Cyclic Steaming Schedule Optimization for Oil Wells
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Solution Overview
Problem
Cyclic steaming in low permeability oil fields faces challenges in maximizing oil production and optimizing steam usage due to variability in well production rates and the need for careful selection and timing of steam injection to maintain efficient oil recovery.
Innovation Solution
A method and system for scheduling cyclic steaming that involves inputting historical data into a production-predicting means to generate production predictions, which are then processed by an optimization means using algorithms such as genetic algorithms to determine an optimized steaming cycle schedule, ensuring efficient use of steam and maximizing oil production.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cyclic steaming is performed repeatedly to maintain oil production from low permeability wells, then oil recovery is improved, but steam consumption increases and operational efficiency decreases
Solution Approach 1:
The system performs preliminary analysis of historical production data and well characteristics before scheduling steaming operations. By predicting future production trends and identifying wells that will benefit most from steaming, the system prepares optimized schedules in advance that maximize oil recovery while minimizing unnecessary steam consumption on wells that don't require intervention.
Solution Approach 2:
The steaming schedule is dynamically adjusted based on real-time and historical well performance data. The system continuously monitors production rates and modifies steaming timing and frequency for individual wells, transitioning from static predetermined schedules to adaptive dynamic scheduling that optimizes the balance between oil production and steam consumption.
2Productivity
If steaming cycles are extended to maximize oil production from each well, then oil recovery improves, but the time available for treating other wells decreases
Solution Approach 1:
The field is segmented into multiple well groups or individual wells with customized steaming schedules. Instead of applying a uniform extended steaming cycle to all wells, the system divides the population and assigns optimized cycle lengths to each well based on its specific characteristics and production response, allowing parallel treatment of multiple wells with different timing.
Solution Approach 2:
The system varies key parameters of the steaming cycle (duration, frequency, timing) across different wells rather than using a fixed parameter set. By changing these parameters based on well-specific performance data, the system optimizes oil production from each well while maintaining an overall schedule that accommodates treatment of the entire well population within available time.
3Productivity
If steaming schedules are optimized for each individual well, then oil production from that well is maximized, but the complexity of managing the overall field schedule increases
Solution Approach 1:
The scheduling system is designed to be self-adjusting and self-optimizing using automated algorithms that analyze well performance data and generate optimized schedules without requiring complex manual intervention. The system serves itself by continuously learning from production data and automatically adjusting schedules, reducing the operational complexity despite the customized nature of individual well schedules.
Solution Approach 2:
The system implements feedback loops where actual well performance data is continuously fed back into the scheduling algorithm. This feedback mechanism allows the system to automatically refine and adjust individual well schedules based on real-world results, maintaining optimization while managing complexity through automated adaptive control rather than static complex planning.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively optimizes the steaming schedule to maximize oil production and minimize steam usage by ranking and refining steaming cycles based on predetermined criteria, leading to improved field performance over time.
Implementation Method 1
inputting to a production-predicting means a group of data describing at least in part the past cyclic steaming and resulting production of a group of petroleum-containing wells; processing the data in the production-predicting means and outputting a group of production predictions
Implementation Method 2
producing a group of new steaming cycle schedules based on the ranking of the initial steaming cycle a schedule optimization algorithm; determining a ranking for the new steaming cycle schedules against the pre-determined ranking criteria
Data Source
AI summary
A method of scheduling cyclic steaming of petroleum-containing wells including: inputting to a production-predictor data describing the past cyclic steaming and resulting production of wells; processing the data in the production-predictor and outputting production predictions for the wells during a future steaming cycle; inputting the production predictions into an optimizer; inputting an initial steaming Optimal Cycle Length schedule for the wells into the optimizer; processing the production predictions and the initial steaming cycle schedule in the optimizer by: determining a ranking for the initial steaming cycle schedule for the production predictions against a pre-determined ranking criteria; producing new steaming cycle schedules based on the ranking of the initial steaming cycle a schedule optimization algorithm; determining a ranking for the new steaming schedules against the ranking criteria; repeating the production of schedules and determining ranking steps until some pre-determined termination criteria is met.