Install "super intelligent brains" on oil and gas fields

The digital economy is booming, and the digital transformation of the industry is accelerating.

As a typical traditional industry, the oil and gas industry has a huge amount of resources, hundreds of billions of assets, and millions of employees; at the same time, it also has high professionalism, complex process flows, long industrial chains, and total equipment assets.

The former puts forward demands for the digital transformation of the industry, while the latter adds difficulty to the digital transformation.

"Under the wave of digital economy transformation, the oil and gas industry is facing the difficulty of turning around the ship.

How to deepen the integration of big data artificial intelligence technology with oil and gas companies and cultivate new opportunities for the development of the industry? The answer given by Gridsum Oil and Gas is: through the implementation of the three core capabilities of computing power, algorithms, and scenarios, people are routinely operated from the economic society Freed from tedious work, by creating a "hybrid industrial intelligence" solution, the full integration and complementarity of humans and machines can be effectively utilized to help vertical industry enterprises in their digital transformation.

Intelligent analysis and prediction has become an important starting point

In Xue Xiaoqu's view, the oil and gas industry's need for digital technology is urgent and lasting: on the one hand, the difficulty of oil and gas development is increasing, and the situation of continuous stable production is severe.

"Issues such as this will continue to exist in the next few years, posing new challenges to the production, operation, management, and decision-making of the oil and gas industry.

"Artificial intelligence technology can inject new kinetic energy into the innovative development of oil and gas fields, and intelligent analysis and prediction technology has become an important means to solve oil and gas research and production problems.

　　According to reports, the Gridsum research team scientifically manages a large number of research results and business models accumulated over the years, and analyzes and processes the exploration and development knowledge in structured and unstructured data through knowledge graph technology: first through industry experts Labeling, gradually training and optimizing machine learning algorithms, and then realizing automatic identification and labeling, successfully building the first domestic oil and gas industry knowledge base based on automatic knowledge extraction and knowledge map architecture, laying the foundation for the application of cognitive computing in exploration and development, and forming oil and gas exploration and development "Super Intelligent Brain".

　　At the same time, Gridsum has further created intelligent oil and gas field overall solutions including intelligent oil and gas reservoirs, intelligent wellsites, intelligent pipelines, intelligent oil and gas production control, oil and gas knowledge sharing and other business application scenarios.
Institutions and other customers have conducted in-depth cooperation, and technological innovations have been fully verified.

　　"Hybrid Industrial Intelligence" is a feasible way

　　The oil and gas industry is one of the scenarios where artificial intelligence technology empowers the digital transformation of traditional enterprises.
According to Liu Jiyang, chief technology officer of Gridsum, if you want to truly apply AI capabilities to specific scenarios for industrial use, you need the blessing of "hybrid industrial intelligence" .

　　He said that "hybrid" firstly refers to the mixture of perceptual intelligence and cognitive intelligence.
The former refers to perceptual intelligence such as computer vision and speech recognition, while the latter refers to making machines realize cognitive abilities through the learning, accumulation and application of knowledge like humans.
"To achieve this, computers must be given to understand language, The ability to learn knowledge, accumulate experience, use relevant knowledge and experience to reason, and solve real-world problems".

　　The second is the mixing of data and knowledge.
Liu Jiyang said that in order to effectively use the large amount of data accumulated in vertical industries, it is necessary to have a deep understanding of business scenarios.
Only by combining data and knowledge can we truly understand the specific businesses of vertical industries and meet the needs of industry customers.

　　The third is a mix of industry experts and data scientists.
Industry experts are responsible for providing knowledge and experience in the industry, while data scientists use this knowledge to do in-depth data mining and build effective models.
The application of artificial intelligence to the industry requires a high degree of collaboration between the two.

　　"Whether it is smart courts, smart energy, smart parks, or smart cities, if you want to realize the scene-based implementation of artificial intelligence in the industry, you need'mixed' capabilities and >

　　Xue Xiaoqu also said that in order to apply artificial intelligence technology to industry scenarios, especially in traditional and complex industries such as oil and gas, technical reserves, industry experience accumulation, artificial intelligence thinking logic, and rooted real application scenarios are indispensable.
This is not only the experience of artificial intelligence technology landing in the oil and gas industry, but also a paradigm that can be extended.
(Reporter Cui Shuang)

Transfer from: Science and Technology Daily

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