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Applications of Convolutional Neural Networks (CNN) to Seismic Interpretation

Applications of Convolutional Neural Networks (CNN) to Seismic Interpretation

As part of our quarterly series on machine learning, we were delighted to have had Dr. Tao Zhao present applications of Convolutional Neural Networks (CNN) in a worldwide webinar on 20 March 2019 that was attended by participants on every continent.  Dr. Zhao highlighted applications in seismic facies classification, fault detection, and extracting large scale channels using CNN technology.  If you missed the webinar, no problem!  A video of the webinar can be streamed via the video player below.  Please provide your name and business email address so that we may invite you to future webinars and other events.  The abstract for Dr. Zhao’s talk follows:

We welcome your comments and questions and look forward to discussions on this timely topic.

Abstract:  Leveraging Deep Learning in Extracting Features of Interest from Seismic Data

Mapping and extracting features of interest is one of the most important objectives in seismic data interpretation. Due to the complexity of seismic data, geologic features identified by interpreters on seismic data using visualization techniques are often challenging to extract. With the rapid development in GPU computing power and the success obtained in computer vision, deep learning techniques, represented by convolutional neural networks (CNN), start to entice seismic interpreters in various applications. The main advantages of CNN over other supervised machine learning methods are its spatial awareness and automatic attribute extraction. The high flexibility in CNN architecture enables researchers to design different CNN models to identify different features of interest. In this webinar, using several seismic surveys acquired from different regions, I will discuss three CNN applications in seismic interpretation: seismic facies classification, fault detection, and channel extraction. Seismic facies classification aims at classifying seismic data into several user-defined, distinct facies of interest. Conventional machine learning methods often produce a highly fragmented facies classification result, which requires a considerable amount of post-editing before it can be used as geobodies. In the first application, I will demonstrate that a properly built CNN model can generate seismic facies with higher purity and continuity. In the second application, compared with traditional seismic attributes, I deploy a CNN model built for fault detection which provides smooth fault images and robust noise degradation. The third application demonstrates the effectiveness of extracting large scale channels using CNN. These examples demonstrate that CNN models are capable of capturing the complex reflection patterns in seismic data, providing clean images of geologic features of interest, while also carrying a low computational cost.

 

Unsupervised vs. Supervised classifiers – Comparing classification results

Unsupervised vs. Supervised classifiers – Comparing classification results

By: Ivan Marroquin, Ph.D. – Senior Research Geophysicist

In machine learning, there is a very interesting challenge in comparing the quality of the classification result generated by either unsupervised or supervised classifiers. Most of the time, we opt for one technique over the other. Sometimes, we perform a comparison study and use a visual examination to decide which classifier produced the best outcome.

Can we do better than this? I believe so! Let’s assume that we have a dataset that consists of three well-defined groups of data points. Then, we use an unsupervised classifier to generate three clusters. The algorithm produces two outputs: (1) cluster centers and (2) membership of each data point to its closest cluster center. As a consequence, we get the boundaries of clusters (see figure A below). If we present the same data to a supervised classifier, assuming that the data points already have a class label assigned to them, the algorithm generates boundaries that separate a class from each other (see figure B below). So far, you would think: I cannot still compare the classification outputs. However, there is a common trait between these results: the presence of boundaries. What if I tell you that we can take advantage of the notion of the boundaries in the context of supervised classifiers. In a way, it can help to derive cluster centers associated with each predicted class (see cluster center symbols with dashed patterns in red in figure B below).

SOM analysis for seismic interpretation

There are so many different types of classification problems, I focused on the case of lithofacies classification from wireline well log data. I used this data to implement a machine learning pipeline to derive cluster centers. The pipeline consists of three steps (see diagram below): (1) generate a lithofacies classification, (2) derive cluster centers from lithofacies classification result, and (3) validate cluster centers. Each of these steps was addressed with a specific machine learning algorithm. For the first step, a multi-class feedforward neural network was used. In the second step, an evolutionary algorithm was used. And in the last step, I used a metric learning algorithm. To ensure that the best performing model in each step of the pipeline was obtained, the algorithms interacted with an automated machine learning method. New research efforts in machine learning have brought forward a concept known as “automated machine learning”. The objective of this new shift is to take us away from the manual adjustment of hyperparameters to using machine learning to optimize another machine learning by finding its best hyperparameters configuration.

SOM analysis for seismic interpretation

To demonstrate the effectiveness of the proposed machine learning pipeline and the quality of the obtained cluster centers, a lithofacies classification was produced from the derived cluster centers. In the next figure, from left to right, the first four panels show the wireline log data used to train the neural network. The following panel displays the neural network-based lithofacies classification. Note that three lithofacies classes were predicted: reservoir sand (bands in yellow), tight sand (bands in cyan), and floodplain rocks (bands in gray). The last panel displays the lithofacies classification from the derived cluster centers. There is a strong match between the two classifications in terms of the occurrence of reservoir sands, but also in the lithofacies sequence and boundaries. I am thankful to Geophysical Insights to grant the permission to present this research work at the upcoming SEG-SBGf Workshop on Machine Learning.

SOM analysis for seismic interpretation

If you are interested in learning on how we extract meaningful geological information from seismic with machine learning, and how our technology has helped geoscientists in finding hydrocarbons, please visit us at https://www.geoinsights.com/.

Or, if you desire further information, feel free to contact us.

Comparison of Seismic Inversion and SOM Seismic Multi-Attribute Analysis

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.

Rocky Roden

Sr. Consulting Geophysicist | Geophysical Insights

ROCKY R. RODEN has extensive knowledge of modern geoscience technical approaches (past Chairman-The Leading Edge Editorial Board).  As former Chief Geophysicist and Director of Applied Technology for Repsol-YPF, his role comprised advising corporate officers, geoscientists, and managers on interpretation, strategy and technical analysis for exploration and development in offices in the U.S., Argentina, Spain, Egypt, Bolivia, Ecuador, Peru, Brazil, Venezuela, Malaysia, and Indonesia.  He has been involved in the technical and economic evaluation of Gulf of Mexico lease sales, farmouts worldwide, and bid rounds in South America, Europe, and the Far East.  Previous work experience includes exploration and development at Maxus Energy, Pogo Producing, Decca Survey, and Texaco.  He holds a B.S. in Oceanographic Technology-Geology from Lamar University and a M.S. in Geological and Geophysical Oceanography from Texas A&M University.