Data + Upstream, where is the road

Data is a key factor of production in the era of digital economy, a key outcome of the third Industrial Revolution, and an important foundation for the fourth Industrial Revolution. Data assets have become the core competitiveness of enterprise development, it is imperative to strengthen the precipitation and construction of data assets. With the acceleration of the process of digital transformation, data science and big data technology have become the core engine for the digital transformation and intelligent development of related industries.

The application of data science and big data technology, mining the value of data assets in the oil and gas field, providing efficient data and integrated services to support scientific research and decision-making management in the oil and gas exploration and development field is of great significance to promote the digital transformation of the oil and gas field, and is the only way for Chinese oil and gas enterprises to become first-class international oil companies.

New opportunities for digital transformation of oil and gas enterprises

 On April 20, 2020, the National Development and Reform Commission clearly included data centers as information infrastructure into the category of "new infrastructure". With the country's emphasis on strategic emerging industries and the proposal of the "new infrastructure" task, data centers have ushered in new development opportunities. In the future, a number of national data centers will be built successively. It is expected that by 2030, the scale of China's data native industry will account for 15% of the entire economy, the overall scale of data will exceed 4YB, accounting for 30% of the global data, and data science and big data-related applications will enter the rapid development track.

China's oil and gas exploration as a whole is in the middle stage of exploration. In recent years, the newly discovered large-scale oil and gas reserves are mainly concentrated in ultra-low permeability, deep depth and unconventional fields. How to apply data science and big data technology to improve the accuracy of structural interpretation, the conformity rate of reservoir interpretation and the success rate of geological target drilling is an important means to consolidate the foundation of long-term stable crude oil production and steady growth of natural gas. At present, China's petroleum enterprises have built a series of information management systems to realize the effective management and application of structured data, but for the lack of management systems such as various reports, articles and results, data assets have not been really established, and massive data still cannot be open and shared, which cannot effectively meet business applications. The integration of data science and big data technologies with E&P operations will provide new opportunities for digital transformation, high-quality development and the realization of strategic objectives in the oil and gas sector.

Data + upstream application roadmap

At present, the application of data science and big data technology has made some achievements, but it also faces many challenges. First, the application of data science and big data technology requires high-quality and all-round data support, and data governance is crucial. Second, the fragmented data in the upstream oil and gas field still need to be deeply integrated, especially the research and use of new generation information technology such as knowledge graph to achieve the integration of multi-source heterogeneous data, so as to build a complete knowledge system. Third, for different business applications, business applications of different organizations, and data, there is no unified platform management and control, and it is difficult to mine data value from the global level. Services can be built through the data center, and professional data association relationships can be established to realize the interoperation and sharing of applications and data. Therefore, the construction of data governance, knowledge graph, and data center in E&P will become the core of digital transformation in upstream fields.

First, attach importance to data governance and provide high-quality and comprehensive data sources. Over the years, Chinese oil companies have been committed to building a data governance system with clear classification, reasonable storage, efficient use, and sustainable improvement, including security mechanisms and data management. Taking petrochina as an example, in the upstream oil and gas field, in order to strengthen the enterprise-level management of geophysical data exploration areas, centralized management of the basin where mineral rights transfer blocks are located, and remote backup management, the construction of cloud data centers for geophysical data is carried out, and data and map data are uniformly controlled and managed to achieve comprehensive and complete management of bulk data in the upstream oil and gas field. In accordance with the principle of "two unified, one universal", the exploration and production sector builds a dream cloud platform, and gradually integrates multi-level and multi-dimensional data of petrochina's upstream specialties through the construction of a data lake to meet the needs of scientific research, production and business management of upstream oil and gas business.

Data governance is the primary link of data system construction. It is suggested to strengthen data governance, and through the construction of data governance system, make these professional data available, easy to use and good use, so as to build high-quality, full-life cycle data assets in the upstream field of petroleum enterprises, and provide all-round data support for business research and operation management in the field of exploration and development.

Second, the construction of domain knowledge map, to carry out intelligent application exploration. The key to the transformation of data into assets in upstream oil and gas field is to construct the knowledge system of oil and gas data assets through the construction of exploration and development knowledge map. With the rapid development of artificial intelligence, especially deep learning and natural language processing technology, knowledge graph has shown rich application value in assisting intelligent question answering, natural language understanding, big data analysis, intelligent recommendation, Internet of Things device interconnection, interpretable artificial intelligence and so on. Knowledge graph technology can reduce the threshold for professionals to use knowledge and shorten the time of knowledge retrieval and investigation. It can quickly discover and tap the value of knowledge, and improve the efficiency of exploration and development decisions. Therefore, it is of great practical significance to realize the automatic management, intelligent retrieval and multi-dimensional analysis of exploration and development data by using the related technology of knowledge graph, and to apply it to various practices in oil and gas field.

To meet the scientific research and production requirements in the field of oil and gas exploration and development, the design and construction of the exploration and development knowledge map should comply with the actual business requirements, combine the characteristics of multi-professional and multi-disciplinary collaboration in the upstream field, consider the domestic scientific research and business model in the field of oil and gas, and construct the domain knowledge map from the geological perspective with basins and oil and gas reservoirs as the main line. The construction process includes knowledge system classification, ontology model construction, named entity recognition, relation extraction and knowledge fusion.

It is suggested to speed up the construction of knowledge map in the whole field of exploration and development, compile the construction standard of knowledge map in the field of exploration and development, complete the construction of knowledge map in the upstream field of oil and gas by co-construction mode, and comprehensively carry out the dual-drive exploration practice of "data + knowledge" in the upstream field, intelligently solve professional problems, and lead the development of "third generation artificial intelligence" technology.

Third, research and development of exploration and development data center, modular reuse service actual business scenarios. Data center refers to the use of a new generation of information technology to collect, calculate, store and process massive structured and unstructured data, and at the same time unify standards to form a big data asset layer, and then provide efficient data services that are strongly related to business, unique to enterprises and reusable. Application practice of data science and big Data technology Through the data center, to achieve the construction and application of data technology capabilities and data assets.

The data service is built through the data center to realize the interoperability and sharing of applications and data. The data in the application system will be split, decouple, and encapsulated into services, and form a new operation management logic, break the information island, achieve application integration and function modularization, service-oriented agile development, and realize business data and data business, to meet the needs of collaborative research and business application.

The field of exploration and development has complex dependence and correlation in terms of both professional and data, and the same kind of data will support services in different business scenarios. Therefore, it is suggested to comprehensively promote the construction of exploration and development data center, unify data, unify identification, refine business scenarios, and develop standard application modules to achieve reuse and support for different business scenarios. Paving the way for the business value of data assets.

In the future, through the in-depth application of data science and big data technology, a digital world of full perception, full link, full scene and full intelligence will be built in the upstream field of oil and gas, and then the business of the physical world will be optimized and reconstructed, and the traditional management model, business model and business model will be comprehensively innovated and reshaped, and the digital ecology will be fully built and digital innovation will be realized. Enhance the competitiveness of enterprises. (Zhou Xiangguang, Information Technology Center, Research Institute of Petroleum Exploration and Development, China)

International oil companies' digital upgrade is remarkable

Shell: Digital twin technology

Shell has been bullish on digital twin technology. Last September, Akselos deployed structured digital twin technology for Shell's Bonga Main floating production storage and offloading tanker in Nigeria. In October, Shell signed a partnership with Aveva to support the use of digital twins in managing asset life cycles through the creation of an engineered data warehouse.

Eni: Supercomputing and algorithms

Eni sees big data processing capabilities as a competitive advantage and introduces a powerful computer for industrial use, the HPC5 supercomputer. In addition to strengthening industrial data processing capabilities, Eni also focuses on the fields of artificial intelligence, human-computer interaction, industrial Internet of Things, robotics and additive manufacturing, and blockchain technology.

ADNOC: Panoramic digital command Center

ADNOC established Panorama Digital Command Center, which aggregates real-time information from 14 specialized subsidiaries and joint ventures to predict a series of operational scenarios through intelligent analytical models, artificial intelligence and big data, and give effective operational insights and recommendations. Continued investment and building on digital transformation over the past few years has made ADNOC more resilient and adaptable in the current industry environment.

Schlumberger: Machine Learning and cloud computing

Schlumberger continues to strengthen its machine learning and cloud computing capabilities. He has worked with AIQ and G42 in ABU Dhabi on the development and deployment of AI, machine learning and data solutions for the oil and gas industry. Partnering with IBM's Red Hat to combine hybrid cloud computing with the oil and gas industry to create a digital platform for the future; We have also worked with companies such as Google and Microsoft to support the delivery of machine learning services to their customers.

Halliburton: Digital supply chain

Halliburton has signed strategic agreements with Accenture and Microsoft to help boost its digital capabilities on Microsoft's Azure cloud. Under the agreement, Halliburton will enhance the reliability and security of Halliburton's overall systems by enhancing the real-time platform of remote operation extension, leveraging machine learning and artificial intelligence to improve the analytical capabilities of the database, accelerate the application of new technologies, and improve the reliability and security of Halliburton's overall systems. Halliburton has also partnered with Accenture to accelerate digital supply chain transformation. Enhance the visibility and operability of real-time supply chains with AI analytics for greater transparency and faster decision making.