In this technology guide sponsored by our friends over at Dell Technologies, we take a deep dive into how the team of Dell Technologies and AMD is working to provide solutions for a wide array of needs for more strategic cultivation of oil and gas energy reserves. We’ll start with a series of compelling use-case examples, and then introduce a number of important pain-points solved with HPC and AI. We’ll continue with some specific solutions for the energy industry by Dell and AMD. Then we’ll take a look at a case study examining how geophysical services and equipment company CGG successfully deployed HPC technology for competitive advantage. Finally, we’ll leave you with a short-list of valuable resources available from Dell to help guide you along the path with HPC and AI.
This technology guide, insideHPC Guide to HPC/AI for Energy, explores how the dynamic confluence of HPC and AI is now considered essential to any organization involved in energy exploration and the process of bringing the resulting energy resources to market.
Use Cases: HPC/AI for Energy
In order to set the stage for the balance of the guide, let’s take a look at a series of compelling use case examples describing the successful use of HPC/AI for energy industry applications.
Geoscience interpretation
Complex and demanding geoscience interpretation applications require performance to drive geological software tools for simulation, mapping, modeling and analysis. HPC is critical in the development and production stage of the hydrocarbon value chain and is why Dell Technologies has focused on developing solutions with leading Oil & Gas industry partners that leverage Dell EMC PowerEdge servers.
Drilling & production
Providing enhanced drilling and production workflows with compact, high performance solutions with the flexibility to support demanding multi-threaded engineering workflows.
Seismic data acquisition and interpretation
Accelerate resource-intensive data interpretation tasks, and optimize the analysis of seismic data to deliver faster property estimations. During the seismic data acquisition phase, energy companies are increasing the amount of HPC-driven data quality testing. For example, Dawson Geophysical collects complex seismic data at a vast scale, processes information centrally and hands projects to oil & gas clients faster using Dell EMC PowerEdge servers.
Geophysics analysis
Process, map and interpret surveys efficiently. A modern HPC portfolio like that offered by Dell Technologies enables large data sets to be processed significantly faster in fewer cycles and with less reprocessing than traditional methods. Accelerated compute makes it more feasible to use larger, more detailed data sets for seismic processing and to carry out advanced analytics such as Large Volume Seismic Interpolation, Seismic amplitude versus offset (AVO) Analysis and 5D Seismic Processing.
Oil rig & oil field system maintenance and monitoring
It’s critical to monitor and maintain systems on an oil rig or oil field. Understanding how assets are performing in real-time is essential for optimizing operations, but it is not enough to truly manage systems effectively. Ideally, preventing a failure in the field is better than reacting to one after the fact, and managing costs with forward planning is better than being forced to scale back operations and to artificially limit production. This is where predictive maintenance using AI is essential. HPC-driven Asset Performance Monitoring and Predictive Maintenance Analytics, can use data collected from sensors on equipment at the edge, along with manufacturer enriched data to identify potential failures before they occur and deploy corrective measures quickly.
Production optimization
When we look at the multiple sources of data for Production Optimization–pipeline and facilities utilization data, well analysis, and logistics data–it’s easy to see why HPC is important to its success, particularly when executing multiple production scenarios in order to select the best solution.
Asset inspection
It’s important to inspect pipelines and refineries and record safety readings. Successfully applying AI to train models designed to detect anomalies and areas of interest across a large, complex data set requires HPC. The resulting analytics workload is a more cost effective, practical, reliable and scalable approach to inspection than a manual process.
Over the next few weeks we will explore how with the accelerated use of HPC and AI in the energy industry, there are strategic options for laying a directed path for these technologies to become a strong competitive advantage. Dell Technologies and AMD is a team that can help you travel down this path:
- HPC/AI for Energy – An Overview
- Use Cases: HPC/AI for Energy
- Energy Industry Pain-Points Solved by HPC/AI
- HPC/AI Technology for Energy
- Case Study: CGG, Next Steps with Dell Technologies Solutions for the Energy Industry, Summary
Download the complete insideHPC Guide to HPC/AI for Energy, courtesy of Dell.
