Neural Network Optimization for Oil Field Production Plateau
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Solution Overview
Problem
Current methods for determining energy production controls for subterranean hydrocarbon fields are inefficient, relying on imprecise models and complex simulations that are computationally expensive and unsuitable for rapid decision-making, failing to accurately optimize sustained production profiles under uncertainty.
Innovation Solution
A method that generates field production controls by determining a reservoir model, simulating production rates, and optimizing net present value (NPV) through an iterative process, using numerical tools to calculate optimal production rates and well controls, which can sustain a plateau-like production profile over time, even under quantifiable uncertainty.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simulation-based field production optimization is used, then accuracy of production control is improved, but computational cost and time consumption increase significantly
Solution Approach 1:
The patent segments the production optimization problem into two distinct phases: an offline training phase where a neural network is trained using simulation data, and an online execution phase where the trained network rapidly predicts optimal controls. This segmentation allows computationally intensive simulations to be performed only during training, while rapid prediction is achieved during actual decision-making.
Solution Approach 2:
The patent performs preliminary action by pre-training the neural network model offline using extensive simulation data before actual field deployment. The training phase pre-computes the relationship between reservoir states and optimal controls, so that during field operation, only fast neural network inference is needed, avoiding real-time simulation computations.
2Measurement precision
If thousands of reservoir flow simulations are performed for optimization, then solution accuracy is improved, but computational cost becomes prohibitive
Solution Approach 1:
The patent creates a simplified computational copy of the reservoir system using a neural network that mimics the behavior of complex reservoir flow simulations. Instead of repeatedly executing expensive reservoir simulations, the trained neural network copy provides rapid approximations of production responses, enabling extensive optimization iterations at minimal computational cost.
3Productivity
If simple analytical models are used for plateau production estimation, then computational speed is improved, but precision and reliability deteriorate
Solution Approach 1:
The patent transforms the reservoir simulation problem into a different parameter space by training a neural network to map reservoir state parameters directly to optimal production controls and predicted outcomes. This parameter transformation allows the system to capture complex nonlinear reservoir behavior without requiring repeated execution of detailed flow simulations.
Data Source
AI summary
A system, method and a computer program product for to determining energy production controls for a given subterranean hydrocarbon (oil) field production and more particularly to specifying controls for sustaining optimal field production (by means of a plateau-like profile over time).