Borehole Parameter Adjustment via Historical Data Comparison
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Solution Overview
Problem
In hydrocarbon exploration and energy industries, operations such as drilling, formation evaluation, stimulation, and production face challenges in monitoring and controlling conditions to prevent equipment failure and ensure safety, as existing methods lack effective data-driven decision-making for optimizing operational parameters in real-time.
Innovation Solution
A method and system that collect historical data from similar operations to plan and control energy industry operations by comparing measured conditions during the proposed operation to historical data, allowing for automatic adjustments of operational parameters to prevent undesirable situations and improve safety and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual monitoring and control methods are used for energy industry operations, then operational flexibility and human decision-making are maintained, but the ability to prevent equipment failure and ensure safety is insufficient due to lack of real-time data-driven adjustments
Solution Approach 1:
The system continuously monitors operational parameters and compares them against historical data to provide real-time feedback for automatic adjustments. This closed-loop feedback mechanism enables the system to detect deviations from optimal conditions and automatically correct them, improving reliability without requiring complex manual intervention.
Solution Approach 2:
The system performs self-adjustment of operational parameters by automatically comparing real-time measurements with historical data and making corrections without human intervention. This self-service capability allows the system to maintain optimal operation and prevent equipment failure independently, reducing the burden on manual operators while enhancing safety and reliability.
2Reliability
If historical data from previous operations is collected and analyzed, then operational planning and safety can be improved through data-driven decisions, but the system complexity and data processing requirements increase
Solution Approach 1:
The system is designed to collect and analyze multiple types of operational parameters (pressure, temperature, flow rate) using a unified data collection framework. This multi-functional approach allows the same system infrastructure to handle various data types and operational contexts, improving operational safety through comprehensive monitoring without proportionally increasing system complexity.
Solution Approach 2:
The system creates simplified digital representations of historical operational data and compares them against real-time measurements. By working with processed and structured copies of historical data rather than raw data, the system can perform complex analytical comparisons with reduced computational overhead, enhancing safety while managing data processing requirements efficiently.
3Productivity
If real-time measurement and comparison with historical data is performed, then automatic adjustment of operational parameters can be achieved, but the measurement and data processing requirements increase
Solution Approach 1:
The system focuses measurement and monitoring efforts on critical operational parameters that have the greatest impact on safety and efficiency. By applying different levels of monitoring precision to different parameters based on their importance, the system achieves effective automatic adjustment and productivity improvement without requiring uniformly high measurement precision across all parameters, thus optimizing resource requirements.
Data Source
AI summary
An embodiment of a method of performing an energy industry operation includes: collecting historical data relating to one or more previously performed operations having a characteristic common to both the one or more previously performed operations and a proposed operation; planning the proposed operation based on the historical data, the proposed operation associated with one or more operational parameters; performing the proposed operation; measuring a condition during performance of the proposed operation and comparing the measured condition to the historical data; and automatically adjusting the one or more operational parameters based on the comparison.