Geophysical Insights and the Paradise AI Workbench are solving interpretation problems with deep learning and machine learning technologies. Visit booth 750 to hear industry thought leaders present applications of machine learning and deep learning technologies to seismic interpretation in different geologic settings. Learn how new technologies will change and enable geoscience workflows for years to come. For more information on EAGE 2022 or to register for the EAGE event, click here.
Featured Speakers – Addressing Machine Learning Orchestration
Applying Machine Learning Technologies to Quickly Produce an Integrated Structural and Stratigraphic Seismic Classification Volume Calibrated to Wells
Traditional seismic interpretation, including fault interpretation and stratigraphic horizon picking, is poorly suited to the demands of unconventional drilling with its typically high well densities. Geophysicists devote much of their efforts to well planning and working with the drilling team to land wells. Machine learning applied in seismic interpretation offers significant benefits by automating tedious and somewhat routine tasks such as fault and reservoir interpretation. Automation reduces the fault interpretation time from weeks/days to days/hours. Multi-attribute analysis accelerates the process of high grading reservoir sweet spots with the 3D volume. Statistical measures make the task of calibrating the unsupervised results feasible.
How Paradise Operates Within the ODSU Environment
This talk presents the current state and direction of Paradise on OSDU and sets out how E&P companies can obtain value from Paradise on OSDU immediately. The talk highlights the capabilities of Paradise while illustrating how Paradise operates within the OSDU environment. Managers and geoscientists will benefit from seeing a high-level view of the commercial, off-the-shelf Paradise AI workbench taking advantage of OSDU today and its future evolution.
Considering the many producers who have adopted OSDU, the geoscience interpretation technologies in operating companies are transitioning from SaaS models to full OSDU operation. Geophysical Insights has worked with AWS to develop a Paradise® – OSDU Connector, enabling Paradise to run on OSDU as infrastructure and take advantage of OSDU communications among the various interpretation platforms. The Paradise OSDU Connector also connects Paradise to storage facilities, other applications, internal applications, and Virtual Data Infrastructure. The Connector can also be run on other cloud services, such as Azure. Companies using OSDU can realize value from Paradise ML applications today, even as we migrate Paradise to a fuller OSDU implementation of the product.
Introduction to Automatic Fault and Thin Bed Detection
Rapid advances in Machine Learning (ML) are transforming seismic analysis. Using these new tools, geoscientists can accomplish the following quickly and effectively:
- Run fault detection analysis in a few hours, not weeks
- Identify thin beds down to a single seismic sample
- Overlay fault images on stratigraphic analysis
Catch this talk at our booth daily for a quick orientation on the technology and examples of how machine learning is being applied to automate interpretation while generating new insights in the data.
Identify Thin Beds and Faults using Paradise Machine Learning Technology
Stratigraphic analysis using multi-attribute machine learning can reveal detailed lithologic features in the data that are not visible using traditional interpretation techniques. The presentation addresses using Self-Organizing Maps (SOM), an unsupervised machine learning (ML) process, to identify carbonate porosity in thin-bed environments. A clear view of reservoir compartmentalization will also be shown by applying the SOM process with deep learning Convolutional Neural Network (CNN) fault detection, a supervised ML method. Further, the talk will discuss how the CNN fault detection algorithm delineates faults in the data using synthetic fault volumes, avoiding picking fault segments manually. The new fault detection techniques save the geoscientists an immense amount of time, producing results in hours, not weeks. A fault attribute volume can be combined with other attributes through the SOM process, enabling the interpreter to integrate faults and stratigraphy.
Machine Learning Fault Detection: A Case Study
An innovative Fault Pattern Detection Methodology has been carried out using a combination of Machine Learning Techniques to produce a seismic volume suitable for fault interpretation in a structurally and stratigraphic complex field. Through theory and results, the main objective was to demonstrate that a combination of ML tools can generate superior results in comparison with traditional attribute extraction and data manipulation through conventional algorithms. The ML technologies applied are a supervised, deep learning, fault classification followed by an unsupervised, multi-attribute classification combining fault probability and instantaneous attributes.
Paradise: A Day in The Life of the Geoscientist
Over the last several years, the industry has invested heavily in Machine Learning (ML) for better predictions and automation. Dramatic results have been realized in exploration, field development, and production optimization. However, many of these applications have been single use ‘point’ solutions. There is a growing body of evidence that seismic analysis is best served using a combination of ML tools for a specific objective, referred to as ML Orchestration. This talk demonstrates how the Paradise AI workbench applications are used in an integrated workflow to achieve superior results than traditional interpretation methods or single-purpose ML products. Using examples from combining ML-based Fault Detection and Stratigraphic Analysis, the talk will show how ML orchestration produces value for exploration and field development by the interpreter leveraging ML orchestration.