Development of a Control-Aware Digital Twin Framework for Real-Time Monitoring and Optimization of Oil and Gas Production Systems

Summary

This study presents a control-aware digital twin framework for monitoring and optimization of oil and gas systems using machine learning and physics-informed models, evaluated on simulated and benchmark datasets.

Abstract

A control-aware digital twin framework for tracking and improving oil and gas production systems is suggested in this paper. The method combines spatiotemporal graph neural networks, neural 4D-Var data assimilation, reinforcement learning-based control, and physics-informed neural operators (FNO and Neural operator model). Benchmark cyber-physical system datasets (SWaT and WADI) and a simulated oil and gas dataset are used to assess the framework. The results, which were confirmed by several independent runs using average measures, demonstrate enhanced performance in state estimation, anomaly detection, and control optimization. However, the assessment is restricted to benchmark and simulated datasets; additional validation using actual industry data is needed to verify practical applicability.

Introduction

The need for safe and effective oil and gas transportation networks has increased due to the world's growing energy demand. However, sustaining pipeline reliability is severely hampered by deteriorating infrastructure and growing system complexity. In this regard, digital twin (DT) technology has drawn interest as a data-driven lifecycle management strategy that permits risk assessment, predictive maintenance, and system optimization in industrial settings^1,2,3,4,5. From reactive maintenance to intelligent, data-driven decision-making, digital tr....

Protocol

This study does not involve any human participants or animal subjects. The datasets used (SWaT and WADI) are publicly available benchmark datasets, and no sensitive or personal data is involved.

The proposed approach establishes a control-conscious digital twin enabling the incorporation of cutting-edge deep learning algorithms, data assimilation, and continuous evaluation and optimization of the oil and gas extraction systems and control. First, real-time multi-modal sensor data from geographically distributed production infrastructure are gathered and modeled using a graph structure that defines connectivity and interactions between produ....

Results

In order to test the validity of the physics-informed neural operators, additional performance tests were conducted to compare the performance of FNO and Neural operator model in modeling the dynamics of the nonlinear multiphase flow system. The results showed that FNO performs better in global spatial consistency, while Neural operator model performs better in adaptability to heterogeneous inputs.

To guarantee effective training and assessment of the suggested digital twin framework, all expe.......

Discussion

The findings from this research demonstrate the effectiveness of the developed control-aware digital twin in terms of its ability to improve real-time monitoring, detection, and control optimization for a wide range of CPS. Compared to the traditional models for monitoring, the combination of the PINO and the graph neural networks is more effective in terms of accuracy for state estimation, even for a noisy system. This finding is true because an advanced machine learning model in the digital twin is more effective in te.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
CPU (Intel Xeon / AMD Ryzen)	Intel / AMD	Xeon / Ryzen Series	Used for system simulation, data preprocessing, and general computation
Deep Learning Framework (PyTorch 2.0)	Meta AI (PyTorch)	https://pytorch.org RRID:SCR_018536	Used for implementing neural networks, optimization, and training pipelines
Deep Neural Network Library (cuDNN 8.x)	NVIDIA cuDNN	cuDNN 8.x	Provides optimized primitives for deep learning operations on GPU
DeepONet	Not Applicable	https://arxiv.org/abs/1910.03193	In the script, it is refered as "neural operator model"
GPU (NVIDIA RTX 4090)	NVIDIA	RTX 4090	Used for training deep learning models including neural operators, ST-GNN, and reinforcement learning components
GPU Acceleration (CUDA 11.8)	NVIDIA CUDA	CUDA 11.8 RRID:SCR_018131	Enables GPU-based parallel computation for high-performance model training
Linux distribution (version 22.04 LTS)	Canonical (Ubuntu)	https://ubuntu.com/download; RRID:SCR_018127	Operating system used for executing experiments and model deployment
Operating System (Ubuntu 22.04 LTS)	Canonical (Ubuntu)	https://ubuntu.com/download RRID:SCR_018127	Provides execution environment for all experiments and model deployment
Optimization Algorithm (Adam Optimizer)	Not Applicable	https://arxiv.org/abs/1412.6980	Used for training models with adaptive learning rate optimization
optimized deep neural network library (version 8.x)	NVIDIA cuDNN	https://developer.nvidia.com/cudnn RRID:SCR_018131	Provides optimized GPU-accelerated primitives for deep neural network operations
Programming Language (Python 3.10)	Python Software Foundation	https://www.python.org/downloads RRID:SCR_008394	Core programming language used for implementation and experimentation
System Memory (≥ 64 GB RAM)	Corsair / Kingston / Crucial	https://www.corsair.com / https://www.kingston.com / https://www.crucial.com	Supports handling of high-dimensional multivariate time-series data and large model training