- Cloud computing is now so advanced and accessible, there is nothing it cannot achieve that used to be possible only on-premises or in massive high-performance computing (HPC) systems. It is already solving problems in other complex industries like finance and health and the oil and gas sector can benefit from the same in simplifying overall operations. Implementing cloud technology is a significant opportunity for oil and gas companies because it allows them to scale their business.
- Standardization is something that has been historically lacking in the oil and gas industry, which is still heavily reliant on proprietary internal systems. A recent exception is the formation of The Open Group OSDU™ Data Platform, an open-source, technology-agnostic platform that allows energy operator and service companies to collaborate. Standardization allows greater innovation and collaboration, simplifying acquisitions and divestments of assets. Companies can overcome data management challenges, and it can all be done in the cloud using a common set of communication protocols.
- Machine learning also leverages cloud technology and common data formats and platforms. Implementing machine learning using legacy data formats and internal infrastructures is time-consuming and limited. Just like other industries, the oil and gas industry is ready to adopt advanced machine learning solutions now. Machine learning will not make the oil and gas workforce obsolete—but it will drive out non-interactive systems. This means the most skilled experts will adopt machine learning and lead faster, more accurate decision-making.
- ‘New Energy’ is the concept of an evolving oil and gas sector as demand for fossil-based energy changes. Oil companies are adapting to market changes, including demand for more transparency on emissions, more sustainable energy sources, and carbon capture technology. Using cloud computing, standardization, and deep learning, these companies are becoming more agile and poised for success throughout the energy transition.
A Seismic Data Format Designed for the CloudAs companies continue to wrestle with enormous, complex data streams such as petabytes of seismic data, the pressure to invest in digital technology intensifies. Bluware has adapted to this imperative, delivering a cloud-based format for storing seismic data called Volume Data Store (VDS™) and an interactive deep learning process called InteractivAI™. Based on concepts developed in the gaming industry, VDS is an advanced, cloud-ready system for storing signal data that enables fast, random access to multidimensional volumetric data. This technology, 20 years in the making, is flexible enough to handle petabytes of seismic data, which can be stored in traditional file storage systems on-premises or as objects in the cloud. VDS is revolutionizing seismic workflows by reading data stored with adaptive compression. By making seismic data more accessible and scalable, VDS offers advantages like fast access and cost-effective storage using cloud providers such as AWS and Microsoft, through a unique combination of features, including: • Adaptive Streaming™, eliminating the need to copy and convert data to different formats • Trulossless™ adaptive compression, enabling SEG-Y data compression without having to store copies • Truserverless™, the server-less, cloud-ready VDS architecture that lowers hardware and storage cost • Random access patterns, which enable interactive deep learning • Optimization for GPU and multithreading • Direct API access to streamline workflows OpenVDS, an open-source API to access VDS, is available on the OSDU Data Platform. Bluware also provides OpenVDS+, which has all the capabilities of the OpenVDS API and adds the ability to compress VDS using Bluware’s industry-leading compression technology.
Streaming Seismic Data into Your Interpretation WorkflowsBluware Flexible Access Storage Transcoding (FAST™) uses Adaptive Streaming to stream seismic data from VDS into your interpretation application, enabling the application to read the data without duplicating it. This means you can move applications to the cloud and access stored objects directly and immediately. FAST creates a centralized hub of seismic data, allowing businesses to reduce or eliminate costs associated with disks and traditional file storage systems while making workflows faster.
AI in oil and gasAI, or artificial intelligence, refers to machines that have been programmed to simulate human intelligence, taking iterative actions to solve problems. Algorithms are an important framework for the many types and levels of AI. Machine learning, a subset of AI that refers to the concept that computer programs can adapt and learn from new data without human assistance, also uses algorithms to model and predict outcomes. This type of automated learning is enabled by deep learning, which is essentially layers of data networks that accurately cluster data and approximate predictions. Adoption of AI applications in the oil and gas industry has been slow but is gaining traction. More specifically, deep learning tools are being used more often to generate results that drive cost-efficient decision-making. Unlike a “black box” method where deep learning tools receive seismic data and generate outputs that data scientists are supposed to trust without any transparency into the pre-trained data, interactive deep learning—a concept trailblazed by Bluware—lifts the veil from the black box by allowing interpreters to train data, adjusting and optimizing the deep learning network in real time. Geoscientists are in control of the deep learning process from the start, producing models they can trust from a blank, untrained network.
Interactive Deep Learning for Seismic WorkflowsInteractivAI™ uses VDS to optimize interpretation workflows so geoscientists can get the most accurate results in a fraction of the time. Most deep learning solutions are not truly interactive, using pre-trained data that may be irrelevant to the project at hand. InteractivAI works with geoscientists, not at them, keeping them in the driver’s seat through two-way, real-time feedback to understand what is being interpreted. This way, the tool captures the expert’s knowledge and delivers a more precise, detailed interpretation. Because it learns through the geoscientist’s refinement, InteractivAI improves outcomes by suggesting alternative interpretations or other features that may have been missed. By capturing a more granular, accurate level of detail from the data, the tool can inform faster decision-making with more holistic results. Rather than replacing geoscientists through AI, this solution can make them even better interpreters. InteractivAI can interpret any geological or geophysical feature—not just faults or salt structures. Geoscientists using this tool can interpret channels, petroleum system elements, shallow hazards, and more. Its ability to preserve and use intelligence from previous projects can guide asset teams more precisely. It also allows geoscientists to collaborate and transfer knowledge to colleagues from anywhere at any time because it is cloud- and web-native.
Revolutionizing the Way Energy Companies Store and Use Seismic DataBluware designed InteractivAI not only with seismic workflows in mind, but also with an eye on the trends shaping the future of the energy sector. Creating a cloud-native data format makes it scalable for E&Ps to do more with their data while lowering costs and speeding up workflows, allowing them to arrive at more accurate decisions faster.
Are you ready to enter the next generation of subsurface data management and workflows?