Dynamic Optimization System for Oilfield Risk and 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
Implementing a system that uses a long-term optimizer and short-term optimizers to balance risks and rewards through a cost function that maximizes long-term rewards while minimizing risks, with dynamic adjustment of control signals based on real-time measurement data and adaptive models to optimize oilfield operations such as drilling, hydraulic fracturing, and cementing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current risk and reward analysis methods are used in oilfield operations, then decision-making can be performed, but accuracy and flexibility are insufficient particularly for unexpected events
Solution Approach 1:
The patent implements a dynamic optimization system that continuously adapts to changing conditions during oilfield operations. The system uses real-time data from sensors and measurement tools to update the cost function and re-optimize operational parameters dynamically, allowing the system to respond flexibly to unexpected events while maintaining accurate risk and reward analysis throughout the operation lifecycle
Solution Approach 2:
The system incorporates continuous feedback loops where measurement signals from the operation are fed back to the optimization module. This feedback mechanism allows the system to learn from actual operational outcomes, update its models, and improve the accuracy of risk and reward analysis for subsequent decisions, while maintaining adaptability to new situations
2Measurement precision
If a comprehensive optimization system with long-term and short-term optimizers is implemented, then decision-making accuracy and flexibility improve, but system complexity increases
Solution Approach 1:
The optimization system is segmented into distinct functional modules: a long-term optimizer for strategic planning, short-term optimizers for tactical adjustments, and a coordination module for integrating them. This segmentation allows each module to specialize in specific time horizons and decision types, improving overall accuracy while managing complexity through modular design that enables independent development and testing of each component
Solution Approach 2:
The coordination module serves as an intermediary between the long-term and short-term optimizers, translating strategic goals into tactical parameters and vice versa. This intermediary layer simplifies the interaction between complex optimization algorithms by providing a standardized interface and coordination protocol, reducing the overall system complexity while maintaining the benefits of multi-timescale optimization
3Productivity
If dynamic adjustment of control signals based on real-time data is performed, then operational efficiency improves, but computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary computations by pre-calculating optimization trajectories and storing optimal parameter sets for various operational scenarios. When real-time data becomes available, the system quickly matches current conditions to pre-computed solutions or makes minor adjustments, rather than performing full optimization calculations from scratch. This preliminary action significantly reduces real-time computational requirements while maintaining operational efficiency
Solution Approach 2:
The system applies partial optimization by focusing computational resources on the most critical operational parameters and time-sensitive decisions. Rather than re-optimizing all parameters continuously, the system identifies and optimizes only those parameters that have the greatest impact on operational efficiency and safety, reducing computational processing time while maintaining overall operational effectiveness
Data Source
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AI summary
A system for optimizing a stimulation operation includes an interface to equipment and sensors for performing the stimulation 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.