Oil in One (OiO) Integrated Intelligent Platform is a next-generation digital-intelligent foundation for AI implementation in the oil and gas industry. Built on high-quality business data sets and oil & gas intelligent agents, it integrates data, knowledge, models, intelligence, and business applications to support full-process digital transformation across exploration and development, production management, and enterprise operations.
During the digital-intelligent transformation of oil and gas enterprises, multiple barriers still exist among data foundations, business knowledge, system capabilities, and AI applications, limiting the large-scale implementation of intelligent capabilities in core business scenarios.
Oil and gas data comes from diverse sources, follows inconsistent standards, and varies in quality, making it difficult to form reusable, high-quality business data assets that can support intelligent applications.
Industry experience, business rules, and professional workflows are dispersed across experts and systems, making structured knowledge accumulation difficult and preventing systems from truly understanding oil and gas business.
Various professional systems are built independently, making it difficult to connect data, tools, and business processes, which limits collaboration efficiency across disciplines and scenarios.
General-purpose AI cannot be directly adapted to complex oil and gas business processes. A platform that integrates data, knowledge, models, and intelligent agents is required to enable AI to enter core business workflows.
Centered on the full oil and gas business process, OiO builds a digital-intelligent support system for AI implementation through the coordinated integration of data, knowledge, models, intelligent agents, and applications.
Integrate multi-source data, establish unified standards and quality systems, and build high-quality business data assets.
Structure experience, rules, and professional knowledge to form a reusable and evolvable knowledge system.
Build capabilities for reasoning, analysis, diagnosis, and decision support.
Connect systems and workflows to enable cross-disciplinary and cross-scenario collaboration with closed-loop management.
Empower core scenarios such as exploration and development, production operations, and business management to improve efficiency, reduce costs, and create business value.
Oil and gas business operations involve well-defined professional objects, workflows, analytical methods, and deliverable requirements. For AI to be truly integrated into business scenarios, a complete support system must be built progressively, driven by specific application scenarios and covering project environments, intelligent capabilities, business services, resource management, business models, and ecosystem connectivity.
Based on this logic, the OiO platform has developed a new seven-layer structure: Scenario Application Agents, Project Workspace, General Intelligent Capability Center, Business Semantic Encapsulation Layer, Business Resource Management Layer, Business Ontology Model Layer, and Ecosystem Integration Layer.
Scenario Application Agents serve as the direct entry point for OiO to provide intelligent support for business users. Focusing on core scenarios such as research, production operations, production management, and business decision-making, the platform builds oil and gas intelligent agents to support data querying, professional analysis, problem diagnosis, map generation, report preparation, scheme comparison, and decision support.
The Project Workspace organizes data, tools, workflows, and deliverables around specific projects, providing a project-based environment for business collaboration and intelligent application operations. Within the same workspace, business users can manage data, invoke tools, and accumulate results, while intelligent agents can participate in analysis, documentation, and report generation based on project context. This enables AI to move beyond one-off Q&A and provide continuous support throughout project workflows, while facilitating review, collaboration, and deliverable reuse.
The General Intelligent Capability Center consolidates and manages reusable intelligent capabilities such as knowledge retrieval, data analysis, graphical generation, report generation, model invocation, and tool orchestration, providing unified support for different projects, business scenarios, and intelligent agents. Through standardized capability encapsulation and reuse, new intelligent applications can be rapidly assembled, flexibly expanded, and continuously iterated, reducing redundant development, improving platform-level capability reuse, and accelerating implementation across a wider range of business scenarios.
The Business Semantic Encapsulation Layer organizes and service-enables data, knowledge, rules, models, algorithms, and tools into callable business capabilities such as data querying, analysis, map generation, diagnosis, and report generation. Through this layer, intelligent agents and upper-level applications do not need to directly handle raw data tables, complex system interfaces, or tool parameters. Instead, they can invoke resources, organize tasks, and generate results based on business language and workflows, improving the alignment between intelligent applications and real business scenarios.
The Business Resource Management Layer provides unified classification, management, and governance for data, maps, models, algorithms, components, tools, and business deliverables distributed across different systems, departments, and projects. Through this layer, existing enterprise resources can be effectively searched, maintained, invoked, and reused, transforming them into foundational assets that support business applications, intelligent agent operations, service stability, intelligent application expansion, and continuous platform evolution.
The Business Ontology Model Layer establishes a unified semantic foundation for oil and gas business operations. It provides standardized descriptions of business objects such as wells, layers, reservoirs, blocks, measures, production, dynamics, and development plans, as well as their relationships, workflows, rules, and knowledge structures. Through this layer, data can be mapped to business objects, knowledge can be linked to business rules, tools can support business workflows, and intelligent agents can understand and execute tasks based on clear business semantics, providing foundational support for resource management, service encapsulation, and intelligent application operations.
The Ecosystem Integration Layer incorporates enterprise data platforms, professional software, industry standards, DataHub, cloud-edge devices, and AI computing resources into a unified platform framework, organizing and integrating them around oil and gas business needs. Through this layer, internal and external enterprise resources can provide coordinated support from a unified business perspective, laying the foundation for business modeling, resource management, capability encapsulation, and intelligent agent applications, and enabling OiO to build a sustainably scalable intelligent foundation.
OiO is built on high-quality business datasets and uses oil and gas intelligent agents as the entry point to connect data, knowledge, models, tools, and scenarios into a closed loop, supporting the continuous implementation of AI capabilities in business operations.
Through data standards, data quality, data asset management, and data service systems, the OiO platform provides unified governance and structured accumulation of multi-source heterogeneous enterprise data. By integrating capabilities such as data lakes, business components, visualization components, and machine learning platforms, OiO builds a data resource foundation for AI training, inference, analysis, and application development.
Through its oil and gas large model, knowledge enhancement, and intelligent agent capabilities, the OiO platform transforms users’ natural language requirements into executable tasks. Based on business semantics, intelligent agents can understand problems, invoke data, knowledge, models, and tools, and carry out tasks such as data querying, analysis, diagnosis, map generation, report generation, and decision support.
Industry-wide models aligned with OSDU standards for lifecycle consistency and interoperability.
A unified framework that connects data, knowledge, and intelligence across the enterprise.
Closed-loop workflows that transform insights into execution through large models and intelligent agents.
Trusted, sovereign data managed across standards, quality, and assets.
This technology translates the complex oil & gas workflow into over 16,000 standardized, elemental business nodes.
Modular, knowledge-based capabilities that expand seamlessly across domains.
OiO’s integrated intelligent platform is founded on the “5 Whatever + 5 Share” principle, which systematically dismantles conventional physical and organizational barriers within the oil and gas sector.
By facilitating the seamless sharing of knowledge, talent, technology, ecosystems, and data throughout the value chain, the platform enhances cross-disciplinary collaboration, supports remote operations, promotes organizational interoperability, and ensures deep data integration. This approach establishes a new paradigm for open, intelligent, and highly efficient collaboration across the industry as a whole.
Sudden water cut increase in a well from 55% to 78%, causing a drop in oil production. On-site team required response recommendations within 2 hours.
Reservoir engineers, production engineers, and surface process engineers collaborated on a unified platform for the well. They analyzed a comprehensive evidence chain, which included liquid production and water cut curves, the response to water injection, comparisons with offset wells, operational history, and surface pressure differentials and conditions.
Field operators: Viewed exception alerts + handling steps + safety tips.
Engineers: Accessed root cause reasoning chain + comparative analysis + recommended actions.
Management: Monitored production impact/risk level/progress.
Real-time collaboration was established between overseas field teams and domestic research institutes, employing synchronized screens to replace the traditional use of email threads and alignment meetings.
Field personnel employed explosion-proof phones and tablets to access information regarding well exceptions and conditions, while engineers conducted thorough analyses on computers.
Project companies, research institutes, and service providers shared data and conclusions based on permissions, with all recommendations being traceable to their source.
JuraSearch integrated time-series, operational, injection, offset well, and surface process data with one click, auto-generating an evidence package.
The same diagnostic components/chart templates/report formats were reused across all wells, eliminating the need to rebuild charts and tables for each case.
The logic for determining the cause of the “sudden water cut increase” and the response pathway were documented as a case card + rule, enabling automatic recall for similar future issues.
A small number of domestic experts provided remote support for multiple wells, while on-site trainees could follow systematic steps to complete diagnostics and handling.
Recommendations were instantly converted into work orders and pushed to EAM/CMMS (or operational systems), enabling closed-loop tracking and review.
Reduced from half a day or a full day to 30 to 60 minutes.
From time-consuming manual data search and repeated coordination efforts toward automated data linking, supported by a single, integrated collaboration interface.
From reliance on isolated human knowledge to the codification and standardization of expertise, establishing a library of enterprise-wide, replicable rules, cases, and templates.
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