Drilling Recipe Optimization via Real-Time KPI Feedback
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Solution Overview
Problem
Current slide drilling methods rely on experience and conventional practices, lacking efficient and reliable methods for optimizing drilling processes, particularly in accurately steering the bottom-hole assembly and maintaining toolface orientation, which can lead to suboptimal drilling efficiency and accuracy.
Innovation Solution
A systematic approach using historical time-series data to develop operational templates and recipe settings for drilling rigs, incorporating key performance indicators (KPIs) such as pre-slide time, toolface setting time, and slide rate of penetration to optimize the slide drilling process, and a control system that monitors and adjusts drilling parameters in real-time to maintain target toolface orientation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional experience-based methods are used for slide drilling, then operational simplicity is maintained, but drilling efficiency and accuracy deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-calculating optimal drilling parameters, toolface orientations, and operational sequences before actual drilling begins. Historical data and geological models are analyzed in advance to create optimized drilling plans, allowing the drilling operation to execute pre-determined efficient sequences rather than relying on real-time experience-based decisions.
Solution Approach 2:
The system implements continuous feedback loops where real-time drilling data (toolface orientation, rate of penetration, motor performance) is monitored and compared against optimal parameters. Automatic adjustments are made based on deviations from target values, creating a closed-loop control system that continuously optimizes drilling efficiency while maintaining manageable complexity through algorithmic decision-making.
2Measurement precision
If manual monitoring and adjustment of drilling parameters is used, then system simplicity is maintained, but measurement precision and control accuracy deteriorate
Solution Approach 1:
The control system performs self-service by automatically monitoring toolface orientation, detecting deviations from target values, and executing corrective actions without continuous human intervention. The system uses sensors to self-measure parameters, algorithms to self-analyze performance, and automated controls to self-adjust drilling parameters, achieving high measurement precision while managing complexity through integration.
Solution Approach 2:
Manual mechanical monitoring and adjustment processes are replaced with electronic sensors, digital measurement systems, and automated control algorithms. This substitution enables precise real-time measurement of toolface orientation and automatic adjustment of drilling parameters, achieving superior measurement precision while the integrated digital system manages the complexity that would be unmanageable with purely manual methods.
3Manufacturing precision
If real-time monitoring and adjustment of drilling parameters is implemented, then drilling accuracy is improved, but operational time and system complexity increase
Solution Approach 1:
The system ensures continuity of useful action by implementing uninterrupted real-time monitoring and continuous automatic adjustment of drilling parameters. Rather than periodic manual checks that interrupt drilling, the system maintains continuous optimization throughout the drilling operation, improving path accuracy while minimizing time loss through automated processes that operate without interruption.
Solution Approach 2:
By pre-calculating optimal drilling paths, toolface orientations, and parameter sequences before drilling begins, the system reduces the need for time-consuming real-time adjustments. The preliminary planning phase creates optimized trajectories and parameter schedules that guide the automated control system, achieving high drilling path accuracy while minimizing operational time through efficient pre-planned sequences.
Data Source
AI summary
A method, apparatus, and system according to which a drilling engine includes a template having a plurality of data fields outlining operational steps and parameters to perform a drilling process, the data fields having a plurality of recipe settings input therein to facilitate performance of the drilling process. A computer system communicates with the drilling engine and an operational equipment engine, and is configured to send a first control signal, based on the template and the recipe settings, to the operational equipment engine to cause the operational equipment engine to perform the drilling process to drill a first wellbore segment. A sensor engine is configured to monitor a key performance indicator (“KPI”) of the operational equipment engine during the performance of the drilling process. In some embodiments, the drilling engine includes a recipe optimization module configured to modify, based on the monitored KPI, at least one of the recipe settings.