Leverage natural language interaction, collaboration, and automated content generation to break down data silos, address core operational pain points like inefficiency, and enhance decision-making speed and business value.
· Core Capability: Transforming human expertise into searchable knowledge, and converting tacit knowledge into explicit rules.
· Key Issues: Extremely high maintenance costs for rules, poor adaptability to new scenarios, and difficulty in scaling knowledge reuse.
· Core Capability: Shifting from rule-driven to data-driven, discovering patterns from historical data.
· Key Issues: Heavy reliance on labeled data, limited model generalization capability, and inability to understand business semantics.
· Core Capability: Understanding natural language and business semantics, possessing cognitive abilities such as logical reasoning, complex Q&A, and content generation.
· Key Issues: Challenges with large model hallucinations and controllability, integration of industry-specific expertise, and high inference costs.
· Core Capability: Evolving from thinking to doing—AI possesses the ability to take action and automatically complete task chains.
· Key Issues: Permission and security controls, process reliability, and failure rollback mechanisms.
Core Capability: Dynamic task planning and autonomous decomposition based on business objectives. An intelligent agent cluster can understand complex business intentions, automatically breaking down requirements such as querying data, analysis, reasoning, report writing into a series of subtasks. It then orchestrates execution by dynamically scheduling different Agents and Skill modules to collaborate.
Multi-source data is scattered across disparate systems, lacking a unified sharing mechanism. The flow of data across departments and platforms is hindered.
Manual data retrieval and organization are time-consuming, with repetitive tasks dominating. Business personnel expend significant effort on data acquisition rather than value creation.
Lack of visualization and intelligent technology prevents deep data value extraction, failing to support precise decision-making and business innovation.
Utilize natural language as the query interface, automatically parsing user inquiries and converting them into standardized data-query tasks.
The central control Agent dynamically plans the query process, automatically decomposing and scheduling various data query and analysis capabilities.
Support unified invocation and result aggregation for both structured and unstructured data queries.
Implement an end-to-end tracking mechanism, ensuring query processes are explainable, results are auditable, and issues are traceable.
Establish a unified data lake and standardized data system, breaking down cross-platform data barriers to enable seamless, efficient data circulation and sharing across the entire organization.
Intelligent search and automated processing compress data retrieval time from hours to seconds, reducing repetitive work by over 80% and allowing business staff to focus on high-value analysis.
These are 3 processing plants and over 160 wells.
Contain roughly 62,000 result documents and about 6.49 million rows of tabular data.
Primary reliance on email and cloud drives for archiving and managing data, resulting in rudimentary search methods and a lack of efficient tools for data retrieval, processing, and professional charting.
Encompasses approximately 4,600 business activities including exploration & development, drilling, injection-production operations, and operation & maintenance.
Solutions and Value
Unlock AI-driven transformation across the oil and gas value chain. Our intelligent applications convert complex data into actionable insights, optimize operations from exploration to production, and build the foundation for a safer, more efficient, and more profitable energy enterprise.
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