Mixed-Integer Nonlinear Optimization for Wellhead Pressure Control
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Solution Overview
Problem
Current oilfield production optimization methods fail to efficiently maximize production while minimizing costs, particularly in managing lift-gas injection and choke control across interconnected wells, leading to suboptimal production and increased operational complexity.
Innovation Solution
A method and system utilizing a mixed-integer nonlinear program solver for offline modeling, combined with online network modeling, to optimize lift-gas injection rates and choke states in each well, ensuring convergence of wellhead pressures and adjusting operating parameters to achieve optimal production and cost efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional production optimization methods are used, then operational simplicity is maintained, but production efficiency and cost optimization are insufficient
Solution Approach 1:
The optimization system is divided into two distinct models: an offline model that performs comprehensive optimization calculations using mixed-integer nonlinear programming, and an online model that handles real-time operational adjustments. This segmentation allows complex optimization to be performed periodically while maintaining simpler real-time operations, thus improving production efficiency without proportionally increasing operational complexity.
Solution Approach 2:
The offline model performs preliminary optimization calculations before actual production operations, determining optimal lift-gas injection rates and choke states in advance. These pre-calculated solutions are then applied during online operations, allowing the system to achieve sophisticated optimization results without requiring complex real-time decision-making, thereby improving productivity while keeping operational complexity manageable.
2Productivity
If lift-gas injection and choke control are not optimized, then operational simplicity is maintained, but production maximization and cost minimization are not achieved
Solution Approach 1:
The system implements a feedback mechanism where the offline model's optimization results are continuously compared with actual production data from the online model. This feedback loop allows the system to learn from actual performance and adjust optimization parameters accordingly, enabling production maximization and cost minimization while maintaining operational simplicity through automated adjustments rather than complex manual interventions.
Solution Approach 2:
The optimization system is designed to be self-adjusting, where the mixed-integer nonlinear program solver automatically determines optimal lift-gas injection rates and choke states based on current production conditions. This self-service capability eliminates the need for continuous manual optimization efforts, achieving production maximization and cost minimization while maintaining operational simplicity.
3Measurement precision
If offline modeling with mixed-integer nonlinear program solver is used, then production optimization precision is improved, but computational complexity increases
Solution Approach 1:
The computational problem is segmented into an offline phase that handles complex mixed-integer nonlinear programming for high-precision optimization, and an online phase that implements the solutions with simpler computations. This segmentation allows the system to achieve high optimization precision through sophisticated mathematical modeling while keeping real-time computational complexity low by using pre-calculated solutions and straightforward adjustments.
4Stability of the object's composition
If wellhead pressures are not stabilized, then operational simplicity is maintained, but production consistency and network efficiency deteriorate
Solution Approach 1:
The system merges pressure stabilization objectives into the overall production optimization framework by integrating wellhead pressure constraints and objectives into the mixed-integer nonlinear program. This unified approach stabilizes wellhead pressures as part of the comprehensive optimization process rather than requiring separate complex pressure control mechanisms, achieving pressure stability while maintaining operational simplicity through integrated control.
Data Source
AI summary
A method of optimizing production of wells using choke control includes generating, for each well, an intermediate solution to optimize the production of each well. The generating includes using an offline model that includes a mixed-integer nonlinear program solver and includes using production curves based on a choke state and a given wellhead pressure. The method further includes calculating, using a network model and the intermediate solution of each well, a current online wellhead pressure for each well. The method further includes setting the intermediate solution as a final solution based on determining that a difference between the current online wellhead pressure of each well and a prior online wellhead pressure of each well is less than a tolerance amount. The method further includes adjusting, using the final solution of each well, at least one operating parameter of the wells.