Using the Context-Sensitive Policy Mechanism for Building Data Acquisition Systems in Large Scale Distributed Cyber-Physical Systems Built on Fog Computing Platforms

The article deals with the use of context-sensitive policies in the building of data acquisition systems in large scale distributed cyber-physical systems built on fog computing platforms. It is pointed out that the distinctive features of modern cyber-physical systems are their high complexity and constantly changing structure and behavior, which complicates the data acquisition procedure. To solve this problem, it is proposed to use an approach according to which the data acquisition procedure is divided into two phases: model construction and data acquisition, which allows parallel realization of these procedures. A distinctive feature of the developed approach is that the models are built in runtime automatically. As a top-level model, a multi-level relative finite state operational automaton is used. The automaton state is described using a multi-level structural-behavioral model, which is a superposition of four graphs: the workflow graph, the data flow graph, the request flow graph and the resource graph. To implement the data acquisition procedure using the model, the context-sensitive policy mechanism is used. The article discusses possible approaches to implementation of suggested mechanisms and describes an example of application.

Metrological characterisation of a textile temperature sensor in archaeological usage

<p>This paper presents the study of a new generation textile temperature sensor in two different heated ovens. The first chamber was used to evaluate temperature and the second was used to evaluate both temperature and humidity. Data acquisition systems based on LabVIEW and Agilent were developed using thermocouples and Pt100 sensors. The results show many metrological characteristics that prove that the sensor is a resistance temperature detector.</p>

Establishing Predictive Friction Factor Ranges in the Permian and Appalachia: Frac Plug Drill Outs

Completions operations, especially in modern day extended laterals, presents challenges related to tripping to total depth, applying weight down and pull up, and rotating. As dozens of stages in laterals exceeding 10,000 ft stepout have become frequent, numerous technologies have risen to assist with pushing the envelope for reliable completions operations in these long laterals. This paper examines a combination of three technologies that are more commonly being applied when drilling out frac plugs in long horizontals in the USA: hydraulic completion units, torque and drag software, and data acquisition systems. Coiled tubing units (CTU) have historically been used to drill out frac plugs in shorter horizontal shale wells for the last two decades, and where coil has mechanical limitations, Hydraulic Completion Units (HCU) have taken over drilling out frac plugs in the longer laterals of &gt;10,000 ft. As the limits of drilling out frac plugs have been tested for HCUs, accurate real time data has enabled the crews to make the most of their equipment to reliably complete wells with longer and longer lateral sections. Torque and drag software modeling is a tool commonly used to predict axial force and torsional values during completions that result in the available hook load and the rotary torque requirements. The largest unknown in the planning phase is the appropriate friction factor to use for the upcoming well, with accurate friction factor prediction therefore the key to accurate prejob analysis. As of 2019 remote telemetry data acquisition systems (DAS) have been used on the HCUs, which has allowed key performance indicators (KPIs) to be automatically calculated. The program provides live feed to the service company and operator so that real time changes can be made if necessary. In addition to tracking KPIs in real time to provide the field crew positive or negative feedback, friction factors can be matched to predictive torque plots to identify trends prior to problems arising. Post-job analysis is needed to produce accurate predictive friction factors for future offset wells. The two main components to a successful post-job analysis are a software model that correctly represents the prior wellbore operations and accurate field data to compare with that model. Unfortunately, the software models in use are commonly limited by necessary assumptions with input data, such as rotary speed and tripping speed, and field data collected for comparison is often rudimentary. Experienced field personnel using engineering best practices can make use of current tools in combination to overcome the limitations commonly inhibiting accurate performance planning and predictive modeling. The inclusion of the DAS present on the HCU has greatly enhanced the accuracy and amount of rig data gathered, which can then be used in conjunction with operational procedures and torque and drag software to accurately plan and execute completions operations in the wellbore. Using data acquisition software, a constant stream of data was collected in one-second intervals in over two dozen wells. This system has the ability to measure both rotary speed and rotary torque, which are critical parameters when drilling out frac plugs. By removing these assumptions in the post-job analysis over a number of wells, a range of friction factors have been established for the Appalachian Basin in the Utica and Marcellus plays. The authors will present field data from two wells as representative case studies, along with the range of predictive friction factors established from 13 wells for the particular completions operations evaluated in the Permian and Appalachia plays. It is the goal of the authors to disseminate technical information on the methodology and practice of modeling wells post-job, calibrating friction factors, and establishing predictive ranges for successful use in future projects.