Flow Control Valve Optimization via Neural Network Simulation
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Solution Overview
Problem
The challenge in the oil field services industry is to optimize the control of flow rates in oil wells to maximize revenue, particularly in long horizontal or multi-lateral wells, while dealing with the 'curse of dimensionality' and non-Markovian properties, which make it computationally impractical to derive the optimal control strategy for downhole flow control valves using existing methods.
Innovation Solution
The implementation of approximate approaches such as the rolling-flexible and nearest neighbor policies, which reduce the computational complexity by using a rolling optimizer and approximating the optimal control strategy through a smaller set of simulation scenarios, allowing for efficient real-time flow rate control and valuation of flow-control valves.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional dynamic programming with exhaustive simulation is used to derive optimal control strategy, then solution accuracy is improved, but computational complexity increases exponentially due to curse of dimensionality
Solution Approach 1:
The patent segments the exhaustive simulation problem into two parts: (1) a reduced set of representative simulation scenarios that capture the essential system behavior, and (2) a neural network model that learns from these scenarios to generalize predictions. This segmentation avoids simulating all possible state combinations while maintaining solution accuracy through the neural network's ability to interpolate between training scenarios.
Solution Approach 2:
The patent creates a virtual copy of the complex simulation system in the form of a neural network model. Instead of running actual exhaustive simulations for every decision point, the system trains a neural network on a subset of simulation data and then uses this learned model to approximate the value function and guide control decisions, significantly reducing computational burden.
2Measurement precision
If exhaustive simulation of all possible valve settings and geophysical scenarios is performed, then optimal valuation is improved, but simulation time increases to hundreds of days
Solution Approach 1:
The patent performs preliminary action by pre-selecting and simulating a reduced set of representative scenarios that capture the most important system behaviors and uncertainties. These pre-simulated scenarios are then used to train a neural network that can quickly evaluate new situations without requiring full exhaustive simulation, thus achieving optimal valuation in a fraction of the time.
Solution Approach 2:
The patent changes the parameter of simulation scope from exhaustive (all possible combinations) to selective (representative subset). By carefully choosing which scenarios to simulate based on their representativeness and impact on the value function, the system maintains valuation accuracy while reducing simulation time from hundreds of days to a practical duration.
3Reliability
If the state space includes all possible valve settings and geophysical properties, then solution completeness is improved, but the curse of dimensionality makes the problem computationally intractable
Solution Approach 1:
The patent introduces a neural network as an intermediary between the complex state space and the control decision-making process. The neural network learns the mapping from system states to optimal actions by training on a reduced set of simulated scenarios, acting as a mediator that captures the essential relationships without requiring explicit enumeration of the entire state space.
Solution Approach 2:
The patent transitions from a static exhaustive enumeration approach to a dynamic learning approach. The neural network is trained dynamically on selected scenarios and can adapt to new situations through its learned representations, enabling the system to handle the full state space implicitly through pattern recognition rather than explicit enumeration.
Data Source
AI summary
Apparatus and methods for controlling equipment to recover hydrocarbons from a reservoir including constructing a collection of reservoir models wherein each model represents a realization of the reservoir and comprises a subterranean formation measurement, estimating the measurement for the model collection, and controlling a device wherein the controlling comprises the measurement estimate wherein the constructing, estimating, and/or controlling includes a rolling flexible approach and/or a nearest neighbor approach.