Smart Field Research Articles

Crude-Oil Scheduling Network in Smart Field Under Cyber-Physical System

The smart field has drawn much attention for the field business functions to be tied into a smoothly operating system. However, its operation decisions still cannot meet the ever-increasing multiple data source requirements. This paper proposes a novel smart field cyber-physical system (SF-CPS), which comprises some sensors transmitting real-time sensed information between the marine terminals and the petroleum refinery, and an asset optimization-based decision maker using the pumping schedule to determine the best configuration. From a unique functional unit perspective, the resource allocation of volumes and qualities is implemented with the Dinkelbach method to address this enterprise-wide optimization. Taking advantage of the unloading flows at a low cost, we settle the state variables to the steady process of refinery planning, and then, the next multi-operations sequence follows the tailored outer approximation approach for decomposition to achieve high-cost efficiency. Moreover, the two-phase stochastic scheduling decisions coupled with inventory levels hosted on the SF-CPS platform can cope well with uncertainty in the process between the oil supply and maritime conditions. The experimental results validate the proposed techniques for typical oil and gas resources.

Intelligent Technologies in the Oil and Gas Industry

The article is devoted to the discussion of improving the quality of oil and gas production, through the introduction of new technologies for the extraction and processing of natural resources. As well as to the procedure, which aims to ensure the increase in the volume of mineral resource (liquid hydrocarbons) and to optimization of production costs in modern enterprises of oil and gas industry. The development of “smart” technologies in the oil and gas industry is mainly associated with a reduction in proven oil and gas resources in the Russian Federation. However, there are oil and gas reserves in places with an unfavorable climate, which significantly increases the cost of developing such deposits. For solving this problem, the “smart” technology “Smart Field” development is proposed.

A secure routing protocol with regional partitioned clustering and Beta trust management in smart home

With the emergence of the Internet of Things (IoT) in recent years, the security has been significantly called more and more people’s attention on wireless communication between the devices and the human-beings, as well as the devices to devices. Smart home (SH), as a small-scale example of the smart application-based field, has benefited from the concept of IoT since it uses an indoor data-centric sensor network. In SH, routing schemes are widely utilized for data aggregation purposes. However, there are three main issues, which can considerably affect the current execution of routing protocol in SH: (1) lack of technical methods for precisely regional division of the network, (2) the difficulty of differentiating data among various functional regions, and (3) the vulnerability of network with advanced internal routing attacks. To address the aforementioned issues, in this paper, a two-layer cluster-based network model for indoor structured SH and a novel Beta-based trust management (BTM) scheme are proposed to defend various types of internal attacks by integrating the variation of trust value, threshold, and evaluation. The proposed structure forms a secure hierarchical routing protocol called SH-PCNBTM to effectively support the data transmission service in SH networks. The performance of SH-PCNBTM is thoroughly evaluated by using a set of comprehensive simulations. We will show that the proposed routing protocol not only ensures the even distribution of cluster-heads in each sub-region, but it also identifies and isolates the malicious sensor nodes accurately and rapidly compared with other trust-based hierarchical routing protocols.

Privacy-preserving aggregation of personal health data streams.

Recently, as the paradigm of medical services has shifted from treatment to prevention, there is a growing interest in smart healthcare that can provide users with healthcare services anywhere, at any time, using information and communications technologies. With the development of the smart healthcare industry, there is a growing need for collecting large-scale personal health data to exploit the knowledge obtained through analyzing them for improving the smart healthcare services. Although such a considerable amount of health data can be a valuable asset to the smart healthcare fields, they may cause serious privacy problems if sensitive information of an individual user is leaked to outside users. Therefore, most individuals are reluctant to provide their health data to smart healthcare service providers for data analysis and utilization purpose, which is the biggest challenge in smart healthcare fields. Thus, in this paper, we develop a novel mechanism for privacy-preserving collection of personal health data streams that is characterized as temporal data collected at fixed intervals by leveraging local differential privacy (LDP). In particular, with the proposed approach, a data contributor uses a given privacy budget of LDP to report a small amount of salient data, which are extracted from an entire health data stream, to a data collector. Then, a data collector can effectively reconstruct a health data stream based on the noisy salient data received from a data contributor. Experimental results demonstrate that the proposed approach provides significant accuracy gains over straightforward solutions to this problem.

Periodic electro-optical characteristics of ion-doped Smectic A phase liquid crystals driven by a low-frequency electric field

ABSTRACTFunctional liquid crystal (LC)-based electro-optical materials are promising candidates in a wide range of smart fields due to their excellent external stimuli-responsive characteristics. Herein, ion-doped Smectic A (SmA)-phase LCs gradually change from a transparent to a scattering state and then return to the transparent state via a circularly driven low-frequency electric field. All optical states can be maintained after removing the electric field. This phenomenon is due to charge transport and convection effects that appear and disappear periodically in SmA LCs doped with suitable ionic liquids. Diverse periodic characteristics were observed upon applying electric fields with different waveforms (e.g. square, sawtooth, and sine). Furthermore, the optical contrast of the periodic feature was abated by increasing the frequency of the electric field. These ion-doped SmA LCs can be widely used in multi-stable optical devices, optical switches, and timers, etc.

The intrapreneurial state: Singapore’s emergence in the smart and sustainable urban solutions field

ABSTRACTThe East Asian developmental state model and the Anglo-American entrepreneurial state model profile varied ways in which the state continues to intervene in economic development. These models are developed by different disciplines and against diverse contexts to capture extrasocietal state responses to neoliberalism and globalization but leave the intrasocietal preconditions for state evolution little explored. We elaborate the concept of state intrapreneurialism as one way of understanding the interrelationship between economic and state transformation – one ingredient of the intrasocietal preconditions underpinning the responses to extrasocietal changes emphasized in the post-developmental state literature. Drawing on the case of Singapore's emergence in the field of smart/sustainable urban solutions, the subsidiary contributions of this paper are to suggest intrapreneurship as a specific and enduring advantage within the developmental state model, especially when set against its limitations signalled in the post-developmental state literature.

Capacitive Enhancement Mechanisms and Design Principles of High‐Performance Graphene Oxide‐Based All‐Solid‐State Supercapacitors

AbstractGraphene oxide (GO)‐based all‐solid‐state supercapacitors (GO‐A3Ss) are superior over liquid electrolyte‐based supercapacitors and capable of being integrated on a single chip in various geometry shapes for the use of future smart wearable electronics field as a fast energy storage device, but their capacitance need to be improved. Here, a new approach has been developed for enhancing the capacitive capability of the supercapacitors through molecular dynamics simulations with the first‐principle input. A theoretical model of charge storage is developed to understand the unique capacitive enhancement mechanism and to predict the capacitance of the GO‐A3Ss, which agrees well with the experimental observations. A novel supercapacitor with GO and reduced graphene oxide (rGO) alternatively layered structures is designed based on the model, and its energy density is the highest among conventional supercapacitors using liquid electrolytes and all‐solid‐state supercapacitors using aerogels or hydrogels as the solid‐state electrolyte. Based on the predictions, two new types of high‐performance GO/rGO multilayered capacitors are proposed to meet different practical applications. The results of this work provide an approach for the design of high‐performance all‐solid‐state supercapacitors based on GO and rGO materials.

Field-dependent nonlinear piezoelectricity: a focused review

ABSTRACTThis contribution presents a multidisciplinary review of the so-called field-dependent nonlinear piezoelectricity. It starts with an introduction that poses the literature analysis framework, through defining this operational (that is often met in practice) piezoelectric field-dependent nonlinearity. Indeed, the latter is a less known phenomenon although it is inherent to stress-free actuation responses of corresponding smart materials, actuators and structures. Then, related experimental observations from piezoelectric materials, actuator devices and smart structures tests are multidisciplinary surveyed for understanding the underlying mechanisms of the encountered field-dependent nonlinearity. Next, empirical material and numerical structural modelling and simulation approaches are critically reviewed from, respectively, the constitutive and finite element analysis points of view. Summary conclusions and few future directions for research are finally provided as a closure. It is worth mentioning that, although it is concise (retains only experiments and experimentally-correlated models and simulations), this critical review covers the last three decades period which is almost the whole age of the piezoelectric materials, actuators and smart structures research field.

Graphene quantum dot–poly(vinylidene fluoride) composite for the preparation of asymmetric bilayer bending transducer

A newly developed asymmetric bilayer bending transducer composed of a graphene quantum dot (GQD)–poly(vinylidene fluoride) (PVDF) composite and a simple adhesive tape has been investigated. This bending transducer displays high actuation displacement (10.2 mm) and large electromechanical strain (0.3%) under relatively low electric field strength (20.8 MV m−1). The key to the high electromechanical performance lies in the unique GQD–PVDF interfacial structure and the high dielectric constant of GQD–PVDF (2350 at 1 Hz, 1% GQD weight percentage) caused by the interfacial polarization effect. The good electromechanical performance of our device shows bright prospect of GQD in smart materials field.

Smart Network Field Theory: The Technophysics of Blockchain and Deep Learning

The aim of this paper is to propose a theoretical construct, smart network field theory, for the characterization, monitoring, and control of smart network systems. Smart network systems are intelligent autonomously-operating networks, a new form of global computational infrastructure that includes blockchains, deep learning, and autonomous-strike UAVs. These kinds of large-scale networks are a contemporary reality with thousands, millions, and billions of constituent elements, and entail a foundational and theoretically-robust model for their design and operation. Hence this work proposes smart network field theory, drawing from statistical physics, effective field theories, and model systems, for criticality detection and fleet-many item orchestration in smart network systems. Smart network field theory falls within the broader concern of technophysics (the application of physics to the study of technology), in which a key objective is deriving standardized methods for assessing system criticality and phase transition, and defining interim system structure between the levels of microscopic noise and macroscopic labels. The farther implications of this work include the possibility of recasting the P/NP computational complexity schema as one no longer based on traditional time (concurrency) and space constraints, due to the availability of smart network computational resources.

Actual Evapotranspiration of Unirrigated Grass in a Smart Field Lysimeter

Core Ideas Small smart lysimeters make high‐quality research of low and dense crops easy. FAO 56 can estimate well the reference crop ET on non‐ or mildly stressed sites. Weather station grass (not water stressed) has lower ET than the reference crop. Lysimeters are basic instruments for evapotranspiration measurement. This study characterized the actual evapotranspiration of unirrigated and unfertilized grass in a warm region of the Czech Republic on a Chernozem loamy soil. An SFL‐300 weighing lysimeter (diameter 0.3 m, depth 0.3 m) was used for this purpose. The suction at its bottom was maintained at the same level as in the native soil nearby. We selected 585 rainless days with regular records for the analysis of daily differences. On most days, the lysimeter‐measured actual evapotranspiration, ETa, was smaller than the Penman–Monteith FAO 56 reference crop evapotranspiration, ET0. The FAO 56 procedure was found to be a reasonable estimator of the unstressed evapotranspiration in a moderately stressed environment. The ETa/ET0 ratio and the canopy surface resistance, rs, depend on the soil water content and suction measured at 5 cm. These graphs break down into horizontal unstressed parts and declining (for ETa/ET0) or inclining (for rs) water‐stressed parts. The ratio ETa/ET0 is about 85% and rs is about 250 s m−1 when the grass is not under water stress. The annual curve of the unstressed crop coefficient has a sine shape. An energy balance criterion suggests that advection of heat is important in winter but not so much in summer. The study provides parameters of evapotranspiration for a canopy that can be found on many standard weather stations and demonstrates that high‐quality research into evapotranspiration of low, dense, and shallow‐rooting crops is possible with small lysimeters of this type.

Integration of oilfield planning problems: infrastructure design, development planning and production scheduling

The oil and gas industry faces considerable technological challenges nowadays, mainly associated with nonconventional reserves and offshore environments. To ensure the economic efficiency of operations under such complicated conditions, industrial solutions called “smart fields” were introduced. A vital part of these solutions is strategic planning which aims to coordinate field development and hydrocarbons production operations to optimize the entire field lifecycle, given the chosen planning criterion (e.g., net present value of the project). Still, in many cases engineering design is conducted with a conservative approach of solving one problem at a time. In this study, a multi-period mixed integer nonlinear problem (MINLP) covering the relevant issues of both development and production phases, is introduced. The contribution of the proposed model to the area of oilfield development planning is related to the model's simultaneously incorporating technological details regarding the infrastructure layout, pressures and flows in the gathering system, and artificial lift performance, all of which ultimately aims to provide realistic production scheduling results and cost evaluation. The composed model is used to study the benefits of joint development and production planning in comparison to planning these two phases of the project separately. This research is relevant to the engineering design departments and contractors for the preliminary design stage of the engineering project when the infrastructures layout and capacities are chosen, and rough estimates of costs and revenues are calculated.

Oilfield development and operations planning under geophysical uncertainty

Abstract The oil and gas industry nowadays is challenged by dealing with nonconventional reserves and offshore environments. Decision-making associated with projects in the petroleum sector has to handle various technological issues, risks, and uncertainty. The Smart Fields approach was introduced to cope with complicated production conditions and make the production of hydrocarbons economically efficient. A significant part of this approach is proactive planning which implies taking into account the uncertainty, or lack of knowledge of the recoverable reserves, future hydrocarbon prices and various operational issues inherent in the projects. In this study, a multi-stage stochastic programming approach is employed to cover the relevant engineering issues of oilfield development and petroleum production while addressing the geophysical uncertainty related to the developed deposit. The proposed model covers such aspects as well drilling, gathering pipeline infrastructure planning, capacity selection for the infrastructure and the processing units, as well as planning the production operations with consideration of artificial lift efficiency. The model aims to optimise the entire field lifecycle, given the chosen planning criterion, that is an economic criterion of the project’s net present value. The contribution of the developed model to the area of planning in the petroleum industry is the detailed consideration of the technology: the flows and pressures in the planned infrastructure, reservoir behaviour, and the artificial lift performance. The goal of including these technological details is to apprehend the economic tradeoff between investments, operating costs and the prospective revenues, given the lack of knowledge of the geophysical properties of the developed deposit. The stochastic modelling implemented in this study is relevant to the development projects in nonconventional environments, where several deposits of various sizes are present; however, not each deposit's properties get to be studied in detail.

A Machine Learning Decision-Support System Improves the Internet of Things’ Smart Meter Operations

An Internet of Things' (IoT) connected society and system represents a tremendous paradigm shift. We present a framework for a decision-support system (DSS) that operates within the IoT ecosystem. The DSS leverages advanced analytics of electric smart meter (ESM) network communication-quality data to improve cost predictions for smart meter field operations and provide actionable decision recommendations regarding whether to send a technician to a customer location to resolve an ESM issue. The model is empirically evaluated using data sets from a commercial network. We demonstrate the efficiency of our approach with a complete Bayesian network prediction model and compare with three machine learning prediction model classifiers: 1) Naïve Bayes; 2) random forest; and 3) decision tree. Results demonstrate that our approach generates statistically noteworthy estimations and that the DSS will improve the cost efficiency of ESM network operations and maintenance.

Smart Field System using LabVIEW and IoT

Smart Field System using LabVIEW and IoT

Smart Behavioural Filter for Industrial Internet of Things

We are currently experiencing the fourth industrial revolution. This is what the German government initiative, first, has identified with ‘Industry 4.0’. The manufacturing future will be marked and will go through the new automation technologies that are being introduced with Industrial Internet of Things (I2oT). Industrial Control Systems (ICSs) are exploiting I2oT for reducing costs and improving efficiency. However, ICSs are already jeopardized by an increasingly large set of threat vectors. Those threats are used by malicious actors to misuse physical Critical Infrastructures that usually are vital services for well-being. I2oT implementation increases the threat surface, generating new possible vulnerabilities. Information Technology (IT) classical approaches to cyber attacks cannot be applied to ICS due to their extreme differences from main priorities to resource constrains. Therefore, innovative approaches and equipment must be developed to suit with ICS world. In this paper, a Smart Behavioural Filter (SBF) for the PLCs (Programmable Logic Controllers) is proposed aiming to secure the PLC itself against logic attacks, that are stealth for other more classical security approaches. An example of the considered logic attacks is many open and close commands towards a valve in a short time. Those logic attacks are usually a sequence of well-formed packets in which the content represents an anomalous and unpredicted behaviour. This smart field equipment can react in short time to cyber attacks isolating the PLC, communicate with other equipment like itself and increasing in general the resilience of the physical system. It can also generate alarms for the local Intrusion Detection System (IDS). The proposed equipment has been developed and validated in a real test-bed within the FP7 CockpitCI project and H2020 ATENA project.

Integrating building and urban semantics to empower smart water solutions

Current urban water research involves intelligent sensing, systems integration, proactive users and data-driven management through advanced analytics. The convergence of building information modeling with the smart water field provides an opportunity to transcend existing operational barriers. Such research would pave the way for demand-side management, active consumers, and demand-optimized networks, through interoperability and a system of systems approach. This paper presents a semantic knowledge management service and domain ontology which support a novel cloud-edge solution, by unifying domestic socio-technical water systems with clean and waste networks at an urban scale, to deliver value-added services for consumers and network operators. The web service integrates state of the art sensing, data analytics and middleware components. We propose an ontology for the domain which describes smart homes, smart metering, telemetry, and geographic information systems, alongside social concepts. This integrates previously isolated systems as well as supply and demand-side interventions, to improve system performance. A use case of demand-optimized management is introduced, and smart home application interoperability is demonstrated, before the performance of the semantic web service is presented and compared to alternatives. Our findings suggest that semantic web technologies and IoT can merge to bring together large data models with dynamic data streams, to support powerful applications in the operational phase of built environment systems.

Benefits and risks of smart home technologies

Smart homes are a priority area of strategic energy planning and national policy. The market adoption of smart home technologies (SHTs) relies on prospective users perceiving clear benefits with acceptable levels of risk. This paper characterises the perceived benefits and risks of SHTs from multiple perspectives.A representative national survey of UK homeowners (n=1025) finds prospective users have positive perceptions of the multiple functionality of SHTs including energy management. Ceding autonomy and independence in the home for increased technological control are the main perceived risks. An additional survey of actual SHT users (n=42) participating in a smart home field trial identifies the key role of early adopters in lowering perceived SHT risks for the mass market. Content analysis of SHT marketing material (n=62) finds the SHT industry are insufficiently emphasising measures to build consumer confidence on data security and privacy.Policymakers can play an important role in mitigating perceived risks, and supporting the energy-management potential of a smart-home future. Policy measures to support SHT market development include design and operating standards, guidelines on data and privacy, quality control, and in situ research programmes. Policy experiences with domestic energy efficiency technologies and with national smart meter roll-outs offer useful precedents.

Conceptualization of smart solutions in oil and gas industry

Abstract: Technological solutions called “smart wells” and “smart fields” have been applied in petroleum industry for nearly two decades. They aim to improve the knowledge of petroleum production processes, and thereby improve the efficiency of operations. Researchers and companies in pursuit of their goals tend to use one-sided implications and numerous synonyms when describing the substance of the term, which leads to an occasional confusion. In order to study the concept of smart field from a general perspective, a literature review has been conducted, and main characteristic attributes of such solutions have been revealed. Selected marketed products offered by engineering companies have been analyzed as examples of the technology implementation. A definition has been proposed along with its practical implications. Additionally, an attempt has been made to place these solutions in a broad scientific context of intelligence and sustainability in contemporary business processes.

Towards the Perceptual Quality Evaluation of Compressed Light Field Images

Evaluation of perceived quality of light field images, as well as testing new processing tools, or even assessing the effectiveness of objective quality metrics, relies on the availability of test dataset and corresponding quality ratings. This paper presents SMART light field image quality dataset. The dataset consists of source images (raw data without optical corrections), compressed images, and annotated subjective quality scores. Furthermore, analysis of perceptual effects of compression on SMART dataset is presented. Next, the impact of image content on the perceived quality is studied with the help of image quality attributes. Finally, the performances of 2-D image quality metrics when applied to light field images are analyzed.