Graph-Based Well Trajectory Planning for Complex Reservoirs
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Solution Overview
Problem
Current field operations in geologic formations face challenges in efficiently planning and executing well trajectories due to limitations in data integration and real-time decision-making, particularly in complex reservoirs with varying properties and fractures.
Innovation Solution
A system and method that utilize data generated during field operations to create a graph with vertices and edges representing relationships, enabling the generation of query results for optimized well planning and execution, incorporating automation and real-time data analysis to improve drilling precision and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data integration methods are used for well planning, then data can be stored and accessed, but real-time decision-making and drilling precision are insufficient
Solution Approach 1:
The system implements real-time feedback loops where drilling data is continuously collected from sensors, processed by the graph database, and used to immediately adjust drilling parameters. This closed-loop feedback enables real-time decision-making while maintaining high drilling precision through dynamic parameter optimization.
Solution Approach 2:
The system dynamically changes drilling parameters (rate of penetration, weight on bit, rotational speed) based on real-time graph query results that analyze formation properties and well trajectory. This parameter adaptation allows the system to maintain optimal drilling precision while responding instantly to changing subsurface conditions.
2Productivity
If complex data relationships are integrated comprehensively, then better decision-making is achieved, but system complexity increases
Solution Approach 1:
The patent introduces a graph database as an intermediary layer between raw drilling data and decision-making processes. This intermediary automatically models complex relationships between drilling parameters, formation properties, and operational outcomes, simplifying the integration process while enabling comprehensive data analysis for improved operational efficiency.
Solution Approach 2:
The system segments the complex data integration task into manageable components: data collection from multiple sources, graph model construction with defined entities and relationships, query processing, and action generation. This segmentation reduces system complexity while maintaining comprehensive data integration capabilities for enhanced productivity.
3Measurement precision
If more data is collected and analyzed, then drilling precision and resource recovery improve, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-building the graph data model with anticipated relationships and pre-computing common query patterns before they are needed. This preparation allows rapid querying and analysis when actual drilling decisions are required, improving well trajectory accuracy without excessive processing time during critical operations.
Data Source
AI summary
A method can include accessing data generated during field operations; generating a graph that includes vertices and edges using at least a portion of the data, where the edges represent relationships between vertices; and generating a query result using the graph responsive to receipt of a query.