Thermal Profiles in Water Injection Wells: Reduction in the Systematic Error of Flow Measurements during the Transient Regime.

This article presents an analytical solution for calculating the flow rate in water injection wells based on the established thermal profile along the tubing. The intent is to minimize the intrinsic systematic error of classic quasi-static methodologies, which assume that all thermal transience on well completion has passed. When these techniques are applied during the initial hours of injection well operation, it can result in errors higher than 20%. To solve this limitation, the first law of thermodynamics was used to define a mathematical model and a thermal profile was established in the injection fluid, captured by using distributed temperature systems (DTSs) installed inside the tubing. The geothermal profile was also established naturally by a thermal source in the earth to determine the thermal gradient. A computational simulation of the injection well was developed to validate the mathematical solution. The simulation intended to generate the fluid's thermal profile, for which data were not available for the desired time period. As a result, at the cost of greater complexity, the systematic error dropped to values below 1% in the first two hours of well operation, as seen throughout this document. The code was developed in Phyton, version 1.7.0., from Anaconda Navigator.

Velocity Prediction of a Pipeline Inspection Gauge (PIG) with Machine Learning.

A device known as a pipeline inspection gauge (PIG) runs through oil and gas pipelines which performs various maintenance operations in the oil and gas industry. The PIG velocity, which plays a role in the efficiency of these operations, is usually determined indirectly from odometers installed in it. Although this is a relatively simple technique, the loss of contact between the odometer wheel and the pipeline results in measurement errors. To help reduce these errors, this investigation employed neural networks to estimate the speed of a prototype PIG, using the pressure difference that acts on the device inside the pipeline and its acceleration instead of using odometers. Static networks (e.g., multilayer perceptron) and recurrent networks (e.g., long short-term memory) were built, and in addition, a prototype PIG was developed with an embedded system based on Raspberry Pi 3 to collect speed, acceleration and pressure data for the model training. The implementation of the supervised neural networks used the Python library TensorFlow package. To train and evaluate the models, we used the PIG testing pipeline facilities available at the Petroleum Evaluation and Measurement Laboratory of the Federal University of Rio Grande do Norte (LAMP/UFRN). The results showed that the models were able to learn the relationship among the differential pressure, acceleration and speed of the PIG. The proposed approach can complement odometer-based systems, increasing the reliability of speed measurements.

System of Sensors and Actuators for the Production of Water Used in the Manufacture of Medicines.

This paper presents the development and implementation of a centralized industrial network for an automatic purified water production system used in the pharmaceutical industry. This implementation is part of a project to adapt an industrial plant to cope with advances in industrial technology to achieve the level of Industry 4.0. The adequacy of the instruments and the interconnection of the controllers made it possible to monitor the process steps by transforming a manual plant, with discontinuous production into an automated plant, improving the efficiency and quality of the produced water. The development of a supervisory system provides the operator with a panoramic view of the process, informing in real-time the behavior of the variables in the process steps, as well as storing data, event history and alarms. This system also prevented the collection of erroneous or manipulated data, making the process more transparent and reliable. Accordingly, we have been able to tailor this water treatment plant to operate within the minimum requirements required by the regulator.

Pipeline Inspection Gauge's Velocity Simulation Based on Pressure Differential Using Artificial Neural Networks.

Industrial pipelines must be inspected to detect typical failures, such as obstructions and deformations, during their lifetime. In the petroleum industry, the most used non-destructive technique to inspect buried pipelines is pigging. This technique consists of launching a Pipeline Inspection Gauge (PIG) inside the pipeline, which is driven by the pressure differential produced by fluid flow. The purpose of this work is to study the application of artificial neural networks to calculate the PIG's velocity based on the pressure differential. We launch a prototype PIG inside a testing pipeline, where this PIG gathers velocity data from an odometer-based system, while a supervisory system gathers pressure data from the testing pipeline. Then we train a Multilayer Perceptron (MLP) and a Nonlinear Autoregressive Network with eXogenous Inputs (NARX) network with the gathered data to predict velocity. The results suggest it is possible to use a neural network to model the PIG's velocity from pressure differential measurements. Our method is a new approach to the typical speed measurements based only on odometer, since the odometer is prone to fail and present poor results under some circumstances. Moreover, it can be used to provide redundancy, improving reliability of data obtained during the test.

Wireless Monitoring of Induction Machine Rotor Physical Variables.

With the widespread use of electric machines, there is a growing need to extract information from the machines to improve their control systems and maintenance management. The present work shows the development of an embedded system to perform the monitoring of the rotor physical variables of a squirrel cage induction motor. The system is comprised of: a circuit to acquire desirable rotor variable(s) and value(s) that send it to the computer; a rectifier and power storage circuit that converts an alternating current in a continuous current but also stores energy for a certain amount of time to wait for the motor's shutdown; and a magnetic generator that harvests energy from the rotating field to power the circuits mentioned above. The embedded system is set on the rotor of a 5 HP squirrel cage induction motor, making it difficult to power the system because it is rotating. This problem can be solved with the construction of a magnetic generator device to avoid the need of using batteries or collector rings and will send data to the computer using a wireless NRF24L01 module. For the proposed system, initial validation tests were made using a temperature sensor (DS18b20), as this variable is known as the most important when identifying the need for maintenance and control systems. Few tests have shown promising results that, with further improvements, can prove the feasibility of using sensors in the rotor.