Optimum Rotary Steerable Settings from Multi-Dimensional Drilling Data
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Solution Overview
Problem
Conventional methods for identifying optimum rotary steerable system settings rely on manual slide sheets that are inconsistent and lack comprehensive data recording, leading to increased costs and reduced reliability in drilling operations.
Innovation Solution
A method and apparatus that utilize a multi-dimensional data matrix to automate the identification of optimum rotary steerable system settings by analyzing data sets from drilling segments, including changes in settings and parameters, and adjusting the steering system through a surface control system to maintain trajectory within tolerance windows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual slide sheets are used to identify optimum rotary steerable system settings, then the process is simple to operate, but the reliability and consistency of drilling operations deteriorate
Solution Approach 1:
The system automatically identifies optimum RSS settings by self-analyzing historical drilling data stored in the database table, eliminating the need for manual slide sheet interpretation. The electronic application autonomously creates multi-dimensional data matrices and extracts optimal parameters, allowing the system to serve itself rather than requiring manual intervention.
Solution Approach 2:
The manual mechanical process of reviewing and interpreting slide sheets is replaced by an electronic computational system. The electronic application uses automated algorithms to analyze drilling data, create multi-dimensional data matrices, and extract optimum RSS settings, substituting human manual analysis with electronic data processing.
2Reliability
If comprehensive data recording is implemented for all drilling variables, then the reliability of identifying optimum settings improves, but the device complexity and data management burden increase
Solution Approach 1:
The database table structure is designed to universally accommodate multiple drilling variables (steering ratios, surface parameters, downhole conditions, etc.) in a standardized format. Each row represents a complete drilling segment with all relevant variables, allowing the same data structure to serve multiple analysis purposes and eliminating the need for separate tracking systems for each variable.
Solution Approach 2:
The system transforms multi-variable drilling data into a multi-dimensional data matrix where each dimension represents a specific variable. This dimensional transformation allows comprehensive data analysis while maintaining organizational simplicity, as the electronic application can slice and dice the data along any dimension to identify optimal settings without managing complex inter-variable relationships manually.
3Productivity
If automated electronic applications are used to create multi-dimensional data matrices, then the productivity and consistency of identifying optimum settings improve, but the device complexity increases
Solution Approach 1:
The system performs preliminary data organization by storing all drilling segment data in a standardized database table structure before analysis is needed. Historical data is pre-categorized with all relevant variables (steering ratios, surface parameters, downhole conditions) already structured in rows and columns, so that when optimum settings are needed, the electronic application can quickly retrieve and analyze the pre-organized data without manual preparation.
Solution Approach 2:
The electronic application creates simplified copies of the comprehensive drilling data in the form of multi-dimensional data matrices. Instead of managing the full complexity of raw drilling data, the system generates condensed matrix representations that capture the essential relationships between variables, making the data easier to analyze while maintaining the underlying comprehensive information.
Data Source
AI summary
A method that includes an electronic application identifying an ending of a first drilling segment and a simultaneous beginning of a second drilling segment; identifying a data set of the first drilling segment; automatically creating, in response to the identification of the first drilling segment ending, a new row in a database table that stores a data set for each drilling segment, with the new row storing the variable values of the first drilling segment; creating a multi-dimensional data matrix based on the values in the database table; and extracting, from multi-dimensional data matrix, an optimum value of a variable for an upcoming drilling segment. The method may also include determining that a trajectory or location of a rotary steering system is outside of a tolerance window; and the step of extracting the optimum value of the variable is in response to this determination.