SAGD Control Model Optimizing Steam Injection and Emulsion Yield
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Solution Overview
Problem
Steam-assisted gravity drainage (SAGD) oil well systems face challenges in optimizing emulsion production due to uncertainties in physics-based predictive models and limitations of statistical approaches, which lead to biased predictions and inefficient steam distribution, resulting in high costs and suboptimal yield.
Innovation Solution
A multiple time step modeling approach is employed, using historical data from sensors to generate and train a predictive model that adjusts control parameters for SAGD systems across multiple time steps, optimizing emulsion production while adhering to operational constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physics-based predictive models are used for SAGD systems, then prediction capability is improved, but model uncertainty and reliability worsen
Solution Approach 1:
The system implements continuous feedback by monitoring actual production data from sensors and comparing it against model predictions. This feedback loop allows for real-time model calibration and adjustment, reducing model uncertainty while maintaining prediction capability. The feedback mechanism enables the system to learn from discrepancies between predicted and actual outcomes, progressively improving reliability.
Solution Approach 2:
The system dynamically adjusts model parameters based on changing operational conditions and accumulated data. By modifying parameters such as steam injection rates, wellbore pressures, and thermal properties in response to real-time measurements, the system adapts the physics-based model to current system states, thereby reducing uncertainty while preserving predictive accuracy.
2Ease of operation
If statistical approaches are used for SAGD systems, then ease of operation is improved, but prediction accuracy and productivity worsen due to biased predictions
Solution Approach 1:
The system replaces pure statistical approaches with a physics-based modeling framework that incorporates fundamental thermodynamic and fluid flow equations. This substitution eliminates the biased predictions inherent in statistical methods while maintaining ease of operation through automated model execution and interpretation, thereby improving emulsion production efficiency without sacrificing operational simplicity.
Solution Approach 2:
The system introduces an intermediary layer between operational controls and production outcomes by implementing a physics-based predictive model. This intermediary translates control parameter adjustments into predicted production results based on fundamental physical principles, providing more accurate and unbiased predictions compared to direct statistical correlations, thus improving productivity while remaining easy to operate.
3Productivity
If steam injection is increased to improve emulsion production, then productivity is improved, but steam requirements and energy consumption worsen
Solution Approach 1:
The system implements dynamic control of steam injection parameters, continuously adjusting injection rates, temperatures, and timing based on real-time system state and predictive model outputs. This dynamic approach optimizes steam utilization by injecting steam only when and where needed to maximize emulsion production, thereby improving productivity while reducing overall steam requirements and energy consumption compared to static high-injection strategies.
Solution Approach 2:
The system performs preliminary actions by using the predictive model to forecast optimal steam injection schedules and parameters before actual injection occurs. This advance planning allows for precise steam allocation that maximizes production efficiency, avoiding excessive steam injection and reducing overall steam requirements while maintaining high emulsion production levels.
4Productivity
If control parameters are adjusted frequently to optimize production, then productivity is improved, but system complexity and difficulty of operation worsen
Solution Approach 1:
The system implements self-service by automating the control parameter adjustment process through an integrated predictive model and control algorithm. The system autonomously monitors production data, predicts optimal parameters, and adjusts controls without requiring complex manual intervention or sophisticated operator expertise. This automation maintains high productivity through frequent parameter optimization while keeping the system easy to operate by eliminating the need for operators to manage the complexity directly.
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
This approach enhances emulsion production efficiency by optimizing control parameters across longer time horizons, reducing steam requirements, and improving overall system productivity, leading to higher returns and more effective resource management.
Implementation Method 1
steam is injected through one or more injector wells and oil is extracted through one or more producer wells
Implementation Method 2
steam-assisted gravity drainage (SAGD) oil well system
Data Source
AI summary
A method for increasing efficiency in emulsion production for a steam-assisted gravity drainage (SAGD) oil well system includes generating a multiple time step model of the SAGD oil well system and training the multiple time step model of the SAGD oil well system utilizing historical time series data relating to one or more SAGD oil wells at one or more SAGD production sites of the SAGD oil well system. The historical time series data is obtained from a plurality of sensors in the SAGD oil well system. The method also includes utilizing the multiple time step model to determine, based on a set of objectives and subject to one or more constraints, two or more sets of values for control parameters associated with one or more of the SAGD production sites of the SAGD oil well system for respective ones of two or more time steps. The method further includes adjusting, in each of the two or more time steps, a set of controls of the SAGD oil well system based on the determined values of the control parameters for the corresponding time step.