Prediction of Field Saturations Using a Fully Convolutional Network Surrogate

Abstract

Summary We are interested in the development of surrogate models for the prediction of field saturations using a fully convolutional encoder/decoder network based on the dense convolutional network (DenseNet; Huang et al. 2017), similar to the approaches used for image/image-regression tasks in deep learning. In the surrogate model, the encoder network automatically extracts the multiscale features from the raw input data, and the decoder network then uses these data to recover the input image resolution at the output of the model. The input of multiple influencing factors is considered to make our surrogate model more consistent with the physical laws, which has achieved good results in the prediction of output fields in our experiments. Various reservoir parameters including the static reservoir properties (i.e., permeability field) and dynamic reservoir properties (i.e., well placement) are used as input features, and the water-saturation distributions in different periods are taken as the output. Compared with traditional numerical reservoir simulation, which has a high computational cost and is time consuming, not only does it present the same precision, but it costs less time. At the same time, it can also be used for production optimization and history matching.

Applicability of Anomaly Detection Knowledge from Computer Science and Mathematics Publications to Oil and Gas Research

“…We therefore provide a complete list of their subclusters, emphasizing the methods used. ANOMALY DETECTION (339 docs, 14 subclusters); Intrusion Detection (72), Time Series Data (72), Outlier Detection (51), Long Short-term Memory (45), Support Vector Machine (38), Log Anomaly Detection (32), Random Forest (29), Anomalous Behavior (30), Data Mining (29), Cyber-physical System (23), Video Surveillance (18), Industry 4.0 (19), Attention Mechanism (17), Fraud Detection (16) NEURAL NETWORK (373 docs, 14 subclusters); Detection System (143), Convolution Neural Network (105), Long Short-term Memory (73), Recurrent Neural Network (67), Artificial Neural Network (57), Support Vector Machine (38), Random Forest (37), Attention Mechanism (23), Graph Neural Network (23), Computer Vision (21), Generative Adversarial Network (18), Adversarial Attack (10), Activity Recognition (10), Fraud Detection (5).…”

“…DEEP LEARNING (305 docs, 14 subclusters). Detection System (147), Intrusion Detection (56), Long Short-term Memory (51), Convolution Neural Network (48), Recurrent Neural Network (44), Computer Vision (30), Random Forest (28), Artificial Intelligence (29), Generative Adversarial Network (21), Big Data (20), Reinforcement Learning (18), Video Surveillance ( 16), Defect Detection (15), Adversarial Attack (13).…”

Exploiting node-feature bipartite graph in graph convolutional networks

“…A number of studies have applied image-based approaches and snapshots of simulation data over a spatially discretized input domain for surrogate modeling of subsurface flow and transport problems. Most of these works leverage convolutional neural networks (CNNs) to learn the nonlinear mappings from the input properties (e.g., permeability) to the output states (pressure and saturation) on regular Cartesian meshes (Mo et al 2019;Tang et al 2020;Wang and Lin 2020;Wen et al 2021;Zhang et al 2021;Jiang et al 2021;Yan et al 2022;Maldonado-Cruz and Pyrcz 2022). While CNNs are powerful in approximating PDE solutions, they are restricted to a specific discretization of the physical domain in which they are trained.…”

Data-driven modelling with coarse-grid network models

“…Ideas from data-driven methods has become increasingly popular for modelling flow in subsurface reservoirs in recent years, and in particular as a means to reduce the computational cost of evaluating the forward model in model-driven production optimization and optimized field development [1][2][3][4][5][6][7][8]. Herein, we consider the somewhat different task of rapidly building a flow model based on well responses but limited geological and petrophysical data.…”