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Speaker: James Lowell, Geoteric
Co-Author: Mark Brownless, GeotericCGG
The potential impact of Artificial Intelligence (AI) within E&P organizations is only just starting to be realized. With increasing compute power, improved data processing speeds and advances in technologies (such as deep learning neural networks), it is likely that AI will continue to be a disruptive technology that will change E&P organizations for many years to come. Although AI can be used within many workflows, some of the most significant progress has been within seismic interpretation. In particular, the detection of faults. The identification of seismic faults is a good example of a task that is challenging and time consuming for geoscientists, yet, with the right network architecture and training, is achievable in a short time to a level of completeness and accuracy that is far outside the reach of most interpreters.
So where does this leave human interpretation? To help answer that question, the talk explores an old analogy that benefits from 20 years of experience, and then relates it back to seismic interpretation. We believe that geoscientists should be central to any G&G workflows. We are therefore developing our AI technology to be closely aligned with the interpreters’ way of working, allowing tightly coupled interaction as appropriate for the dataset and the individual interpreter, making AI seismic interpretation a reality.
In this talk we will share a workflow that integrates human experience with AI prowess to deliver a step change in the seismic interpretation process. This allows improved interpretation in both time taken and accuracy, allowing previously unidentified elements (to date considered sub-seismic) to be identified and understood. The degree of incorporation of the AI into the interpretation is at the discretion of the user, ranging from 100% AI to 100% user, and anywhere in between.
To achieve this technological advance, we have developed bespoke deep learning networks that have been trained using thousands of examples of different fault signatures from different geological environments across varying data quality. The pretrained networks can be applied directly onto unseen data cubes or can be fine-tuned with interpretations from new data sets, capturing additional knowledge from existing interpretations.
Critically, any interpreter changes to AI predicted results are captured by the deep learning network, allowing the network to learn from the interpreter’s experience and knowledge and subsequently applied on future data sets.
Speaker Biography: James Lowell, Geoteric
James joined Geoteric in September 2007, after obtaining PhD in machine learning and medical image processing from the University of Durham. Now R&D director, James works with a team of data scientists to advance the use of AI within geoscience.
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