Well Lifecycle Lift Plan Optimization via Dynamic Algorithmic Selection
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Solution Overview
Problem
Current methods for selecting and optimizing artificial lift types in hydrocarbon wells are often based on limited analysis and do not account for future production changes or consider all available lift type and parameter combinations, leading to suboptimal economic value and lack of flexibility in lifecycle planning.
Innovation Solution
A system and method that uses a computer-based network analysis to determine the optimal artificial lift plan, including recommendations for initial lift type selection and timing of changes, while providing visualization tools for users to interactively adjust and modify the plan to reflect real-world variations and changes in well production decline curves.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a single artificial lift type is selected based on current well status and experience, then the decision-making process is simple and quick, but the economic value and adaptability to future production changes are suboptimal
Solution Approach 1:
The system transitions from static, experience-based lift type selection to dynamic, algorithmic optimization that continuously adapts to changing well conditions and production decline curves throughout the well lifecycle
Solution Approach 2:
The system performs preliminary analysis of multiple lift types and parameter combinations before making a selection, evaluating economic value across the entire well lifecycle rather than relying on current status alone
2Device complexity
If only a subset of available lift types and parameter combinations is considered, then the analysis complexity is reduced, but the optimal economic value cannot be achieved
Solution Approach 1:
The system creates a universal evaluation framework that can assess all available lift types and parameter combinations using a common economic value model, ensuring comprehensive coverage without proportionally increasing complexity
Solution Approach 2:
The system replaces manual, experience-based selection processes with automated algorithmic optimization that systematically evaluates all lift type options and parameter combinations
3Reliability
If a single optimal lift plan is determined algorithmically, then the analysis is comprehensive, but flexibility to accommodate real-world variations and deviations is limited
Solution Approach 1:
The system enables dynamic adjustment of lift plan parameters such as changeover timing and lift type selection, allowing users to modify the optimal plan to accommodate real-world constraints and variations while maintaining economic optimization
4Loss of time
If the lift type selection is based on current production rate and decline curve estimates, then the decision can be made quickly, but future production dynamics and the need for lift type replacement are not accounted for
Solution Approach 1:
The system performs preliminary evaluation of multiple future scenarios and production decline trajectories before selecting the optimal lift type and changeover timing, incorporating future production dynamics into the current decision
Solution Approach 2:
The system incorporates feedback loops that continuously monitor actual production against predicted decline curves and adjust lift type recommendations and changeover timing accordingly throughout the well lifecycle
Data Source
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AI summary
According to one embodiment, there is provided herein a system and method for producing a well lifecycle lift plan that includes considerations of multiple types of lift, multiple lift configurations associated with each lift type, and can be used to provide a prediction of when or if it would be desirable to change the lift plan at some time in the future. Another embodiment utilizes a heuristic database with rules that might be used to limit the solution space in some instances by restricting the solution to feasible configurations. A further embodiment teaches how multiple individual well optimization results might be combined with a reservoir model to obtain an optimized lift schedule for an entire field.