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Methane (CH4), the primary constituent of natural gas and is the second-most abundant greenhouse gas after carbon dioxide (CO2), accounts for 16% of global emissions. The lifetime of methane in the atmosphere is much shorter than CO2, but CH4 is more efficient at trapping radiation than CO2. Pound for pound, the comparative effect of CH4 is more than 25 times greater than CO2 over a 100-year period. Natural-gas emissions from oil and gas facilities such as well sites, refineries, and compressor stations can have significant safety, economic, and regulatory effects. Continuous emission detection systems enable rapid identification and response to unintended emission events.
Fractures Robots (FracBots) are underground IoT (internet of things) wireless sensor nodes designed to be deployed in oil/gas reservoirs to map hydraulic fractures (HF) and monitor in real-time main reservoir parameters including pressure and temperature in real-time. A significant amount of FracBots will be injected inside hydraulic fractures immediately after conducting the fracturing job to gain the required data. They will launch the network connectivity in a fashion of FracBot-to-FracBot to collect, exchange data and transmit it to the base station installed in the wellbore. FracBots technology employs the same concept of the wireless underground sensor network (WUSN), a very capable technology for the applications in hydraulic fractures network. However, developing such technology is exceptionally challenging due to many environmental complications. The main challenges are severe environment, size, and energy limitations which contribute heavily to destroying the quality of the underground wireless communication link. FracBots technology comprises a gateway, a base station (BS), and FracBots. The main role of the BS is to transfer power to FracBots for charging purpose, gather data from FracBots, and finally transmit data to a gateway that send them to a centralized computer for further processing. The function of FracBot nodes is to sense the required data, send it wirelessly among them in multi-hop fashion and then communicate data to BS.
This paper discusses the available wireless communication techniques including electromagnetic waves (EM) and magnetic induction (MI) wireless communication that might be suitable candidates for hydraulic fractures environment. They are compared in terms of the path loss model, path loss parameters, and the advantages and disadvantages of each technique. Moreover, we provide several simulations showing the importance of omnidirectional antenna and the effect of an alignment angle (between the axes of transmitter and receiver) on the path loss of the MI network. Also, we analyze the environmental effects on the path loss inside hydraulic fractures. After studying both EM and MI techniques in fractures environment. We found that the heterogenous environment stiffly influences the EM and MI communications inside hydraulic fractures in oil reservoirs. A hybrid solution that combines both EM and MI communication technologies will be very suitable for such an environment.
The power generation industry is seeking solutions to prevent failures in high energy piping systems including main steam and hot reheat steam pipelines. A thorough review of prior failures in these systems has shown that 60-70% of all failures can be attributed to hanger and strain monitoring systems not performing within specification for extended periods of times (typically 1-5 years). The typical failure mode has been creep in seam welds. The consequence of failure for high energy piping can be severe and the effect of unexpected or unknown displacements cannot be quantified when estimating remaining life. In this case study, we developed wireless LIDAR sensor networks that can be installed on either hangers or above insulation on these piping systems. The purpose of the sensor network is to protect life, property, and environment by reducing the likelihood catastrophic failures of high energy piping. The wireless sensors would communicate with the central control system at a power station and alert plant staff when the piping strain / bulk displacement is outside of design limits. A Bayesian network (BN) model was created which links the piping displacement to the remaining creep life predicted based on API (1) 579-1 “Fitness-For-Service”. Therefore, by knowing real-time pipe displacement measured using wireless sensor network, the remaining creep life can be dynamically predicted though the Bayesian network model.
The power generation industry is seeking to develop solutions to prevent failures in high energy piping systems. There are two systems of concern. The first system is Main Steam pipe (540°C, 24.13 MPa, ∼50 cm diameter piping) and the second system is a Hot Reheat (540°C, 4.83 MPa, also large diameter) steam pipeline. A thorough review of prior failures in these systems has shown that 60-70% of all failures in these systems can be attributed to hanger and strain monitoring systems not performing within specification for extended periods of times (typically 1-5 years). The typical failure mode has been creep in seam welds. The primary issue is increased stress in isolated locations where hanger systems are not functioning within design requirements. Stress levels can increase 2-3 times beyond typical design levels, which drive the isolated location to artificially age a weldment and result in premature failure. Welded repairs of a system typically require extensive costs. Scaffolding, welding, heat treatment, and volumetric inspection are required of all repairs given that the wall thicknesses of these components are typically 5-15 cm thick. These estimates do not count lost generation during a repair which can easily exceed $100-thousand per day.
The ability to provide a stable and durable energy supply for sensing nodes in a wireless sensors network (WSN) is an important research issue for WSNs in structural health monitoring (SHM) systems. Furthermore, a common approach to reduce energy consumption of sensors in a WSN is having control sensors periodically enter a low-power mode or sleep state. This, however, is challenging to implement in large-scale WSNs due to the need for faultless time synchronization. In practice, the sampling process will be delayed if a sensor node receives a sampling command but the node remains in listening-time cycle. Hence, the capabilities of external-radio triggering can improve stability and durability when integrated with a peripheral low-power circuit attached to sensing nodes. If sensors are in a low-power mode or sleep state, an effective approach is to transmit a wake-up command when specific start-up conditions are met to quickly awaken sensing nodes and work wirelessly. The aim of this work is to develop an efficient energy management scheme for a WSN-based SHM system by integrating an on-site earthquake early warning system and wake-on radio. A coordinator is integrated with WSN gateways and employed to link and synchronize all sensing nodes in advance through seismic prediction and radio-triggering technology. The simulation results reveal that the average power consumed was measured at about 350 μA for sensing nodes. Such a sensor will be more effective in measuring structural responses after an earthquake by increasing available sleep time, thereby saving energy and extending the life of such a wireless sensing system.
A building may sustain damage when subjected to a severe load such as a strong earthquake or when its structural materials deteriorate. Consequently, monitoring the structural health of buildings and civil infrastructure has attracted considerable interest over the past decade (Lynch and Loh, 2006; Jo et al., 2010; Rice et al., 2010). The paradigm of global structural health monitoring (SHM) research conventionally adopted vibration-based (acceleration-based) methods (Doebling et al., 1996, Sohn et al., 2003, Carden and Fanning, 2004). These approaches identify structural damage by detecting modal property changes, such as natural frequencies, modal damping, or mode shape. Thus, densely distributed sensors are critical to ensuring the efficiency of vibration-based damage identification. Also, optimal placement of sensors for SHM is an important issue, but not considered in this work. Wireless sensor networks (WSNs) are an attractive sensing technology in which densely distributed sensors are deployed to measure structural responses and external excitations due to their low manufacturing costs, low power requirements, miniaturize size, and lack of cables (Lin et al., 2012; Noel et al. 2017). But measuring structural response by earthquake excitation is very difficult owing to their unpredictability. Earthquake early warning (EEW) systems, however, helps to mitigate the uncertainty of event detection, and as such, the integration of EEW and SHM systems is valuable to reduce unnecessary sampling events.
Lithium-ion batteries are currently the predominant energy source for sensing nodes in a WSN-based system. A serious problem is that the amount of energy needed for energy harvesting methods or advanced power management is severely limited. In commonly known WSN platforms, their power managements are usually divided into several grades: (1) the active processing mode, where the CPU will execute at the selected clock speed; (2) the CPU doze mode, where the processor clock is stopped, although all other parts of the device continue to run; and (3) deep sleep mode, which consumes the least power. This mode can be excited by a power down, a hardware reset, or a general purpose I/O (GPIO) event (e.g., internal wakeup timers or external interrupt).
Lu, Ping (AECOM/National Energy Technology) | Wright, Ruishu F. (ORISE/National Energy Technology Laboratory) | Ziomek-Moroz, Margaret (National Energy Technology Laboratory) | Buric, Michael P. (National Energy Technology Laboratory) | Zandhuis, Paul (AECOM/National Energy Technology) | Ohodnicki, Paul R. (National Energy Technology Laboratory)
A novel optical fiber sensor is being developed to monitor the internal corrosion processes in the natural gas transmission pipelines. Water droplet detection and ionic strength monitoring have been experimentally demonstrated by using the proposed sensor. This fiber optic sensor provides a multifunctional platform for monitoring several major processes involved with internal corrosion, including water dropwise condensation, dissolution of corrosive gases, and corrosion product formation on the steel surface, as well as changes in environmental parameters such as temperature and stress in steel pipes. Furthermore, a distributed sensor interrogation system combining several spatially resolved demodulation schemes is proposed to determine the location of incipient corrosion events and produce distributed information about the potential for on-set of corrosion along the path of the optical fiber sensor.
Natural gas occupies nearly 30% of the energy consumption in the United States in 2016 according to Energy Information Administration. There are more than 528,000 km of natural gas transmission and gathering lines, and significant maintenance costs for pipeline operation is associated with corrosion control and integrity management. According to Pipeline and Hazardous Materials Safety Administration database, corrosion accounted for ~25% of the natural gas transmission and gathering pipeline incidents over the last 30 years, 61% of which was caused by internal corrosion. The corrosion-related annual cost is $6-$10 billion in the transmission pipeline industry in the United States. Therefore, it is important to monitor corrosion inside the gas pipelines to implement corrosion mitigation and control before any failure. Top-of-the-Line corrosion (TLC) is a phenomenon encountered in the natural gas transmission pipelines when internal corrosion occurs due to water vapor condensation and dissolved corrosive substances.1-5 When the corrosion inhibitors are injected into the pipe operated in stratified gas-liquid pipe flow, they remain at the bottom of the line and are not able to protect the top of the line. Despite of an upstream gas dehydration treatment, liquid water can still form through the condensation of water vapor in the gas phase on the internal upper pipe walls due to the heat exchange occurring between the pipe and colder environments. In addition, glycol used in the gas dehydration units is introduced to the pipelines as a water/glycol mixture through mist carryover. Thus the condensed liquid becomes enriched by various corrosive species present in the gas stream and assumes a low pH from dissolved acid gases such as inherently existing CO2 and H2S as it is unbuffered. The pH rises as TLC occurs because the saline droplets become saturated and supersaturated with corrosion products. Corrosion sensing plays an important role in the activities of large infrastructure health monitoring in industry sectors. Optical fibers have shown great potential as a sensing platform for in-situ corrosion monitoring and early detection, providing an effective way to assess the structural health of the natural gas pipelines and enhance the safe operation of natural gas pipelines.6-15 In this work, a fiber-optic sensor network for internal corrosion monitoring in gas pipelines is proposed. A multifunctional fiber-optic sensor (FOS) can perform precise localized multi-parameter measurements of condensing water properties. A fiber-optic sensor array (FSA) consists of a sequence of FOS concatenated in a two-dimensional configuration and reflects the state of the environment in which it is physically placed in a local area to assist in the prediction of the corrosion rate and evaluation of the corrosion level. The FOS interfaces with a distributed sensor interrogation (DSI) system with high spatial resolution and large measurement range through the FSA module, achieving a spatially resolved evaluation of the tendency of corrosion. This paper is organized as follows. Working principle of proposed FOS as well as first demonstration of water droplet detection is given in the next section. Then experimental results of ionic strength monitoring and other multiparameter sensing are shown in the following sections. Lastly, spatially resolved sensing algorithm by using a multiplexed fiber-optic corrosion sensor network is discussed.
Miniaturized transponder systems for mapping hydraulic fractures, monitoring unconventional reservoirs and measuring other wellbore parameters are under development. These devices, called FracBots (Fracture Robots), are envisioned as an extension of RFID (Radio Frequency IDentifcation) tags to realize Wireless Underground Sensor Networks (WUSNs) for mapping and characterization of hydraulic fractures in unconventional reservoirs. When injected during hydraulic fracturing operations, autonomous localization algorithms could be used to build up 3D constellation maps of proppant bed placement. To explore this concept, a FracBot prototype platform was developed, with which three key functions have been demonstrated, and are here reported. First, we developed a novel cross-layer communication framework for Magnetic Induction (MI) -based FracBot networks in dynamically changing underground environments, combining joint selection of modulation, channel coding and power control, a transmitter-based CDMA scheme and a geographic forwarding paradigm. Second, we developed a novel MIbased localization framework which exploits the unique properties of the MI field to determine the locations of the randomly deployed FracBot nodes. Third, we developed an accurate energy model framework for a linear FracBot network topology that gives feasible FracBots' transmission rates and FracBot network topology while respecting harvested energy constraints. Future work will include design and fabrication of miniaturized MIbased FracBot nodes for evaluation in a physical WUSN testbed.
Deployable structures are lightweight and compact, and can be transported in a folded package to their destination by underwater robotics and then morph into their final form. These inflatable structures can morph into final shapes that are hundreds of times larger than their original volume, and are capable of creating sensor systems which can provide long term observation capabilities and feature a large detection range.
In this paper, underwater sensor systems based on inflatable structures are proposed and designed. An underwater tubular structure is designed and initial prototypes are built to study the mechanics of inflatable tubes in water. Numerical approaches to model the inflation process and bending under loading are developed in order to predict the inflatable tubular behavior during the structure morphing process under different loading conditions. The methods used in this research provide a solution for underwater inflatable structure design and analysis.
Oceans account for 71% of the area of Earth's surface (National Academies, 2009), and there is a growing need for the installation of subsea structures for ocean exploration. A new research concept known as Underwater Sensor Network (USN) has been widely accepted and studied (Chitre, 2008). The USN employs sensors and underwater platforms, such as Remote Operated Vehicles (ROVs), Autonomous Underwater Vehicles (AUVs), and ocean gliders for underwater data collection. To be more specific, USN can be used for applications such as stationary observation, target detection, and tracking. Compared with conventional monitoring systems, USN can employ different types of sensors to provide a high resolution and wide area surveillance. Practical applications for USN include environment monitoring, underwater prospecting, and disaster preparedness. Offshore pollution is a pressing issue, which needs close observation.
Morphing ocean structures can be deployed from existing ocean research platforms and are equipped with underwater acoustic or imaging sensors. These underwater sensors can form a sensor network to provide a wide area of data collection with a higher resolution compared with the sensors carried by AUVs.
The demand on the versatility of microseismic monitoring networks is increasing rapidly. In early projects, being able to locate any triggers was considered a success. These early successes lead to a better understanding of how to extract value from microseismic results. Operators, regulators, and service providers must work closely together to find the optimum network design to meet various requirements. In this article we discuss three aspects that are of concern in the design phase of modern microseismic monitoring networks: 1) Detection Limits; 2) Location accuracy; 3) Ground-motion hazard.
We determine the location error using both conventional "phase-picking" inversion scheme, and a novel method, based on Point Spread functions, to evaluate the accuracy "migration-style" location schemes.
Using the well-documented example region around the San Andreas Fault Observatory at Depth (SAFOD) located north of Parkfield, California, we present several approaches to these sometimes competing requirements. Modelling these parameters prior to installation of a sensor network, helps to identify the potential challenges to meet the survey objectives and thus appropriate changes to the design can be implemented.
We use the detailed velocity model of Thurber et al. (2004) which covers an area of 26.2x21.2km around the SAFOD drill site with a resolution of 200m for both P-and S-wave velocity (Figure 1). We are modelling the performance of several hypothetical sensor networks within the area. To highlight the importance of using the correct velocity model, we then compare these performance predictions with those for a simplistic 1D velocity model, commonly the only available source of velocity for hydraulic fracturing projects.
Presentation Date: Tuesday, October 18, 2016
Start Time: 4:10:00 PM
Location: Lobby D/C
Presentation Type: POSTER
Bergman, Jeffrey (Acellent Technologies Inc.) | Chung, Howard (Acellent Technologies Inc.) | Janapati, Vishnuvardhan (Acellent Technologies Inc.) | Li, Irene (Acellent Technologies Inc.) | Kumar, Amrita (Acellent Technologies Inc.) | Kumar-Yadav, Susheel (Acellent Technologies Inc.) | Chapman, Daniel (Chevron Energy Technology Company) | Nissan, Andrew (Chevron Energy Technology Company) | Sarrafi-Nour!, Reza (Chevron Energy Technology Company)
In order to ensure the continued operation of fixed equipment assets such as vessels, pipeline and piping systems, operators must invest in regular inspection of their systems by a variety of methods, including visual inspection, inline inspection (ILI), and traditional non-destructive evaluation (NDE) based techniques. This results in intermittent inspection of the piping and increased operating costs. Alternately, by utilizing Structural Health Monitoring (SHM) systems operators can monitor pipelines and piping on a continuous, rather than intermittent, basis and drive toward more cost effective Condition-Based Maintenance (CBM) of their systems. To support this need, a system is being introduced, which allows for the detection, localization and quantification of corrosion and erosion damage in metal piping. This system enables monitoring over an extended region of the pipe surface providing information on defect size and location in addition to the remaining wall thickness.
The system consists of a network of miniature ultrasonic sensors embedded in a thin dielectric film that can be integrated with the pipe. Diagnostic hardware housing data analysis software is used to acquire data from the sensor network and to determine onset and progress of corrosion damage. This paper discusses validation testing work performed using the system on piping.
Energy refining and distribution by the oil and gas industry is a key component of modern infrastructure. Corrosion and erosion are significant concerns for operators especially as economics drives the expectations for long life of their fixed equipment assets. These assets such as piping, pipelines, pressure vessels and tanks are vulnerable to both internal and external corrosion, which can result in deterioration of the pressure boundary. As a result, there is a need for regular inspection of steel pipeline for corrosion/erosion damage to monitor for wall loss on both the external and internal surfaces [1-2].
Real-time In-situ Seismic Imaging (RISI) is a breakthrough technology for monitoring and mapping the subsurface geophysical structures and dynamics in realtime. Instead of collecting data to a central place for post-processing, the distributed seismic data processing and computing are performed in-situ, delivering an evolving 3D image in real-time for visualization. As a paradigm-shifting approach, the RISI system is plug-nplay, scalable, self-adaptive and fault-tolerant. We have created multiple in-situ distributed seismic imaging algorithms including tomography and migration, and validated them using synthetic and field seismic data sets. This paper presents a case study of travel-time seismic tomography. The RISI prototype system has been implemented, and can be extended as a general field instrumentation platform, to incorporate new geophysical data processing and computing algorithms.
The seismic monitoring process today involves massive data collection, often manual retrieval, from hundreds and thousands of seismic sensors to a central place for post computing. The whole process is expensive and often takes months to complete. There is great demand for real-time as it would reduce the costs and risks of exploration and production activities and mitigate the environmental concerns.
The sensor network technology has emerged as an effective method for monitoring remote environment in real-time (Song et al., 2009). A sensor network is a selfforming and self-healing mesh network, where neighbors can communicate with each other directly, while far-apart stations can communicate through the relays. Real-time data collection in small-or-sparse wireless networks or low-data-rate applications has been demonstrated (Song et al., 2009). In seismic imaging applications, where the network is large-and-dense and data fidelity is high, it is virtually impossible to collect all the raw seismic data in real-time due to the severe bandwidth and energy constraints. Furthermore, data collection is prone to the bottleneck problem and not fault-tolerant.
Fortunately, the computation and communication capabilities of sensor node can be utilized for in-situ data processing and computing to generate real-time seismic images. Seismic imaging algorithms commonly in use today cannot be directly implemented in sensor networks, because they are centralized and need global information at the beginning. Time varying, real-time seismic imaging requires a distributed or decentralized approach, that can process seismic data and computing seismic images in-situ in real-time, under the constraint of network resources (bandwidth, energy, computing power, memory, etc).