Artificial Intelligence Comes of Age in Oil and Gas
- Gentry Braswell (JPT Online Technology Editor)
- Document ID
- Society of Petroleum Engineers
- Journal of Petroleum Technology
- Publication Date
- January 2013
- Document Type
- Journal Paper
- 50 - 57
- 2013. Copyright is held partially by SPE. Contact SPE for permission to use material from this document.
- 3 in the last 30 days
- 379 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||Free|
|SPE Non-Member Price:||USD 15.00|
Once the stuff of science fiction, artificial intelligence (AI) has become ubiquitous in our daily life, and the modern oil and gas industry is no exception.
Artificial neural networks, fuzzy logic, and evolutionary algorithms are common among AI techniques being applied today in oil and gas reservoir simulation, production and drilling optimization, drilling automation and process control, and data mining.
“Today, when you talk to information technology (IT) people, they mention four trends: social media, mobile devices, the cloud, and big data,” said Reid Smith, manager of IT Upstream Services at Marathon Oil, and a fellow of the Association for the Advancement of Artificial Intelligence.
Social media are in everyday use for collaboration; mobile devices are proving valuable in field operations; cloud computing has the potential to deliver cost savings and increased flexibility and performance in networking and data management; and hyperdimensional, complex, big data are well suited for analysis by machine learning, which is a key element of today’s applications of AI.
In addition to a large existing volume of historical oil and gas data, today’s increasingly complex upstream environments generate vast amounts of data for which the value is greatly enhanced with cutting-edge IT. “Some argue there is a substantial amount of oil to be found by applying new analysis techniques to data already on the shelf,” Smith said.
It is important to distinguish between data management and AI. SPE’s Artificial Intelligence and Predictive Analytics (AIPA) group was previously a subsection of SPE’s Digital Energy Technical Section. Now it constitutes its own technical section, Petroleum Data Driven Analytics. Essentially, digital energy is the curating—gathering, storage, and generation—of data, whereas AIPA involves using the data to perform tasks without human intervention, said Shahab D. Mohaghegh, professor of petroleum and natural gas engineering at West Virginia University, and founder of oil-field consulting and software company Intelligent Solutions, Inc. (ISI).
“There is a push in our industry toward smart fields,” Mohaghegh said. “There is a misconception in our industry today that equates automation with intelligence. Just because an operation is automated does not mean that it is smart. Without AI, you may have auto-mated fields, but you will not have smart fields. An automated field may provide the brain, but AI provides the mind. AI is the language of intelligence; it is what makes the hardware smart. To fully realize this, one must subscribe to a complete paradigm shift.”
A good modern example of the data-driven model outside the industry is credit card fraud detection and prevention tools that monitor consumer purchasing habits. “At ISI, that’s how we build reservoir management tools today,” Mohaghegh said. “We are now using the pattern recognition power of this technology to predict, manage, and design hydraulic fracture details in shale, using the hard data rather than soft data.”
|File Size||720 KB||Number of Pages||7|