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.
By: Iván Marroquín, Ph.D. – Senior Research Geophysicist
I am very excited to participate as a speaker in the workshop on Big Data and Machine Learning organized by European Association of Geoscientists & Engineers. My presentation is about using machine learning and deep learning to advance seismic interpretation process for the benefit of hydrocarbons exploration and production.
Companies in the oil and gas industry invest millions of dollars in an effort to improve their understanding of their reservoir characteristics and predict their future behavior. An integral part of this effort consists of using traditional workflows for interpreting large volumes of seismic data. Geoscientists are required to manually define relationships between geological features and seismic patterns. As a result, the task of finding significative seismic responses to recognize reservoir characteristics can be overwhelming.
In this era of big data revolution, we are at the beginning of the next fundamental shift in seismic interpretation. Knowledge discovery, based on machine learning and deep learning, supports geoscientists in two ways. First, it interrogates volumes of seismic data without preconceptions. The objective is to automatically find key insights, hidden patterns, and correlations. So then, geoscientists gain visibility into complex relationships between geologic features and seismic data. To illustrate this point, Figure 1a shows a thin bed reservoir scenario from Texas (USA). In terms of seismic data, it is difficult to discern the presence of the seismic event associated with the producing zone at well location. The use of machine learning to derive a seismic classification output (Figure 1b) brought forward a much rich stratigraphic information. Upon closer examination using time slice views (Figure 1c), it is indicated that the reservoir is an offshore bar. Note how well oil production matches the extent of the reservoir body.
Figure 1. Seismic classification result using machine learning (result provided by Deborah Sacrey, senior geologist with Geophysical Insights).
Another way knowledge discovery can help geoscientists is to automate elements of seismic interpretation process. At the rate machine learning and deep learning can consume large amounts of seismic data, it makes possible to constantly review, modify, and take appropriate actions at the right time. With these possibilities, geoscientists are free to focus on other, more valuable tasks. The following example demonstrates that a deep learning model trained can be trained on seismic data or derived attributes (e.g., seismic classification, instantaneous, geometric, etc.) to identify desired outcomes, such as fault locations. In this case, a seismic classification volume (Figure 2a) was generated from seismic amplitude data (Taranaki Basin, west coast of New Zealand). Figure 2b shows the predicted faults displayed against the classification volume. To corroborate the quality of the prediction, the faults are also displayed against the seismic amplitude data (Figure 2c). It is important to note that the seismic classification volume provides an additional benefit to the process of seismic interpretation. It has the potential to expose stratigraphic information not readily apparent in seismic amplitude data.
Figure 2. Fault location predictions using deep learning (result provided by Dr. Tao Zhao, research geophysicist with Geophysical Insights).
Machine learning is foundational to the digital transformation of the oil & gas industry and will have a dramatic impact on the exploration and production of hydrocarbons. Dr. Tom Smith, the founder and CEO of Geophysical Insights, conducts a comprehensive survey of machine learning technology and its applications in this 24-part series. The course will benefit geoscientists, engineers, and data analysts at all experience levels, from data analysts who want to better understand applications of machine learning to geoscience, to senior geophysicists with deep experience in the field.
Aspects of supervised learning, unsupervised learning, classification and reclassification are introduced to illustrate how they work on seismic data. Machine learning is presented, not as an end-all-be-all, but as a new set of tools which enables interpretation on seismic data on a new, higher level that of abstraction that promises to reduce risks and identify features that which might otherwise be missed.
The following major topics are covered:
- Operation – supervised and unsupervised learning; buzzwords; examples
- Foundation – seismic processing for ML; attribute selection list objectives; principal component analysis
- Practice – geobodies; below-tuning; fluid contacts; making predictions
- Prediction – the best well; the best seismic processing; over-fitting; cross-validation; who makes the best predictions?
This course can be taken for certification, or for informational purposes only (without certification).
Enroll today for this valuable e-course from Geophysical Insights!