Bottom Hole Assembly Selection Using Offset Drilling Similarity
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Solution Overview
Problem
Existing drilling operations lack efficient methods for selecting and optimizing bottom hole assemblies and drilling fluids based on historical feature data, leading to suboptimal drilling performance and resource field development.
Innovation Solution
A method and system that utilize historical feature data to compute feature distances and assess similarity between drilling operations, outputting recommendations for selecting components of the bottom hole assembly and drilling fluid to enhance drilling efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional drilling operations are used without historical data analysis, then the drilling process is simpler and faster to implement, but the selection of bottom hole assemblies and drilling fluids is suboptimal leading to reduced drilling performance
Solution Approach 1:
The system performs preliminary analysis of historical drilling data before actual drilling operations to pre-determine optimal bottom hole assembly configurations and drilling fluid selections. By computing feature distances and assessing similarities in advance, the system prepares optimized drilling parameters before they are needed, improving drilling performance without adding operational complexity during the actual drilling process
Solution Approach 2:
The patent replaces traditional empirical and experience-based drilling equipment selection with a computational system that uses historical data analysis, feature distance computation, and similarity assessment. This substitution of mechanical/e经验-based methods with information-processing methods enables optimized drilling performance while maintaining relatively simple system implementation
2Measurement precision
If historical feature data analysis is implemented to assess similarity between drilling operations, then the selection accuracy of drilling components is improved, but the computational processing time and complexity increase
Solution Approach 1:
The system extracts only the most relevant features from historical drilling data that are necessary for accurate similarity assessment and component selection. By identifying and processing only critical features rather than complete historical datasets, the system achieves high selection accuracy while minimizing computational processing time and resource requirements
Solution Approach 2:
The system transforms historical drilling data into standardized feature representations with consistent dimensions and scales, enabling efficient computation of feature distances. By changing the parameter representation format and normalizing data, the system improves both selection accuracy and computational efficiency, reducing processing time while maintaining precision
3Productivity
If feature distance computation is used to generate offset drilling operations, then the optimization of drilling components is enhanced, but the device complexity and data processing requirements increase
Solution Approach 1:
The system develops a universal feature distance computation framework that can assess similarity across different drilling operations, formations, and equipment configurations using a standardized approach. This multi-functional system handles various drilling scenarios with a single unified methodology, improving drilling efficiency through consistent optimization while avoiding the need for multiple specialized complex systems
Solution Approach 2:
The system creates simplified representations (copies) of historical drilling operations with essential features extracted and standardized. These copied representations enable efficient feature distance computation and similarity assessment without requiring access to or processing of complete, complex historical datasets, thereby improving drilling efficiency while reducing data processing complexity
Data Source
AI summary
A method can include receiving input for a drilling operation that utilizes a bottom hole assembly and drilling fluid; generating a set of offset drilling operations using historical feature data, where the historical feature data are processed by computing feature distances; performing an assessment of the offset drilling operations as characterized by at least feature distance-based similarity between the drilling operation and the offset drilling operations; and outputting at least one recommendation for selection of one or more of a component of the bottom hole assembly and the drilling fluid based on the assessment.