Smart Well ICV Optimization Using Real-Time Nodal Analysis
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Solution Overview
Problem
Conventional well production optimization methods for multi-segmented wells are often cumbersome and based on trial and error, failing to adjust production in a timely manner, leading to suboptimal performance and increased operating expenditures.
Innovation Solution
Implementing real-time nodal analysis and automated optimization algorithms that combine segment production data and downhole parameter estimations to determine optimal inflow control valve (ICV) settings, enabling immediate system adjustments and continuous optimization of multi-segmented well production.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual trial and error methods are used to adjust ICV settings, then engineers can optimize production, but the process is cumbersome and time-consuming
Solution Approach 1:
The system performs self-optimization by automatically analyzing real-time production data, calculating optimal ICV settings through nodal analysis, and adjusting valve positions without requiring continuous manual intervention from engineers. The automated system serves itself by monitoring its own performance and making corrective adjustments.
Solution Approach 2:
The system implements continuous feedback loops where production data from multiple segments is constantly monitored, compared against target performance, and used to dynamically adjust ICV settings. The real-time data feedback enables the system to detect deviations and automatically correct them, eliminating the delay inherent in manual trial-and-error methods.
2Extent of automation
If real-time automated optimization is implemented, then immediate adjustments can be made, but system complexity increases
Solution Approach 1:
The nodal analysis software serves multiple functions: it models well behavior, analyzes production data, calculates optimal ICV settings, and controls downhole valves. By consolidating these functions into a single integrated system, the patent achieves high automation without proportionally increasing complexity, as the same software platform performs diverse optimization tasks.
Solution Approach 2:
The nodal analysis model acts as an intermediary between raw production data and control actions. It translates complex multi-segment production data into simplified optimal ICV setting recommendations, which are then transmitted to the control system for implementation. This intermediary layer manages complexity by decoupling data analysis from control execution.
3Reliability
If conventional manual optimization is used, then initial production conditions can be optimized, but later life production deteriorates as conditions change
Solution Approach 1:
The system transitions from static initial ICV settings to dynamic real-time adjustment. The ICV positions are continuously modified based on changing production conditions, reservoir pressure, and segment performance. This dynamic approach ensures the system adapts to evolving well conditions throughout its lifecycle, maintaining optimal performance from initial production through later stages.
Solution Approach 2:
The system performs preliminary analysis of production data trends to anticipate future performance deviations. By continuously monitoring segment production rates and bottom-hole pressures, the system proactively adjusts ICV settings before significant performance degradation occurs, preventing rather than merely reacting to production declines.
Data Source
AI summary
Systems and methods include a method for multi-segmented oil production. A multi-segmented well production model representing production at a multi-segmented oil production facility is calibrated. The model models production based on well rates and flowing bottom-hole pressure data at various choke settings for multiple flow conditions for each segment of the multi-segmented well. Real-time updates to the well rates and the flowing bottom-hole pressure data are received. Changes to triggers identifying thresholds for identifying production improvements are received. The model is re-calibrated based on the changes to the triggers and the real-time updates. An optimization algorithm is executed to determine new optimal inflow control valve (ICV) settings. Using the re-calibrated multi-segmented well production model, a determination is made whether the new optimal ICV settings improve production. If so, the optimal ICV settings are provided to a control panel for the multi-segmented oil production facility.