Oilfield Optimization System Dynamic Risk Reward Analysis
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Solution Overview
Problem
Current risk and reward analysis in oilfield operations lacks accuracy and flexibility, particularly in addressing unexpected high-risk events and pursuing low-risk, high-reward opportunities, leading to suboptimal decision-making.
Innovation Solution
The implementation of an oilfield operation optimization system that utilizes a control module configuration with long-term and short-term optimizers to balance risks and rewards through dynamic adjustment of decision variables, incorporating real-time measurement signals and adaptive models to optimize operations such as drilling, cementing, and hydraulic fracturing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current risk and reward analysis methods are used, then decision-making can be performed, but accuracy and flexibility are insufficient especially for unexpected events
Solution Approach 1:
The patent implements dynamic optimization by continuously updating the cost function with real-time measurement signals during oilfield operations. The system adjusts decision variables dynamically based on current job state, allowing the risk and reward analysis to adapt to unexpected events as they occur rather than relying on static pre-planned analysis
Solution Approach 2:
The system incorporates measurement signals from sensors and interfaces that provide real-time feedback on the actual job state. This feedback loop allows the optimization system to compare expected versus actual outcomes and adjust decisions accordingly, improving both accuracy and flexibility in responding to unexpected events
2Device complexity
If static decision-making approaches are used, then simplicity is maintained, but unexpected high-risk events are addressed only in an ad-hoc manner
Solution Approach 1:
The system performs preliminary optimization by determining optimized values for decision variables before executing the oilfield operation. The long-term optimizer establishes an initial cost function and optimized parameters in advance, creating a structured approach that prevents ad-hoc reactions to unexpected events while maintaining system simplicity
3Measurement precision
If comprehensive risk and reward analysis is implemented, then decision quality improves, but computational complexity and data processing requirements increase
Solution Approach 1:
The optimization system is segmented into distinct functional modules: a long-term optimizer that establishes the initial cost function and optimized parameters, and a short-term optimizer that makes real-time adjustments during operation. This segmentation allows comprehensive analysis to be divided into manageable computational tasks, reducing overall system complexity while maintaining accuracy
Data Source
AI summary
A system for optimizing a completion operation includes an interface to equipment and sensors for performing the completion operation. The interface supplies control signals to the equipment and obtains measurement signals from the sensors. The system further includes a short-term optimizer that derives a current job state based at least in part on the measurement signals, and that further adjusts the control signals to optimize a short-term cost function. The short-term cost function includes a difference between the current job state and a desired job state derived from optimized values of a set of decision variables. The system further includes a long-term optimizer module that determines the optimized values based on a long-term cost function, the long-term cost function accounting for at least a long-term reward and a final state cost.