The global oil and gas industry is currently standing at a critical crossroads of profound transformation, facing the dual pressures of energy transition and digital transformation. On the one hand, as easily recoverable resources are gradually depleted, exploration and development targets are rapidly shifting toward more complex domains such as deepwater, deep formations, and unconventional resources, where geological conditions and engineering operations are accompanied by significantly higher risks and uncertainties. On the other hand, the normalization of low oil price cycles and the rigid demand for cost reduction and efficiency improvement are compelling the industry to achieve a fundamental increase in total factor productivity through digital means.
However, although more than two decades of informatization have delivered certain achievements, they have also left the industry trapped in three deep-seated challenges, which have become major traps in the journey toward digital intelligence transformation:
1. Silos Effect: Professional systems have been built independently, causing business processes to break down at cross-disciplinary nodes and resulting in low collaboration efficiency. For example, a refined 3D geological model built by geologists in Petrel often needs to be dimensionally reduced or simplified when transferred to drilling engineers using Landmark for wellbore trajectory design. This loss of information directly leads to a disconnect between engineering design and geological understanding, invisibly increasing drilling risks and costs.
2. Data Islands: Due to the absence of unified enterprise-wide data standards, massive volumes of core data, such as well logging data, seismic data, and daily production reports, are locked within the proprietary formats of different software systems. These data assets cannot be effectively indexed, correlated, or queried, and therefore become dormant assets. As a result, high-quality enterprise data—the most critical fuel for AI in the era of artificial intelligence—has become an almost unattainable luxury.
3. Rigidity: Traditional monolithic IT architectures are slow to respond, and their construction and delivery cycles lag far behind the pace of business change. For instance, when a frontline operation area seeks to develop a simple mobile application for convenient entry of fracturing data, the closed nature of existing systems often forces the IT department to go through a months-long process of project approval, bidding, and development. This severely delays the response to frontline needs and creates a disconnect between technology and business.
These issues are not merely technical debt. They are architectural constraints that prevent the industry from effectively navigating the dual storm of energy transition and digital disruption.
In response to these challenges, this white paper puts forward a core vision: the oil and gas industry must fundamentally reconstruct its digital intelligence foundation and build a next-generation intelligent platform. This platform will no longer be a simple accumulation of traditional tools, but a new operating system capable of systematically addressing the above challenges. It will guide the industry across the gap and toward a new era truly driven by data and intelligence.
Fundamentally overcoming the industry’s challenges requires a new architectural paradigm: a true enterprise-grade operating system. The strategic core we propose is a dual-engine approach, which involves building both a Data Operating System and an Intelligent Operating System. Working in close coordination, these two systems form a complete closed loop from data foundation to intelligent applications.
The synergy mechanism of the “dual-engine” strategy ensures that data is no longer a dormant burden, but a flowing and value-accreting asset; intelligence is no longer an abstract concept detached from practice, but a core driving force capable of penetrating business operations and creating tangible value.
The methodological foundation for achieving this lies in our atomic decomposition of oil and gas business: five-dimensional business ontology modeling.
Five-dimensional business ontology modeling is the core innovation that differentiates us from traditional data platforms. It serves as the “common language” and “translator” connecting artificial intelligence with professional oil and gas business operations. By creating a “holographic profile” of business activities at the finest level of granularity, it fundamentally addresses the challenge of AI being unable to understand specialized data and workflows, thereby laying a structured blueprint for platform intelligence.
To accurately locate and describe any piece of data, tool, or activity within a complex business system, we have innovatively proposed a 4+1-dimensional business coordinate model. Like a GPS system, this model assigns a unique and computable business “address” to every digital resource within the enterprise.
Based on the business coordinates, we decompose complex business processes into a series of standardized and manageable Minimal Business Units (MBUs), also referred to as business nodes. These nodes serve as the “atoms” through which the platform schedules resources and attaches knowledge. To date, we have successfully identified and defined more than 16,000 business nodes across oil and gas exploration and development.
To create a holographic profile for each business node, we adopt the IPOMSQ framework for standardized description:
Taking the common business node of single-well composite columnar section drawing as an example, its IPOMSQ profile is as follows:
1. Input: Stratigraphic data, lithology data, well logging curves, formation descriptions, etc.
2. Process: Drawing tools and integrated mapping software.
3. Output: The finalized single-well composite columnar section, delivered in PDF or image format.
4. Management: Stratigraphic boundaries must be finally reviewed and confirmed by geological experts.
5. Standard: Lithology symbols, curve colors, line types, and other elements must comply with industry mapping standards.
6. Question: Potential issues may include abnormal splicing of well logging curves, depth-matching errors, and other technical challenges.
By defining each business activity at the atomic IPOMSQ level, we create a computable representation of the entire enterprise business system. This marks a fundamental breakthrough that enables AI—not only humans—to understand, orchestrate, and ultimately automate complex oil and gas workflows.
Based on the five-dimensional business ontology methodology, we have built the OiO (Oil in One) Integrated Intelligent Platform. This is not a mere evolution of existing IT systems, but a fundamental reshaping of the enterprise digital nervous system. Its overall architecture follows the design philosophy of data converging upward and intelligence empowering downward. By tightly coupling underlying data resources with upper-level intelligent applications, the platform forms an efficient, collaborative, and organic whole.
The core innovation of the platform architecture lies in its adoption of a dual-knowledge-graph system, which fundamentally addresses the coordination challenge between data governance and knowledge application.
1. KG0: Business Ontology Graph
This serves as the dictionary and rulebook for the entire industry. Based on more than 16,000 identified business nodes, it solidifies the rules, processes, and intrinsic relationships that should exist within oil and gas business operations. KG0 is defined by industry experts and remains relatively stable, forming the platform’s static skeleton.
2. KG1: Instance Resource Graph
This serves as the enterprise’s own digital twin and dynamic ledger. Using KG0 as its ontology schema, KG1 instantiates all digital resources owned by the enterprise, including data, tools, algorithms, standards, and cases, and accurately attaches them to the corresponding business nodes. If KG0 defines the concept of a well in the dictionary, then KG1 contains the specific and living records of “Well A01,” including its real-time production data, maintenance history, and geological background. KG1 evolves dynamically and forms the platform’s dynamic flesh and blood.
The platform’s brain is an intelligent hub that integrates advanced AI capabilities. It is responsible for understanding user intent, orchestrating tasks, and driving business execution.
1. JuraSeek: Industry Large Model
JuraSeek is built upon a general-purpose large language model and enhanced with an external full-business knowledge graph. It deeply understands industry terminology, such as porosity, permeability, saturation, and dogleg severity, as well as complex business logic. It provides the foundation for domain-specific cognitive intelligence.
2. JuraRAG: Retrieval-Augmented Generation
To address the hallucination problem of large models and ensure the professionalism and accuracy of generated content, the platform adopts an RAG architecture. When a user asks a question, the system first performs precise retrieval within the Instance Resource Graph (KG1). It then provides the retrieved factual evidence, such as the actual production data of a well or the fracturing plan of an adjacent well, as context for the large model. This ensures that the generated answer is well-founded, evidence-based, and traceable.
3. JuraAgent: Autonomous Agent
JuraAgent serves as the platform’s execution unit. Based on the ReAct framework, which enables an agent to first create a logical plan through reasoning before taking action, thereby simulating the problem-solving process of human experts, JuraAgent can autonomously understand complex business tasks, such as analyze the remaining oil potential of Block X. It can break the task down into multiple steps, automatically invoke the tools attached to the knowledge graph, and execute them in sequence to ultimately complete the task.
JuraX is the core capability platform that connects general-purpose large models with enterprise-specific professional data. Its key role is to use the five-dimensional business ontology as a translator, enabling large language models to accurately understand, query, and operate specialized oil and gas data without producing factual errors or hallucinations. JuraX is the technical key to enabling large models to truly understand oil and gas business operations.
Driven by the business ontology, the platform has built three foundational components to support upper-level intelligent applications:
1. JuraData: Full-Business Data Management Platform
Based on the business ontology, JuraData enables the automatic aggregation, cleansing, standardization, and governance of multi-source heterogeneous data, transforming raw data into usable data assets.
2. JuraComponents: Full-Component Tool Management Platform
JuraComponents decouples traditional large-scale professional software into a series of independently callable and reusable business components according to business nodes, such as curve depth correction components and decline analysis components.
3. GeoMapPro: Full-Business Visualization Platform
GeoMapPro breaks through the limitations of traditional GIS platforms and enables an integrated “map-data-business” holographic perspective. Any object on a geographic map or model can be directly linked to the underlying data and business activities behind it.
This integrated technical architecture successfully applies the cutting-edge model of “large models + knowledge graphs + agents” to the oil and gas industry, creating unprecedented core value for driving comprehensive paradigm transformation.
The value of the next-generation intelligent platform lies not only in technological innovation but also in its ability to fundamentally reshape operational methods, decision-making processes, and knowledge management across the oil and gas industry, thereby driving a profound paradigm transformation.
In traditional workflows, exploration, development, engineering, production, and other business links operate like isolated islands. Data and deliverables are transferred manually through file copying, resulting in numerous process breakpoints and low collaboration efficiency.
Under the new model, guided by the KG0 process graph and supported by the tools indexed in KG1, JuraAgent acts as an “intelligent dispatcher” running through the entire business process. It seamlessly connects different professional domains and enables closed-loop intelligent collaboration across geology, engineering, and economics, thereby fundamentally breaking down disciplinary barriers.
The new model puts our “dual-engine” philosophy into practice. It integrates the industry-wide knowledge embedded in the JuraSeek large model with the enterprise-specific real-time dynamics captured in the KG1 instance graph, providing managers with forward-looking risk warnings, production optimization recommendations, and investment benefit analysis.
This enables a fundamental shift in decision-making from post-event response to pre-event prediction, making decisions more scientific, agile, and informed.
The oil and gas industry commonly faces the challenges of talent gaps and the loss of experts’ tacit knowledge. The new model solidifies valuable expert experience within the business ontology graph (KG0) and the industry large model, while preserving enterprise success cases and accumulated data within the Instance Resource Graph (KG1).
Together, these elements form a dynamically evolving and widely accessible “organizational intelligence brain.” It not only consolidates and disseminates core knowledge assets but also fundamentally shortens the growth cycle of the next generation of talent.
Under the traditional model, various professional software systems exist in isolation, creating difficult-to-overcome “tool barriers.” Through five-dimensional business ontology as a common “gear system,” the new platform enables, for the first time, the seamless coupling of large models with general reasoning capabilities and specialized algorithmic models, such as seismic interpretation and reservoir simulation models.
This fully opens the channel between artificial intelligence and enterprise data resources, building a truly unified, collaborative, and composable oil and gas intelligent platform.
This comprehensive transformation represents the inevitable path for the oil and gas industry to achieve high-quality and sustainable development in the new era.
The ultimate value of theory and architecture lies in their ability to solve real business problems. This chapter demonstrates the application value of the OiO platform through two core business scenarios in the oil and gas industry.
· Business Pain Points:
Mature oilfields have generally entered stages characterized by high water cut and high recovery degree. They face severe challenges such as rapid production decline, unclear remaining oil distribution, and long, inefficient cycles for manually adjusting injection-production schemes.
· OiO Solution:
1. Intelligent Diagnosis of Operating Conditions
The platform uses algorithms such as convolutional neural networks (CNNs) to automatically identify real-time dynamometer cards transmitted from pumping units. It can accurately diagnose abnormal pumping conditions such as insufficient fluid supply, gas interference, traveling valve leakage, standing valve leakage, and paraffin deposition, with a diagnostic accuracy of over 95%. It can also automatically generate maintenance work orders and precisely push them to frontline operation teams through mobile terminals.
2. Intelligent Analysis of Remaining Oil Potential
After receiving the instruction to analyze remaining oil potential, JuraAgent can automatically invoke historical production data, well logging interpretation results, and reservoir numerical simulation models from the knowledge graph. Combined with the comprehensive analytical capabilities of the large model, it can rapidly identify remaining oil enrichment areas and present them in the form of visual maps.
3. Injection-Production Linkage and Automatic Scheme Optimization
Based on graph neural network technology, the platform deeply analyzes injection-production conflicts within the well pattern. It can automatically generate injection allocation adjustment recommendations for each water injection well, enabling refined water injection management. It can also predict the expected production uplift after scheme implementation, for example, a projected 5% increase, thereby supporting engineers in making efficient decisions.
· Business Pain Points:
Under the traditional model, the workflows of exploration and development departments are disconnected. When a 3D geological model built by the exploration department is handed over to the development department, information loss or dimensionality reduction often occurs, forcing development personnel to spend substantial time rebuilding models. This not only reduces efficiency, but also easily leads to inconsistencies between well location deployment and geological understanding, significantly increasing drilling risks.
· OiO Solution:
1. Unified Geological Framework and Model Version Management
The platform establishes a unified “geological framework library” to enable real-time synchronization and version control from exploration models to development models. Similar to the Git mechanism used in software development, it supports differential comparison between different versions of geological models, ensuring seamless inheritance and iteration of geological understanding.
2. Intelligent Fusion of Multimodal Data
The platform can automatically integrate multi-source heterogeneous data, including seismic, well logging, mud logging, and core data. By leveraging the strong pattern recognition capabilities of large models, it assists geological experts in identifying subtle faults or favorable sedimentary facies belts that are difficult to detect using traditional methods within massive seismic data volumes, thereby improving reservoir prediction accuracy.
3. Intelligent Recommendation of Well Location Deployment
JuraAgent can comprehensively integrate geological models, risk maps, successful adjacent-well cases, and economic evaluation models to automatically generate multiple candidate well location deployment schemes. At the same time, it can compare the advantages and disadvantages of each scheme, including predicted productivity, drilling risks, and return on investment, providing strong intelligent support for experts’ final decision-making.
These scenarios represent only a small part of the platform’s capabilities. They demonstrate the tremendous potential of the next-generation intelligent operating system in empowering core oil and gas business operations and improving productivity.
Intelligent transformation is not a one-time big bang project. Rather, it is a systematic transformation that requires structured planning, phased implementation, and continuous evolution. Based on industry practice, we propose a pragmatic and progressive three-step implementation path to ensure steady progress, controllable risks, and continuous value realization.
Timeline: 6–8 months
Objective:
Build an enterprise-level digital intelligence foundation, enabling core data to be ingested into the data lake and supporting visualized queries for key business operations.
Core Tasks:
Deploy a private cloud environment; build an enterprise-level business ontology graph (KG0) to unify data standards; complete the cleansing, standardization, and ingestion of core historical data, such as data from the past five years; and launch a unified data query portal and multi-dimensional visualization cockpit.
Expected Outcomes:
Break down core data islands and establish a clear understanding of the enterprise’s data assets.
Timeline: 12–18 months
Objective:
Gradually replace legacy systems and enable online collaboration across core business processes, while accumulating common capabilities as reusable components.
Core Tasks:
Use the new platform to reconstruct and replace legacy monolithic systems, such as production daily reporting and drilling reporting systems; encapsulate common business capabilities, such as geological mapping and decline analysis, into reusable business components; and enable comprehensive online collaborative operations across core business workflows, including exploration, development, and engineering.
Expected Outcomes:
Eliminate “siloed systems,” improve business collaboration efficiency, and accumulate rich online data and capability components for intelligent applications. This will directly improve business efficiency and generate measurable results, such as a 30% increase in the processing efficiency of new well investment approval.
Timeline: Continuous evolution
Objective:
Deepen AI applications and achieve a fundamental shift of artificial intelligence from assisting business to driving business.
Core Tasks:
Promote intelligent applications such as JuraSeek, an intelligent search engine, and JuraAgent, an intelligent assistant, across the entire company; explore frontier autonomous intelligence scenarios in mature domains, such as autonomous drilling and intelligent production enhancement; and establish a continuously iterative operation mechanism for models, algorithms, and knowledge bases.
Expected Outcomes:
Fully enter the era of intelligent operations and achieve a leapfrog improvement in productivity.
The core argument of this white paper is that a next-generation intelligent operating system, built upon the foundation of the five-dimensional business ontology and deeply integrating the triad of large models, knowledge graphs, and agents, is the inevitable choice for the oil and gas industry to address current challenges and achieve leapfrog development.
This architecture provides a fundamental solution to the industry’s deep-rooted challenges. It breaks down disciplinary barriers through intelligent agents, transforms data islands into an enterprise digital twin through a unified knowledge graph, and replaces rigid legacy systems with a flexible and adaptive core.
The ultimate vision of our platform is to enable large models to truly “understand” oil and gas business operations. By precisely defining every business activity at the atomic level, we have unlocked the path toward an intelligent system capable of autonomously supporting oil and gas exploration and development.
The future has arrived. A new era of a more intelligent, efficient, and collaborative oil and gas industry is rapidly approaching. Let us embrace this transformation together and move toward a new era of autonomous intelligence in oil and gas exploration and development.
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