Multi-Well Drilling Data Integration for Real-Time Benchmarking
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Solution Overview
Problem
Integrating real-time and historical drilling data across multiple wells is challenging due to manual data processing inefficiencies and static analysis methods, which hinder performance benchmarking and dynamic decision-making in wellbore operations.
Innovation Solution
A system that automatically combines and normalizes real-time and historical data from multiple sources, allowing for continuous comparative analysis and dynamic adjustments, enabling self-service analytics and optimized drilling decisions through a computing device with integrated data processing and visualization tools.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data processing methods are used to integrate real-time and historical drilling data, then data integration can be performed, but non-productive time increases and processing efficiency decreases
Solution Approach 1:
The system enables self-service analytics where the drilling operation automatically integrates and analyzes its own real-time and historical data without requiring manual intervention. The automated data processing system continuously benchmarks performance metrics across multiple wells and rigs, allowing the operation to serve itself with insights and recommendations.
Solution Approach 2:
The patent replaces manual mechanical data processing methods with an automated computational system. The system uses software algorithms to automatically integrate real-time sensor data with historical drilling data, eliminating the need for manual data collection, consolidation, and analysis processes that previously consumed significant non-productive time.
2Adaptability or versatility
If static analysis methods are used for drilling data, then analysis can be performed, but dynamic decision-making capability is hindered
Solution Approach 1:
The system transforms static drilling data analysis into a dynamic process by continuously integrating real-time sensor data with historical data. The system automatically updates performance benchmarks and provides real-time recommendations, enabling adaptive decision-making that responds to changing drilling conditions across multiple wells and rigs.
Solution Approach 2:
The data integration system is designed to handle multiple data sources, analysis types, and decision-making requirements through a single unified platform. It can simultaneously perform real-time data integration, historical benchmarking, performance analysis, and predictive modeling across diverse drilling operations, reducing overall system complexity despite the sophisticated functionality provided.
3Measurement precision
If real-time data from multiple sources is integrated, then performance benchmarking is improved, but data processing complexity increases
Solution Approach 1:
The system segments the complex task of multi-source data integration into manageable components: real-time data collection from sensors, historical data retrieval, data normalization, benchmarking calculations, and recommendation generation. This modular approach improves measurement precision for performance benchmarking while keeping the integration system complexity manageable through structured processing stages.
Data Source
AI summary
Real-time data from one or more sensors can be received about one or more active wellbore operations. A blended data set can be generated that includes the real-time data combined with historical data about previously completed wellbore drilling operations, and associated with performance attributes. Additionally, identification of a criteria of focus, types of parameters for optimization, and operation groupings for the blended data set can be received. Further, the criteria of focus, types of parameters for optimization and operation groupings for the data can be applied to the blended data set to determine an adjustment for an active wellbore drilling operation of interest. The adjustment for the active wellbore drilling operation of interest can be outputted.