The Current Status of Digital Oilfield in China

Intelligent technology has begun to be widely applied in the digital transformation of oilfield exploration and development. In the foreseeable future, intelligent technology will become the most core technology of digital transformation, which is also the key to the construction of intelligent oilfield. In the process of building smart oilfield, many experts have talked about what intelligence means in smart oilfield? What are the theoretical framework and architectural system of smart oilfield? But for specific business researchers, how to make artificial intelligence and big data technology in the field of intelligent oilfield landing, and ultimately into the scale of application, is the most worthy of attention.

After decades of technological development, the oilfield exploration and development industry has formed a fine industrial division of labor and a high degree of specialization requirements, and is extremely complex in terms of theory, academia, and industry applications. Oil and gas development has become one of the most complex and huge industrial systems in the world today, and only the most conventional oil and gas exploration and development work needs to go through a series of links, such as project evaluation, exploration planning, geological research, prospecting deployment, petroleum engineering, closed reserve management, oil and gas reservoir evaluation, development plan design, production capacity construction, production monitoring and optimization, and so on.

At present, in terms of software tools in the entire field of oil and gas exploration and development, there is already a set of all-field solution system in the international arena, and the corresponding product chain covers the application software of all business data and knowledge, and completes the closed loop of integrated application of seismic, geology, reservoir, engineering and so on. The oil industry solution providers represented by Schlumberger, Halliburton and Baker Hughes have formed a complete set of whole industry chain software development model from the underlying data model to the professional application software, and then to the whole business application closed loop. A unified industry data model and knowledge model, unified industry application software integration, and unified business collaboration covering the whole business ecosystem have been realized. The ecological environment of the industry is gradually forming.

Globally, the history of oilfield digitization and informationization construction is well over 20 years, and it is evolving from the original geological and mathematical models towards big data, artificial intelligence, knowledge management and cloud collaboration, with automation and intelligence becoming important features. In terms of data construction, the oil industry also has a complete set of data standard ecosystem in the international arena, for example, RPDS, PPDM, POSC and many other underlying data models have already provided a very complete definition of various data in the overall process of oilfield.

However, China’s system of defining many data within the oilfield is still very imperfect, and there is still a gap in geographic and mathematical models, and data standards and definitions are largely dependent on professional software from other countries. The lack of mastery and understanding of the core standards has made it difficult for China to carry out more in-depth application when utilizing big data and artificial intelligence to build digital oilfields.

Although there is a lot of research and application of big data and artificial intelligence technology in various fields of oilfield in China every year, the construction effect has never been able to meet the expectations. A large part of the reason lies in the lack of domestic professional data management and professional software construction, resulting in data, applications, overall architecture, business system does not form a set of perfect and unified system, and ultimately let the domestic petroleum industry’s application of artificial intelligence technology lags behind Europe and the United States and other countries.

The popularization of big data and artificial intelligence technology has ushered in a golden period for the industry application of artificial intelligence. However, our backwardness in the core technology of oilfield exploration and development, as well as the lack of in-depth integration of industry and technology, have seriously constrained the development of intelligent technology. We need to make up for our “short boards” as soon as possible, accelerate the improvement of our core technologies and products in the field of oil and gas development digitalization, as well as jointly build professional teams and specialized teams to achieve the integration of oilfield development teams and artificial intelligence teams.

Taking intelligent drilling as an example, the biggest demand in the drilling process is to reduce cost and improve efficiency, and the most critical of which is to increase drilling speed (ROP). Factors affecting drilling speed include: formation geology and lithology factors, drilling bit factors, wellbore tubing combinations and power units, drilling fluid configurations, and wellhead engineering and construction parameters. During the drilling process, it is necessary to consider the influence of several factors, such as different formations, drill bits, tubing and drilling fluids, and wellhead power systems, and to clarify the correlation between drilling speed and different parameters.

This needs to be changed. If the digital oilfield can be used in the domestic major oilfields, then the efficiency of oil production, output and even safety can be well guaranteed. At a time when artificial intelligence and new technologies are springing up, it is urgent to better develop the oil industry and oil industry-related software and hardware through new technologies.

https://youtu.be/cyG4VFXwwhw