Sparse Water Fracture Channel Detection from Subsurface Sensors Via a Smart Orthogonal Matching Pursuit

2021 ◽  
Author(s):  
Klemens Katterbauer ◽  
Abdallah Al Shehri ◽  
Alberto Marsala
Keyword(s):  
Deep Learning ◽  
Monitoring System ◽  
Water Saturation ◽  
Matching Pursuit ◽  
Base Station ◽  
Carbonate Reservoir ◽  
Subsurface Reservoir ◽  
Front Movement ◽  
Water Front

Abstract Water front movement in fractured carbonate reservoirs occurs in micro-fractures, corridors and interconnected fracture channels (above 5 mm in size) that penetrate the carbonate reservoir structure. Determining the fracture channels and the water front movements within the flow corridors is critical to optimize sweep efficiency and increase hydrocarbon recovery. In this work, we present a new smart orthogonal matching pursuit (OMP) algorithm for water front movement detection in carbonate fracture channels. The method utilizes a combined artificial intelligence) AI-OMP approach to first analyze and extract the potential fracture channels and then subsequently deploys a deep learning approach for estimating the water saturation patterns in the fracture channels. The OMP utilizes the sparse fracture to sensor correlation to determine the fracture channels impacting each individual sensor. The deep learning method then utilizes the fracture channel estimates to assess the water front movements. We tested the AI-OMP framework on a synthetic fracture carbonate reservoir box model exhibiting a complex fracture system. Fracture Robots (FracBots, about 5mm in size) technology will be used to sense key reservoir parameters (e.g., temperature, pressure, pH and other chemical parameters) and represent an important step towards enhancing reservoir surveillance (Al Shehri, et al. 2021). The technology is comprised of a wireless micro-sensor network for mapping and monitoring fracture channels in conventional and unconventional reservoirs. The system establishes wireless network connectivity via magnetic induction (MI)-based communication, since it exhibits highly reliable and constant channel conditions with sufficiently communication range inside an oil reservoir environment. The system architecture of the FracBots network has two layers: FracBot nodes layer and a base station layer. A number of subsurface FracBot sensors are injected in the formation fracture channels to record data affected by changes in water saturation. The sensor placement can be adapted in the reservoir formation in order to improve sensor measurement data quality, as well as better track the penetrating water fronts. They will move with the injected fluids and distribute themselves in the fracture channels where they start sensing the surrounding environment’s conditions; they communicate the data, including their location coordinates, among each other to finally transmit the information in multi-hop fashion to the base station installed inside the wellbore. The base station layer consists of a large antenna connected to an aboveground gateway. The data collected from the FracBots network are transmitted to the control room via aboveground gateway for further processing. The results exhibited strong estimation performance in both accurately determining the fracture channels and the saturation pattern in the subsurface reservoir. The results indicate that the framework performs well; especially for fracture channels that are rather shallow (about 20 m from the wellbore) with significant changes in the saturation levels. This makes the in-situ reservoir sensing a viable permanent reservoir monitoring system for the tracking of fluid fronts, and determination of fracture channels. The novel framework presents a vital component in the data analysis and interpretation of subsurface reservoir monitoring system of fracture channels flow in carbonate reservoirs. The results outline the capability of in-situ reservoir sensors to deliver accurate tracking water-fronts and fracture channels in order to optimize recovery.

scholarly journals In-Situ Pipeline Monitoring System Design for Identifying and Locating Damaging Activities Based on Wireless Sensor Network

2020 ◽  
pp. 33-46
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Monitoring System ◽  
Remote Monitoring ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Monitoring Methods ◽  
Pipeline Monitoring

Due to the recent advancements in the fields of Micro Electromechanical Sensors (MEMS), communication, and operating systems, wireless remote monitoring methods became easy to build and low cost option compared to the conventional methods such as wired cameras and vehicle patrols. Pipeline Monitoring Systems (PMS) benefit the most of such wireless remote monitoring since each pipeline would span for long distances up to hundreds of kilometers. However, precise monitoring requires moving large amounts of data between sensor nodes and base station for processing which require high bandwidth communication protocol. To overcome this problem, In-Situ processing can be practiced by processing the collected data locally at each node instead of the base station. This Paper presents the design and implementation of In-situ pipeline monitoring system for locating damaging activities based on wireless sensor network. The system built upon a WSN of several nodes. Each node contains high computational 1.2GHz Quad-Core ARM Cortex-A53 (64Bit) processor for In-Situ data processing and equipped in 3-axis accelerometer. The proposed system was tested on pipelines in Al-Mussaib gas turbine power plant. During test knocking events are applied at several distances relative to the nodes locations. Data collected at each node are filtered and processed locally in real time in each two adjacent nodes. The results of the estimation is then sent to the supervisor at base-station for display. The results show the proposed system ability to estimate the location of knocking event.

scholarly journals Q-Meter: Quality Monitoring System for Telecommunication Services Based on Sentiment Analysis Using Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1880
Author(s):  
Samuel Terra Vieira ◽  
Renata Lopes Rosa ◽  
Demóstenes Zegarra Rodríguez ◽  
Miguel Arjona Ramírez ◽  
Muhammad Saadi ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Monitoring System ◽  
Online Social Networks ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Base Station ◽  
Quality Monitoring ◽  
Telecommunication Services

A quality monitoring system for telecommunication services is relevant for network operators because it can help to improve users’ quality-of-experience (QoE). In this context, this article proposes a quality monitoring system, named Q-Meter, whose main objective is to improve subscriber complaint detection about telecommunication services using online-social-networks (OSNs). The complaint is detected by sentiment analysis performed by a deep learning algorithm, and the subscriber’s geographical location is extracted to evaluate the signal strength. The regions in which users posted a complaint in OSN are analyzed using a freeware application, which uses the radio base station (RBS) information provided by an open database. Experimental results demonstrated that sentiment analysis based on a convolutional neural network (CNN) and a bidirectional long short-term memory (BLSTM)-recurrent neural network (RNN) with the soft-root-sign (SRS) activation function presented a precision of 97% for weak signal topic classification. Additionally, the results showed that 78.3% of the total number of complaints are related to weak coverage, and 92% of these regions were proved that have coverage problems considering a specific cellular operator. Moreover, a Q-Meter is low cost and easy to integrate into current and next-generation cellular networks, and it will be useful in sensing and monitoring tasks.

A Novel Deep Reinforcement Sensor Placement Method for Waterfront Tracking

2021 ◽  
Author(s):  
Klemens Katterbauer ◽  
Abdallah Al Shehri ◽  
Alberto Marsala
Keyword(s):  
Reinforcement Learning ◽  
Water Saturation ◽  
Learning Algorithm ◽  
Sensor Placement ◽  
Base Station ◽  
Carbonate Reservoir ◽  
Carbonate Reservoirs ◽  
Sensor Data ◽  
Sweep Efficiency ◽  
Reservoir Monitoring

Abstract Waterfront movement in fractured carbonate reservoirs occurs in micro-fractures, corridors and interconnected fracture channels (above 5 mm in size) that penetrate the carbonate reservoir structure. Determining the fracture channels and the waterfront movements within the flow corridors is critical to optimize sweep efficiency and increase hydrocarbon recovery. In this work, we present a new deep reinforcement learning algorithm for the optimization of sensor placement for waterfront movement detection in carbonate fracture channels. The framework deploys deep reinforcement learning approach for optimizing the location of sensors within the fracture channels to enhance waterfront tracking. The approach first deploys the deep learning algorithm for determining the water saturation levels within the fractures based on the sensor data.. Then, it updates the sensor locations in order to optimize the reservoir coverage. We test the deep reinforcement learning framework on a synthetic fracture carbonate reservoir box model exhibiting a complex fracture system. Fracture Robots (FracBots, around 5 mm in size) technology will be used to sense key reservoir parameters (e.g., temperature, pressure, pH and other chemical parameters). The technology is comprised of a wireless micro-sensor network for mapping and monitoring fractures in conventional and unconventional reservoirs [1]. It establish a wireless network connectivity via magnetic induction (MI)-based communication since it exhibits highly reliable and constant channel conditions with sufficient communication range in the oil reservoir environment. The system architecture of the FracBots network has two layers: FracBot nodes layer and a base station layer. A number of subsurface FracBot sensors are injected in the formation fractures that record data affected by changes in water saturation. The sensor placement can be adapted in the reservoir formation to improve sensor data quality, as well as better track the penetrating waterfronts. They will move with the injected fluids and distribute themselves in the fractures where they start sensing the surrounding environment's conditions and communicate data, including their location coordinates, among each other to finally send the information in multi-hop fashion to the base station installed inside the wellbore. The base station layer consists of a large antenna connected to an aboveground gateway. The data collected from the FracBots network will be transmitted to the control room via aboveground gateway for further processing. The results exhibited resilient performance in updating the sensor placement to capture the penetrating waterfronts in the formation. The framework performs well particularly when the distance between the sensors is sufficient to avoid measurement interference. The framework demonstrates the criticality of adequate sensor placement in the reservoir formation for accurate waterfront tracking. Also, it shows that itis a viable solution to optimize sensor placement for reservoir monitoring. This novel framework presents a vital component in the data analysis and interpretation of subsurface reservoir monitoring system for carbonate reservoirs. The results outline the opportunity to deploy advanced artificial intelligence algorithms, such as deep reinforcement methods, to optimally place downhole sensors to achieve best measurement success, and track the waterfronts as well as determine sweep efficiency.

scholarly journals A Generalization Performance Study Using Deep Learning Networks in Embedded Systems

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1031
Author(s):  
Joseba Gorospe ◽  
Rubén Mulero ◽  
Olatz Arbelaitz ◽  
Javier Muguerza ◽  
Miguel Ángel Antón
Keyword(s):  
Deep Learning ◽  
Embedded Systems ◽  
Embedded System ◽  
General Purpose ◽  
Learning Networks ◽  
Performance Study ◽  
Learning Techniques ◽  
Wide Range ◽  
Learning Architectures

Deep learning techniques are being increasingly used in the scientific community as a consequence of the high computational capacity of current systems and the increase in the amount of data available as a result of the digitalisation of society in general and the industrial world in particular. In addition, the immersion of the field of edge computing, which focuses on integrating artificial intelligence as close as possible to the client, makes it possible to implement systems that act in real time without the need to transfer all of the data to centralised servers. The combination of these two concepts can lead to systems with the capacity to make correct decisions and act based on them immediately and in situ. Despite this, the low capacity of embedded systems greatly hinders this integration, so the possibility of being able to integrate them into a wide range of micro-controllers can be a great advantage. This paper contributes with the generation of an environment based on Mbed OS and TensorFlow Lite to be embedded in any general purpose embedded system, allowing the introduction of deep learning architectures. The experiments herein prove that the proposed system is competitive if compared to other commercial systems.

scholarly journals Power Electric Transformer Fault Diagnosis Based on Infrared Thermal Images Using Wasserstein Generative Adversarial Networks and Deep Learning Classifier

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1161
Author(s):  
Kuo-Hao Fanchiang ◽  
Yen-Chih Huang ◽  
Cheng-Chien Kuo
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Monitoring System ◽  
Absolute Difference ◽  
Generative Adversarial Networks ◽  
Economic Losses ◽  
Maintenance Cost ◽  
Full Time ◽  
Noise Interference ◽  
In Series

The safety of electric power networks depends on the health of the transformer. However, once a variety of transformer failure occurs, it will not only reduce the reliability of the power system but also cause major accidents and huge economic losses. Until now, many diagnosis methods have been proposed to monitor the operation of the transformer. Most of these methods cannot be detected and diagnosed online and are prone to noise interference and high maintenance cost that will cause obstacles to the real-time monitoring system of the transformer. This paper presents a full-time online fault monitoring system for cast-resin transformer and proposes an overheating fault diagnosis method based on infrared thermography (IRT) images. First, the normal and fault IRT images of the cast-resin transformer are collected by the proposed thermal camera monitoring system. Next is the model training for the Wasserstein Autoencoder Reconstruction (WAR) model and the Differential Image Classification (DIC) model. The differential image can be acquired by the calculation of pixel-wise absolute difference between real images and regenerated images. Finally, in the test phase, the well-trained WAR and DIC models are connected in series to form a module for fault diagnosis. Compared with the existing deep learning algorithms, the experimental results demonstrate the great advantages of the proposed model, which can obtain the comprehensive performance with lightweight, small storage size, rapid inference time and adequate diagnostic accuracy.

scholarly journals Deep Learning for Monitoring Agricultural Drought in South Asia Using Remote Sensing Data

2021 ◽  
Vol 13 (9) ◽  
pp. 1715
Author(s):  
Foyez Ahmed Prodhan ◽  
Jiahua Zhang ◽  
Fengmei Yao ◽  
Lamei Shi ◽  
Til Prasad Pangali Sharma ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
South Asia ◽  
Model Performance ◽  
Remote Sensing Data ◽  
Agricultural Drought ◽  
Gradient Boosting ◽  
Complex Nature ◽  
Sensing Data

Drought, a climate-related disaster impacting a variety of sectors, poses challenges for millions of people in South Asia. Accurate and complete drought information with a proper monitoring system is very important in revealing the complex nature of drought and its associated factors. In this regard, deep learning is a very promising approach for delineating the non-linear characteristics of drought factors. Therefore, this study aims to monitor drought by employing a deep learning approach with remote sensing data over South Asia from 2001–2016. We considered the precipitation, vegetation, and soil factors for the deep forwarded neural network (DFNN) as model input parameters. The study evaluated agricultural drought using the soil moisture deficit index (SMDI) as a response variable during three crop phenology stages. For a better comparison of deep learning model performance, we adopted two machine learning models, distributed random forest (DRF) and gradient boosting machine (GBM). Results show that the DFNN model outperformed the other two models for SMDI prediction. Furthermore, the results indicated that DFNN captured the drought pattern with high spatial variability across three penology stages. Additionally, the DFNN model showed good stability with its cross-validated data in the training phase, and the estimated SMDI had high correlation coefficient R2 ranges from 0.57~0.90, 0.52~0.94, and 0.49~0.82 during the start of the season (SOS), length of the season (LOS), and end of the season (EOS) respectively. The comparison between inter-annual variability of estimated SMDI and in-situ SPEI (standardized precipitation evapotranspiration index) showed that the estimated SMDI was almost similar to in-situ SPEI. The DFNN model provides comprehensive drought information by producing a consistent spatial distribution of SMDI which establishes the applicability of the DFNN model for drought monitoring.

Simulation of Miscible Displacement in Full-Diameter Carbonate Cores

1982 ◽  
Vol 22 (05) ◽  
pp. 647-657 ◽  
Author(s):  
J.P. Batycky ◽  
B.B. Maini ◽  
D.B. Fisher
Keyword(s):  
Oil Recovery ◽  
Water Saturation ◽  
Accurate Determination ◽  
Miscible Displacement ◽  
Carbonate Reservoir ◽  
Core Material ◽  
Immiscible Fluid ◽  
Displacement Efficiency ◽  
Carbonate Cores ◽  
Capacitance Model

Abstract Miscible gas displacement data obtained from full-diameter carbonate reservoir cores have been fitted to a modified miscible flow dispersion-capacitance model. Starting with earlier approaches, we have synthesized an algorithm that provides rapid and accurate determination of the three parameters included in the model: the dispersion coefficient, the flowing fraction of displaceable volume, and the rate constant for mass transfer between flowing and stagnant volumes. Quality of fit is verified with a finite-difference simulation. The dependencies of the three parameters have been evaluated as functions of the displacement velocity and of the water saturation within four carbonate cores composed of various amounts of matrix, vug, and fracture porosity. Numerical simulation of a composite core made by stacking three of the individual cores has been compared with the experimental data. For comparison, an analysis of Berea sandstone gas displacement also has been provided. Although the sandstone displays a minor dependence of gas recovery on water saturation, we found that the carbonate cores are strongly affected by water content. Such behavior would not be measurable if small carbonate samples that can reflect only matrix properties were used. This study therefore represents a significant assessment of the dispersion-capacitance model for carbonate cores and its ability to reflect changes in pore interconnectivity that accompany water saturation alteration. Introduction Miscible displacement processes are used widely in various aspects of oil recovery. A solvent slug injected into a reservoir can be used to displace miscibly either oil or gas. The necessary slug size is determined by the rate at which deterioration can occur as the slug is Another commonly used miscible process involves addition of a small slug within the injected fluids or gases to determine the nature and extent of inter well communication. The quantity of tracer material used is dictated by analytical detection capabilities and by an understanding of the miscible displacement properties of the reservoir. We can develop such understanding by performing one-dimensional (1D) step-change miscible displacement experiments within the laboratory with selected reservoir core material. The effluent profiles derived from the experiments then are fitted to a suitable mathematical model to express the behavior of each rock type through the use of a relatively small number of parameters. This paper illustrates the efficient application of the three-parameter, dispersion-capacitance model. Its application previously has been limited to use with small homogeneous plugs normally composed of intergranular and intencrystalline porosity, and its suitability for use with cores displaying macroscopic heterogeneity has been questioned. Consequently, in addition to illustrating its use with a homogeneous sandstone, we fit data derived from previously reported full-diameter carbonate cores. As noted earlier, these cores were heterogeneous, and each of them displayed different dual or multiple types of porosity characteristic of vugular and fractured carbonate rocks. Dispersion-Capacitance Model The displacement efficiency of one fluid by a second immiscible fluid within a porous medium depends on the complexity of rock and fluid properties. SPEJ P. 647^

An in-situ monitoring system on the grinding process

2010 ◽  
Author(s):  
ByoungChang Kim ◽  
MinCheol Kwon ◽  
JaeBoong Ha ◽  
KangWoo Lee
Keyword(s):  
Monitoring System ◽  
In Situ Monitoring ◽  
Grinding Process
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