the principal components of the different energies contained in the decomposed wavefield.  PCA measures are performed in local windows around individual depth slices and all directivity bins within the directional gathers.

The next stage involves using the power of convolutional neural network (deep learning) to train and classify these principal component directivity wavefields into geological features, such as reflectors, point diffractors, faults, plus other identifiable components, such as ambient noise, acquisition footprint or coherent migration “smiles”. This is a reliable method for separating these components and produce targeted images from the decomposed wavefield.  The training of the deep learning algorithm use a data library containing many examples of different geometrical features, therefore increasing the credibility of the network learning process. Deep learning algorithm consist of multiple layers, where each layer contains a set of learnable filters with a small visual field of the input image. During the training process, each filter is convolved across the width and height of the input image, computing the dot product between the entries of the filter and the input, and producing an activation map of that filter. As a result, the network learn filters that activate when it detects a specific type of geometric feature at some spatial position in the input. The results reveal superior high-resolution images over previous diffraction weighted stack filters. Additionally, the deep learning approach offers significantly better time-to-results.

Bio: Elive Menyoli 

Elive Menyoli is Geoscience Business Development Manager for Emerson E&P software solutions. In this role, he helps to develop new business opportunities for Emerson’s Geoscience Services in North America as well as being a technical advisor for Emerson’s Seismic Processing and Imaging software products.

Elive has over 17 years of experience as a geophysicist and has led exploration teams analyzing producing basins and drilling hydrocarbon wells around the world for large and midsize companies, including Total SA and Marathon Oil.  Immediately prior to joining Emerson, he was the principal and founder of a geological/geophysical consulting firm, SITA Energy, LLC, working with small and midsize operators in offshore East and West Africa.

Elive holds an M.S. in Physics from Georg-August University of Goettingen, Germany and a Ph.D. from University of Hamburg, Germany. He is a member of the SEG, AAPG, SPE, GSH, HGS and OTC.