Amid the accelerating wave of digital transformation in the oil and gas industry, artificial intelligence (AI) has been entrusted with high expectations to drive paradigm shifts in exploration and development, production operations, and decision support. However, the path to true integration is far from smooth. As highlighted in the industry’s “Upstream Informatization Top-Level Design” documents, three long-standing core pain points—deficiency, redundancy, and isolation—have collectively created a significant gap between AI technologies and core oil and gas business processes. This gap has become a strategic obstacle limiting the industry’s ability to unlock new forms of productivity.
This gap manifests in three major challenges:
1.Data Silos: Oil and gas exploration and development generate massive volumes of data, yet these datasets are often inconsistent and dispersed. From geological reports to logging curves, from production data to equipment logs, unstructured, semi-structured, and structured data coexist in a fragmented manner. Consequently, AI models cannot form a unified and effective understanding, preventing valuable data assets from being converted into actionable intelligence.
2.Tool Barriers: The industry heavily relies on various specialized software (e.g., Petrel, Eclipse). While these tools are powerful, their workflows are rigid, forming strong technical barriers. AI, especially large language models (LLMs), cannot directly interpret the operational logic of these tools, let alone invoke their functions to execute specific business tasks, resulting in a disconnect between intelligent technologies and existing workflows.
3.LLM Limitations: Although general-purpose large language models demonstrate strong capabilities in reasoning and language understanding, they know what but not why. Lacking deep domain knowledge in oil and gas and being completely detached from enterprise-specific, real-time data, these models cannot accurately comprehend professional problems. Their outputs are often superficial and may even generate misleading hallucinations, rendering them insufficient for addressing practical challenges in exploration and development.
Bridging this gap requires more than simple technical overlays or isolated breakthroughs. What the industry truly needs is a profound paradigm shift at the infrastructure level—one that fundamentally reshapes how data and intelligence are connected.
The core vision of this white paper is to establish a unified, intelligent industry operating system that fundamentally addresses the aforementioned AI–business “gap.” This is not merely a technical solution but a strategic transformation, aimed at elevating enterprise operations from a process-driven model to a data- and intelligence-driven paradigm. We propose a Dual-Engine Strategy, consisting of a Data Operating System and an Intelligent Operating System, jointly powering the next-generation intelligent transformation of the oil and gas industry.
1.Data Operating System (DOS): Serving as the unified foundation of the entire architecture, its primary mission is to transform massive volumes of unstructured raw enterprise data into structured, computable, and business-logic–aligned enterprise assets through an innovative Business Ontology modeling technology. It is designed to break down data silos, govern data quality at the source, and convert chaotic data into orderly, usable digital oil, providing a solid data foundation for upper-layer intelligent applications.
2.Intelligent Operating System (IOS): Built atop the Data Operating System, this represents an advanced cognitive layer. By deeply integrating general-purpose intelligence from large language models (LLMs) with proprietary business ontology, it equips AI with a true business brain. Within this operating system, AI can not only interpret natural language commands but also comprehend the underlying business intent, autonomously invoking data and tools to execute a range of complex tasks—from automatically generating research reports to analyzing and resolving production issues.
The cornerstone of this dual-engine architecture is an innovative business modeling technology that provides a unified language and framework for the seamless collaboration of data and intelligence.
The technical cornerstone and core innovation enabling the aforementioned vision is our proposed Five-Dimensional (5D) Business Ontology modeling technology. This technology represents a fundamental strategic shift: moving from treating data as a byproduct of business operations to constructing business architecture around computable knowledge itself. By providing an unprecedented atomic and holistic description of oil and gas business processes, it fundamentally reshapes the industry’s information architecture and lays a solid foundation for the deep integration of data and AI.
At the heart of the 5D Business Ontology technology is the decomposition of complex oil and gas exploration and development workflows into a series of indivisible, minimal work units—referred to as Business Nodes. Each business node represents a specific, independent business activity. To date, we have successfully identified and mapped over 16,000 oil and gas exploration and development business nodes, forming a comprehensive system that fully covers industry knowledge.
This technological innovation has three key characteristics:
1.Precision: Using a holographic description framework, each business activity’s inputs, processes, outputs, and associated constraints are precisely defined, ensuring the accuracy and correctness of business representations.
2.Scale: Mapping over 16,000 business nodes guarantees comprehensive coverage across the entire value chain—from geological research to drilling engineering and production management.
3.Granularity: Breaking down macro, complex workflows into atomic, manageable units enables automation, intelligence, and optimization.
To achieve a holographic description of each business node, we have developed the proprietary IPOMSQ framework, which defines each node using six standardized attributes. Together, these six dimensions constitute a complete profile of a business node.
Example: The Single-Well Composite Log Business Node
To make this concept more concrete, let us take a common geological study task—creating a single-well composite log—as an example, and analyze its IPOMSQ composition:
I (Input): Layered data, lithology data, logging curves, stratigraphic descriptions, etc.
P (Platform/Process): Using plotting tools or specialized composite charting software.
O (Output): A finalized single-well composite log figure.
M (Management): Requires review and verification of stratigraphic boundaries by geological experts.
S (Standard): Adherence to industry-standard lithology symbols, curve colors, line styles, and other specifications.
Q (Question): Potential technical challenges such as curve splicing issues or depth alignment errors.
Through this standardized description, business activities that were previously ambiguous and dependent on individual expertise are transformed into structured information that machines can understand and execute.
This chapter explains how the Five-Dimensional Business Ontology defined in the previous section is transformed into an executable and queryable enterprise-wide knowledge graph (KG). This knowledge graph serves as the core of the Data Operating System, converting abstract business blueprints into interactive and analyzable digital twins within enterprise operations, acting as the hub connecting data, tools, and intelligence.
We have developed a dual-layer knowledge graph architecture, effectively separating and mapping business rules and reality.
1.KG0 – Business Ontology Graph (The Ontology / Schema): Built upon over 16,000 business nodes, KG0 defines a structured framework of all work units in oil and gas operations, their dependencies, sequential processes, and mandatory rules. It contains no specific instance data; rather, it serves as the business blueprint or schema for the intelligent platform, providing the meta-knowledge repository that governs how business operations should be executed.
2. KG1 – Instance Resource Graph (The Instance Graph): KG1 instantiates all real-world enterprise assets—including data files, software tools, compliance standards, and expert knowledge—onto the KG0 ontology. Each instance is accurately linked to the corresponding business node in KG0. KG1 thus constructs a digital twin of enterprise business operations, reflecting the real-time status and availability of enterprise resources.
Based on this dual-layer knowledge graph architecture, we have built three core foundational platforms, which together constitute the functional entity of the Data Operating System:
JuraData: The enterprise-wide data management platform. Its core is ontology-driven, automatically aggregating, classifying, and governing all data (I/O corresponding to business nodes) according to their associated nodes. This fundamentally addresses data silos, ensuring high-quality, business-relevant data.
JuraComponents: The enterprise-wide tool management platform. It decouples and componentizes all specialized software tools (corresponding to the P – Process of business nodes) according to the business nodes they support. This breaks down tool barriers, enabling any tool functionality to be invoked by intelligent agents on demand.
GeoMapPro: The enterprise-wide visualization platform. It implements an integrated Diagram-Data-Business perspective, allowing users to view all data, diagrams, and business workflows associated with any business object (e.g., a well or a reservoir) within a unified interface, providing a panoramic view of enterprise information.
This chapter focuses on how to safely and controllably apply the powerful reasoning capabilities of general-purpose large language models (LLMs) to professional oil and gas business scenarios. In high-risk, high-value industrial decision-making, ensuring the reliability of AI is an uncompromisable prerequisite. This is precisely the core of our Intelligent Operating System, whose goal is to equip AI with a true business brain.
Applying general LLMs directly to enterprise scenarios faces two core challenges: first, they cannot access private enterprise data; second, they are prone to hallucinations when addressing uncertain professional problems—that is, generating fabricated facts.
To overcome these challenges, we developed the JuraX Intelligent Service Platform. JuraX acts as a bridge or interpreter between general LLMs and enterprise-specific data. Its key mechanism is the use of our Five-Dimensional Business Ontology and knowledge graph as a precise intermediate language. When a user poses a business question, JuraX first parses the query and maps it to relevant business nodes and entities in the knowledge graph. It then generates structured queries or task instructions for the LLM to perform reasoning and planning. This process ensures that all LLM operations are evidence-based and constrained within the scope of the enterprise knowledge graph, enabling the model to understand, query, and operate on professional oil and gas data without producing hallucinations.
Based on the JuraX platform, we provide three core intelligent services, collectively forming the capability layer of the Intelligent Operating System:
1. JuraSeek – Intelligent Search Service: A next-generation search beyond traditional keyword-based methods. JuraSeek leverages understanding of user business intent to perform semantic searches within the knowledge graph, accurately locating the data, tools, or standards needed to complete specific business nodes—achieving a leap from findable to precisely findable and fully comprehensive.
2.JuraRAG – Retrieval-Augmented Generation Service: This service combines the generative capabilities of LLMs with the enterprise-private knowledge contained in the KG1 Instance Resource Graph. When faced with professional questions, JuraRAG first retrieves the most relevant and verified factual data from the knowledge graph, providing this information as context to the LLM, thereby generating precise, reliable, and verifiable responses.
3.JuraAgent – Intelligent Agent: The most advanced intelligent service. JuraAgent autonomously interprets complex business objectives (e.g., “analyze the cause of production decline in Well A”), decomposes them into a series of business nodes, and automatically invokes the associated tools (P) and data (I) in the knowledge graph to complete the task step by step, ultimately generating analytical reports (O) and achieving end-to-end automation.
The oil and gas industry requires not only the general reasoning capabilities of large models but also numerous specialized computational models (small models) to perform domain-specific tasks. The Five-Dimensional Business Ontology acts as a precise gear system, perfectly integrating the two and achieving a best of both worlds solution:
1.Large Models: Serve as the commanders, responsible for general reasoning, natural language understanding, task planning, and workflow orchestration.
2.Small Models: Serve as the execution experts, performing highly specialized domain algorithms, such as seismic interpretation, reservoir numerical simulation, and well logging curve analysis.
Through unified scheduling enabled by the business ontology, large models can accurately invoke the corresponding small models according to business workflow requirements, integrating the results into the final solution. This unified orchestration ultimately establishes a truly intelligent oil and gas platform, combining general AI capabilities with specialized domain expertise.
This chapter provides technical experts and strategic planners with a concrete implementation path and key technical insights for transforming the architecture described above into an industry-specific large language model (LLM).
A critical step in enhancing the domain capabilities of a general-purpose large model is fine-tuning it using high-quality, industry-relevant data. Our dual-layer knowledge graph (KG0 and KG1) provides a unique foundation for automatically generating massive amounts of structured, high-quality fine-tuning corpus.
By traversing the nodes, attributes, and relationships in the knowledge graph, we can automatically generate tens of millions of professional Q&A pairs, covering scenarios from basic definitions to complex process analysis. The quality of these generated pairs far exceeds what can be obtained by crawling unstructured documents.
Examples of automatically generated Q&A pairs include:
1.Definition Q&A:
Q: What is the main function of reservoir simulation?
A: The main function of reservoir simulation is to numerically model the flow and distribution of fluids within the reservoir and predict development outcomes.
2.Data Requirement Q&A:
Q: What key parameters are needed for reservoir evaluation?
A: Key parameters include porosity, permeability, water saturation, and pressure.
3.Process/Operation Q&A:
Q: How is a well pressure test conducted?
A: Well pressure testing is performed by shutting in the well and using a pressure gauge to measure subsurface pressure changes, in order to analyze reservoir characteristics.
4.Causal/Analytical Q&A:
Q: Why is downhole geological analysis necessary in oil and gas exploration?
A: Downhole geological analysis helps evaluate physical reservoir properties (e.g., porosity, permeability) to confirm the presence of hydrocarbons and assess drilling safety, thereby reducing exploration risk.
During the development of an industry-specific large model, we addressed three core technical challenges:
1.Data Quality and Standardization: Traditional methods perform data governance at the post-application stage, which is time-consuming and limited in effect. Our solution leverages the Five-Dimensional Business Ontology to define structured and standardized data at the source, embedding governance into business modeling. This ensures the quality of data input to AI from the outset.
2.Multimodal Data Integration: The oil and gas industry contains vast amounts of graphical (e.g., diagrams, profiles, curves) and tabular data, which general-purpose LLMs struggle to interpret. Using our business ontology, standardized graphic primitives, layers, and template diagrams are strongly associated with business nodes, allowing the model to understand their business meaning and generate multimodal outputs—for example, automatically creating PPTs with charts based on analytical results.
3.Accuracy of Professional Terminology: Accurate terminology is essential for domain-specific applications. We implement a multi-layer safeguard:
(1)Fine-Tuning: Core terms are directly encoded into the model parameters for high-frequency concepts.
(2)Retrieval-Augmented Generation (RAG): A large terminology dictionary provides dynamic context for low-frequency or new terms.
(3)Constrained Decoding: Enforces the use of correct terms programmatically, ensuring 100% accuracy of critical terminology.
Based on this intelligent platform, we construct a three-layered intelligent application structure (L1–L3), achieving full automation from information acquisition to autonomous execution:
L1 – Business Fact Information Retrieval:
This layer addresses what and where questions. For example, an enterprise-wide intelligent Q&A system can answer factual questions about standards, equipment parameters, and historical cases; automatically extract key information from massive reports; and dynamically construct a “knowledge encyclopedia” of wells and blocks.
L2 – Business Problem Analysis and Decision-Making:
This layer addresses why and how questions, supporting semi-automated daily research and automated risk warnings during drilling. For instance, the automatic diagnosis of production decline follows this workflow:
(1)User Input: A user asks in natural language: Why did production decline in Well A1?
(2)Intent Understanding and Template Retrieval: The LLM interprets user intent and uses RAG to retrieve matching standard workflow templates for production decline analysis.
(3)Dynamic Workflow Generation: Based on the retrieved template, the system generates a detailed analysis flowchart including nodes such as geological factor analysis, engineering factor analysis, and “production data verification.”
(4)Autonomous Agent Execution: The intelligent agent executes the workflow sequentially, automatically querying production data (I) and invoking professional software for operational analysis (P).
(5)Result Aggregation and Causal Reasoning: The results from all nodes (O) are integrated, causal reasoning is performed, and a comprehensive report with figures and analysis is generated.
L3 – Automated Generation and Execution of Business Applications:
This is the ultimate form of intelligent application, realizing the vision of Software as a Service. At this level, the system dynamically organizes and generates software services tailored to the user’s role and task needs. Users no longer need to learn or switch between multiple fixed software applications; instead, natural language interaction allows the system to construct and execute intelligent applications on demand, achieving true personalized and context-specific service.
This white paper systematically presents the vision, architecture, and implementation pathway for a next-generation Intelligent Operating System in the oil and gas industry, enabled by the core technology of the Five-Dimensional Business Ontology. We firmly believe that the 5D Business Ontology is the key to bridging the gap between the general intelligence of large language models and the complex business scenarios and massive enterprise data resources of the oil and gas sector. It serves as the core engine for building a truly deployable and trustworthy intelligent platform for oil and gas operations.
The future we envision is one in which intelligent applications evolve beyond the current fragmented stack of software tools into a unified and open industry operating system. Within this ecosystem, intelligent agents built upon the business ontology will function as infinitely reusable and continuously evolving digital oilfield workers, autonomously executing end-to-end tasks—from data analysis to solution design and production optimization.
Our ultimate goal is to enable large language models to truly understand oil and gas business. By constructing an industry future driven by an autonomous intelligent platform, we will fully unleash the value of data, reshape the productivity of knowledge workers, and lead the oil and gas industry into an unprecedented era of efficiency, intelligence, and autonomy.
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