[China Petroleum News] AI Reshaping the Future of the Oil and Gas Industry

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The oil and gas industry, being a heavy asset, high-investment, and high-risk sector, is characterized by both technological and labor intensity, with the constant need to prevent safety accidents and ecological disasters. Facing the influx of AIGC (Artificial Intelligence Generated Content), how the oil and gas industry can quickly respond and complete digital transformation and intelligent integration in time has become a significant topic of our era.

The urgent need for AI in the digital transformation of oil and gas enterprises

On May 8, 2024, the internationally renowned academic journal "Nature" published a structural biology paper stating that the latest iteration of the artificial intelligence model AlphaFold3, developed by the teams at Google DeepMind and Isomorphic Labs, can predict complex structures composed of DNA, small molecules, ions, and proteins. The advent of this model shocked the world, making scientists realize the immense potential of AI to achieve goals once considered unreachable. As the core technology of the new industrial revolution, the integration of AI and the oil and gas industry has increasingly attracted the attention of oil and gas companies.

In fact, since November 30, 2022, when the American company OpenAI released ChatGPT, various pre-trained large language models have been continuously emerging both domestically and internationally. ChatGPT itself has also undergone iterative upgrades, and on February 16, 2024, OpenAI released its large-scale model tool Sora, which can create realistic and imaginative scenes based on textual instructions. Currently, AI, especially AIGC, has attracted widespread attention from all sectors of society due to its powerful capabilities.

The application scenarios of AIGC are extremely diverse, with conversational chatbots being just one of its public manifestations. Behind its technological logic lies robust computational power, memory, comprehension, problem-solving capabilities, and a vast knowledge base encompassing various industries or specific domains. This enables it to become a universal assistant for open-ended tasks. Its capabilities in creative writing, strong contextual understanding, generating control instructions based on human logic, and controlling drones or complex robots have already demonstrated its global and disruptive power, profoundly impacting various aspects of human production and life.

From the perspective of socio-economic development, AIGC holds greater value and broader prospects in the field of enterprise-level services. In particular, AIGC provides new pathways for the digital transformation and intelligent development of numerous traditional industries. So far, numerous traditional industries such as finance, transportation, and healthcare are actively introducing AIGC technology to significantly enhance service efficiency and trigger continuous evolution and transformation in related business models.

The oil and gas industry, as a heavy asset, high-investment, and high-risk sector, is characterized by both technological and labor intensity, with constant vigilance required to prevent safety accidents and ecological disasters. Facing the influx of AIGC, how the oil and gas industry can quickly respond and complete digital transformation and intelligent integration in time has become a significant topic of our era.

The upstream of the oil and gas industry is a discrete industry, while the downstream is a process industry, lacking the characteristics of digital native enterprises. In the era of digital economy and generative AI, the boundaries of the role of ecological partners and the flow of information and data between them are undergoing fundamental changes. Under the strong constraints of the "dual carbon" target, how to achieve green, low-carbon, and sustainable development is a severe challenge faced by oil and gas enterprises as well as the petroleum and petrochemical industry. Digital transformation and intelligent development have become crucial choices for oil and gas enterprises to overcome difficulties. The digital and intelligent transformation of oil and gas enterprises urgently requires the powerful support of new-generation AI and other technologies.

Overcoming challenges in the digital transformation of the oil and gas industry

Driven by the digital wave, the intelligent development of industries has become a crucial step in the digital transformation of the industrial sector. However, ultimately, the implementation and development of industrial intelligence require understanding the underlying logic of digital intelligence. In previous research, the author and the team proposed a "four-world model" to clarify this underlying logic. The first world is the physical world we live in, the second is the human cognitive world, the third is the machine cognitive world, and the fourth is the digital world constructed through digitization.

The logic behind this is: in artificial intelligence technology primarily driven by data-driven deep learning, people acquire data and conduct digital twin modeling through sensors' ubiquitous perception of the physical world to build a digital world. Then, they use machine learning algorithms to automate repetitive work based on explicit mechanisms and explore new knowledge boundaries by discovering correlations in the digital world through inferences based on unclear mechanisms. Additionally, they achieve comprehensive lifecycle cognition, prediction, optimization, and closed-loop control of local or the entire physical world through the mapping interaction between the physical and digital worlds and the intelligent sharing of "composite twins."

From the physical world to the digital world, digital twin modeling is required and follows Shannon's sampling theorem, involving the understanding, abstraction, and perceptual sampling of the physical world. Therefore, the construction of the digital world can only be staged, considering issues such as hierarchy, compatibility, certainty, completeness, stability, and interpretability. Furthermore, the path from the first world (physical world) to the fourth world (digital world) and then to the third world (machine cognition world) is decoupled from the existing organizational (enterprise) systems and mechanisms, meaning that the digital world exists independently from existing organizations and their systems and mechanisms, potentially encountering various personnel obstacles. Hence, the digitization and intelligent transformation of traditional industries like oil and gas face significant challenges.

AIGC will be widely applied in the oil and gas sector.

AIGC represents a significant development and substantial breakthrough in artificial intelligence technology primarily driven by data-driven deep learning. It directly transitions from the second world (human cognitive world) through literature learning and semantic understanding to the third world, forming a machine cognitive world. This new path is coupled with the existing organizational (enterprise) systems and mechanisms, closely linked to them, effectively avoiding various personnel obstacles. At the same time, this new path also effectively addresses issues encountered in the construction of the digital world, such as staging, hierarchy, compatibility, certainty, completeness, stability, and interpretability.

We have reason to believe that utilizing generative artificial intelligence and rapidly completing cognitive iterations based on powerful computing power can efficiently generate various required content and solutions in various links and scenarios of oil and gas geology, geophysics, well logging, drilling and completion engineering, oil and gas reservoir engineering, oil and gas well production surface engineering, oil and gas storage and transportation, refining chemicals, and petrochemicals. It is a powerful tool to enhance oil and gas industry production efficiency and connect with the digital economy. Without exaggeration, the emergence of large language models and the rise of AIGC have brought fundamental changes to the digital transformation path of the oil and gas industry, and it is bound to be widely applied in oil and gas exploration and development in the future.

The application prospects of AIGC in the oil and gas industry are vast but also challenging. In 1859, the first underground oil was obtained through drilling in Pennsylvania, the United States, marking the beginning of the modern petroleum industry. Since then, the general knowledge, regional knowledge, mechanistic models, exploration data, and production data in the oil and gas field have grown rapidly and accumulated continuously, with continuous iterative upgrades in their business processes and gradual coordination and optimization of their business and value chains. Today, a comprehensive knowledge system and rigorous industry standards have been established in the oil and gas field, laying a solid foundation for the research and application of AIGC in the oil and gas industry.

However, it should also be noted that the business logic of the oil and gas industry is highly complex. Taking the upstream oil and gas industry, i.e., the core business of exploration and development, as an example, it typically involves multiple stages such as resource exploration, resource evaluation, oil and gas discovery, oil and gas reservoir evaluation, development and production, and abandonment of oil and gas fields. Each stage involves comprehensive research in data collection, processing, interpretation, and application. Core oil and gas business enterprises are often "research-oriented production enterprises" or "production-oriented research companies," with continuous interleaving and iteration of scientific research and production. Each stage also involves project management, including planning, project cost, investment budget, production operation, quality supervision, safety supervision, project acceptance, project settlement, and other aspects. In addition, it also involves corporate operations in various aspects such as financial management, human resources, equipment management, material supply, legal affairs, production sales, and customer relationships. In the industrial economic system that achieves large-scale development through professional and technical division of labor, it is necessary to emphasize clear responsibilities for each link. The institutional mechanisms and continuous information construction in the past have led to the existence of a large number of information silos, data barriers, and technical secrecy. Therefore, in the process of AIGC pre-training literature learning and semantic understanding, distinguishing the true from the false is a very difficult task. In the process of continuous construction, although supported by the top-level design of the country, the leadership and executive levels are still mainly observing, with little substantial progress. At present, there is a lack of high-level talent in the field of artificial intelligence, and there is a barrier between oil and gas business experts and AIGC experts, leading to poor communication and overly simplified business scenario construction, which greatly increases the difficulty of implementing digitalization and intelligence in the oil and gas industry. All of these should be practically taken into account when planning and implementing large-scale generative artificial intelligence projects and decisions in the oil and gas industry.