Self-Improving Reasoning Tools for Oil and Gas
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Solution Overview
Problem
Conventional decision support systems in oil and gas production face challenges in adapting to changing environments, with data-conditioning workflows often lacking and human workflows not being centrally managed or automated, leading to bottlenecks in real-time data flow and poor asset awareness, which can result in missed business opportunities.
Innovation Solution
The integration of data-driven modeling and knowledge-based reasoning tools to create self-improving systems that adapt to changing environments, using hybrid artificial intelligence systems combining data-driven and expert reasoning to capture and deploy knowledge across dynamic operations, enabling adaptive decision-making and performance optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional decision support systems are used in oil and gas production, then systems can be customized for individual operating environments, but data flow bottlenecks occur and real-time responsiveness is lost
Solution Approach 1:
The patent implements a universal data-conditioning workflow platform that serves multiple functions: data collection, validation, transformation, and distribution to multiple applications simultaneously. This centralized architecture eliminates data flow bottlenecks while maintaining adaptability to individual operating environments through configurable workflow templates that can be customized for different oil and gas production scenarios without requiring separate custom systems for each environment.
2Reliability
If human production workflows are manually managed, then expert knowledge can be applied, but workflows are not centrally managed or appropriately automated leading to inefficiency
Solution Approach 1:
The system implements self-service automation where the workflow engine automatically executes data-conditioning workflows based on predefined rules and templates. Expert knowledge is encoded into reusable workflow templates that automatically guide data processing, validation, and transformation without requiring manual human intervention for each execution. The system serves itself by automatically managing workflow orchestration, error handling, and result distribution across multiple applications.
3Device complexity
If data-conditioning workflows are not in place, then system complexity is reduced, but data flow bottlenecks occur and asset awareness is poor
Solution Approach 1:
The patent segments the data-conditioning workflow into distinct modular components: data collection modules, validation modules, transformation modules, and distribution modules. Each segment performs a specific function and can be independently configured and managed. This modular segmentation reduces overall system complexity by breaking down complex workflows into manageable units while ensuring comprehensive data processing that prevents information loss and maintains full asset awareness throughout the production environment.
4Adaptability or versatility
If decision-making processes depend on user knowledge and individual experience, then customized solutions can be created, but workflows become non-standardized and难以 to scale
Solution Approach 1:
The system performs preliminary action by pre-configuring standardized workflow templates that encode expert knowledge and best practices for common oil and gas production scenarios. These templates are prepared in advance with predefined data validation rules, transformation logic, and distribution configurations. When deployed, the templates automatically adapt to specific operating environments through parameter configuration rather than requiring custom development, thus maintaining both standardization and adaptability.
Data Source
AI summary
Implementations that integrate data-driven modeling and knowledge into self-improving reasoning systems and processes are described. For example, an implementation of a method may include determining at least one recommended action using a reasoning component having a data-driven modeling portion and a knowledge-based portion. Such determining includes integrating one or more determination aspects determined by the data-driven modeling portion, and one or more additional determination aspects determined by the knowledge-based portion.