Hierarchical MPC Control for Hydrocarbon Well Production Optimization
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Solution Overview
Problem
Operational challenges in hydrocarbon fields, such as over or under injection/production, impaired well productivity, and poor overall performance, arise from the complexity of managing and deciding on a large number of production and injection wells, which existing technologies struggle to address effectively.
Innovation Solution
Implementing a computer-implemented method using supervisory Model Predictive Control (MPC) and individual MPC or PID controllers to automate the monitoring and control of production and injection wells, breaking down field-wide control into individual well control and coordinating multiple wells to achieve long-term targets while optimizing operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual control methods are used for managing a large number of production and injection wells, then operational flexibility is maintained, but operational efficiency deteriorates due to the complexity of managing and deciding on many wells
Solution Approach 1:
The control system is segmented into multiple independent controllers, each responsible for specific wells or well groups. This allows distributed decision-making across the field, where each controller autonomously manages its assigned wells based on local conditions and field-wide targets, reducing the operational burden while maintaining overall efficiency
Solution Approach 2:
Controllers are designed to autonomously determine operating parameters for their assigned wells without requiring manual intervention for each decision. The system self-manages by automatically adjusting production and injection rates based on real-time measurements and predictive models, eliminating the need for complex manual coordination
2Reliability
If existing control technologies are used, then current operational procedures are maintained, but well performance deteriorates due to over or under injection/production and impaired productivity
Solution Approach 1:
The system continuously monitors actual production and injection measurements from each well and compares them against target values. This feedback loop enables real-time detection of deviations such as over-injection or under-production, allowing the controllers to automatically adjust operating parameters to maintain optimal well performance and prevent productivity impairment
Solution Approach 2:
The predictive control component determines future operating parameters in advance based on predicted field behavior and long-term targets. By calculating optimal production and injection rates before operational changes are needed, the system proactively prevents performance deterioration rather than reacting to problems after they occur
3Productivity
If field-wide control is implemented, then overall field targets are achieved, but individual well optimization deteriorates due to lack of specific well-level attention
Solution Approach 1:
The control architecture divides field-wide management into hierarchical levels: field-level controllers set long-term targets and coordinate overall field performance, while well-level controllers optimize individual well operations. This segmentation allows simultaneous achievement of field-wide production targets and individual well optimization without conflict
Solution Approach 2:
The system merges field-wide control objectives with individual well optimization by integrating multiple controllers that operate at different hierarchical levels. Each controller operates autonomously within its scope but contributes to the overall field target, combining the benefits of centralized coordination and decentralized optimization
4Productivity
If more wells are drilled to increase production, then field output is improved, but operating expenses deteriorate due to increased field development costs
Solution Approach 1:
Instead of increasing the number of wells, the system changes operational parameters such as production rates, injection rates, and well configuration to optimize output from existing wells. By adjusting these parameters dynamically based on real-time conditions and predictive models, the system maintains or increases field production while avoiding the capital and operating expenses associated with drilling additional wells
Data Source
AI summary
In a hydrocarbon field including multiple production wells and injection wells, at a hydrocarbon field level, a long-term field-level target and optional long-term well- level targets for the field are received by a field-level processor. The long-term field- level target including a long-term field-level production target indicating a quantity of hydrocarbons to be produced and a long-term field-level injection target indicating a quantity of fluid to be injected into the field. The field-level processor determines short- term individual production targets for the production wells and short-term individual injection targets for the injection wells to achieve the long-term field-level target. At an individual well level, individual hydrocarbon productions of the production wells or individual fluid injections of the injection wells are controlled, by at least one individual well-level processor independent of the field-level processor, to achieve the long-term field-level target.