Error-correcting coding usage for data transmission through power lines

A concept of data transmission within downhole telemetry systems in oilfield industry through power lines is presented. Based on this concept, MATLAB/Simulink models simulating communication lines in downhole telemetry systems are built, which can be used as prototypes for development of real systems. The most appropriate signal modulation methods for data transmission in downhole telemetry systems are suggested and discussed. The influence of high-voltage interference on signal transmission through power line from downhole unit to ground based unit is simulated. Usage of error-correcting coding methods for data transmission such as Hamming code, Reed-Solomon code, BCH code is suggested, and its efficiency is demonstrated. © 2018 Institute of Advanced Engineering and Science. All rights reserved.Ministry of Education and Science of the Russian Federation, Minobrnauka: 14.578.21.0134Work on PNIER RFMEFI57815X0134 was performed with the financial support of the Ministry of Education and Science of the Russian Federation in the framework of the agreement №14.578.21.0134 on 27 October 2015

Integracija "big data" analize i inženjerskog načina razmišljanja s ciljem upravljanja i kontroliranja inteligentnog opremanja i umjetnog načina podizanja nafte i plina za odabranu bušotinu s područja Hrvatske : diplomski rad

In order to reach more complex reservoir and increase ultimate recovery, engineers are searching for new technologies. One of these is intelligent completion which provides system monitoring, fluid production or injection control, and optimization. Operator can make decisions about managing completion based on real-time data coming from the downhole sensors. In addition, machine learning is becoming more popular in the oil industry. It finds application in automatization of processes and reducing time and error in decision making process. The aim of the thesis is to couple intelligent completion with machine learning (neural network) on the real example-gas well. The goal is to see if neural network can predict optimal interval control valve sizes for specific scenarios.Inženjeri kontinuirano istražuju nove tehnologije kako bi razradili kompleksnija ležišta i povećali njihov ukupni iscrpak ležišta. Jedan od načina je i inteligentno opremanje koje pruža mogućnost daljinskog nadgledanja i kontrole cjelokupnog procesa pridobivanja ugljikovodika, te optimizaciju cijelog procesa. U takvom sustavu operator donosi odluke na temelju podataka koji dolaze u stvarnom vremenu sa senzora postavljenih u bušotinu. Nadalje, strojno učenje (engl. machine learning) postaje sve popularnije i u naftnoj industriji. Primjenjuje se u automatizaciji procesa kako bi se smanjilo vrijeme i greške prilikom donošenja odluka. Cilj ovoga rada je spojiti inteligentno opremanje sa neuronskom mrežom na stvarnom primjeru plinske bušotine. Uz to, cilj je vidjeti može li neuronska mreža predvidjeti optimalne veličine intervalnog kontrolnog ventila za različite slučajeve

AI-driven Maintenance Support for Downhole Tools and Electronics Operated in Dynamic Drilling Environments

Downhole tools are complex electro-mechanical systems that perform critical functions in drilling operations. The electronics within these systems provide vital support, such as control, navigation and front-end data analysis from sensors. Due to the extremely challenging operating conditions, namely high pressure, temperature and vibrational forces, electronics can be subjected to complex failure modes and incur operational downtime. A novel Artificial Intelligence (AI)-driven Condition Based Maintenance (CBM) support system is presented, combining Bottom Hole Assembly (BHA) data with Big Data Analytics (BDA). The key objective of this system is to reduce maintenance costs along with an overall improvement of fleet reliability. As evidenced within the literature review, the application of AI methods to downhole tool maintenance is underrepresented in terms of oil and gas application. We review the BHA electronics failure modes and propose a methodology for BHA-Printed Component Board Assemblies (PCBA) CBM. We compare the results of a Random Forest Classifier (RFC) and a XGBoost Classifier trained on BHA electronics memory data cumulated during 208 missions over a 6 months period, achieving an accuracy of 90 % for predicting PCBA failure. These results are extended into a commercial analysis examining various scenarios of infield failure costs and fleet reliability levels. The findings of this paper demonstrate the value of the BHA-PCBA CBM framework by providing accurate prognosis of operational equipment health leading to reduced costs, minimised Non-Productive Time (NPT) and increased operational reliability

Evaluation of ceramic coaxial cable sensors for long-term in-situ monitoring of geologic CO₂ injection and storage

Monitoring is an essential component of CO₂ injection and storage projects in order to manage the injection process, identify leakage risks, provide early failure warnings, determine the movement of CO₂ plume and provide input into reservoir models. In-situ monitoring provides critical and direct data points that can be used to fulfill the above objectives. However, downhole sensors that can withstand the harsh conditions and run over decades of the project life cycle remain unavailable. A new idea of ceramic coaxial cable temperature, pressure and strain sensor has recently been put forward and the sensors are under development. A high pressure high temperature (HPHT) testing system was developed in order to characterize the novel ceramic coaxial cable sensors under combined temperature, pressure and strain conditions with water, oil, brine, CO₂ and CO₂ brine mixture. Tests were conducted on a semi-rigid coaxial cable temperature sensor under combined temperature and pressure conditions with water. Besides, a preliminary test was conducted on the ceramic coaxial cable pressure sensor model to help with the design of the sensor. The semi-rigid coaxial cable temperature sensor showed an excellent ability of recording the actual temperature of hydraulic water with a constant resolution of ± 1 ºC. The preliminary test on ceramic coaxial cable pressure sensor model decided stainless steel as the proper material for sensor jacket. --Abstract, page iii

Electrical Signature Analysis of Synchronous Motors Under Some Mechanical Anomalies

Electrical Signature Analysis (ESA) has been introduced for some time to investigate the electrical anomalies of electric machines, especially for induction motors. More recently hints of using such an approach to analyze mechanical anomalies have appeared in the literature. Among them, some articles cover synchronous motors usually being employed to improve the power factor, drive green vehicles and reciprocating compressors or pumps with higher efficiency. Similarly with induction motors, the common mechanical anomalies of synchronous motor being analyzed using the ESA are air-gap eccentricity and single point bearing defects. However torsional effects, which are usually induced by torsional vibration of rotors and by generalized roughness bearing defects, have seldom been investigated using the ESA. This work presents an analytical method for ESA of rotor torsional vibration and an experimentally demonstrated approach for ESA of generalized roughness bearing defects. The torsional vibration of a shaft assembly usually induces rotor speed fluctuations resulting from the excitations in the electromagnetic (EM) or load torque. Actually, there is strong coupling within the system which is dynamically dependent on the interactions between the electromagnetic air-gap torque of the synchronous machine and the rotordynamics of the rotor shaft assembly. Typically this problem is solved as a one-way coupling by the unidirectional load transfer method, which is based on predetermined or assumed EM or load profile. It ignores the two-way interactions, especially during a start-up transient. In this work, a coupled equivalent circuit method is applied to reflect this coupling, and the simulation results show the significance of the proposed method by the practical case study of Electric Submersible Pump (ESP) system. The generalized roughness bearing anomaly is linked to load torque ripples which can cause speed oscillations, while being related to current signature by phase modulation. Considering that the induced characteristic signature is usually subtle broadband changes in the current spectrum, this signature is easily affected by input power quality variations, machine manufacturing imperfections and the interaction of both. A signal segmentation technique is introduced to isolate the influence of these disturbances and improve the effectiveness of applying the ESA for this kind of bearing defects. Furthermore, an improved experimental procedure is employed to closely resemble naturally occurring degradation of bearing, while isolating the influence of shaft currents on the signature of bearing defects during the experiments. The results show that the proposed method is effective in analyzing the generalized roughness bearing anomaly in synchronous motors

AI-Driven Maintenance Support for Downhole Tools and Electronics Operated in Dynamic Drilling Environments

Downhole tools are complex electro-mechanical systems that perform critical functions in drilling operations. The electronics within these systems provide vital support, such as control, navigation and front-end data analysis from sensors. Due to the extremely challenging operating conditions, namely high pressure, temperature and vibrational forces, electronics can be subjected to complex failure modes and incur operational downtime. A novel Artificial Intelligence (AI)-driven Condition Based Maintenance (CBM) support system is presented, combining Bottom Hole Assembly (BHA) data with Big Data Analytics (BDA). The key objective of this system is to reduce maintenance costs along with an overall improvement of fleet reliability. As evidenced within the literature review, the application of AI methods to downhole tool maintenance is underrepresented in terms of oil and gas application. We review the BHA electronics failure modes and propose a methodology for BHA-Printed Component Board Assemblies (PCBA) CBM. We compare the results of a Random Forest Classifier (RFC) and a XGBoost Classifier trained on BHA electronics memory data cumulated during 208 missions over a 6 months period, achieving an accuracy of 90 % for predicting PCBA failure. These results are extended into a commercial analysis examining various scenarios of infield failure costs and fleet reliability levels. The findings of this paper demonstrate the value of the BHA-PCBA CBM framework by providing accurate prognosis of operational equipment health leading to reduced costs, minimised Non-Productive Time (NPT) and increased operational reliability

Applications of aerospace technology to petroleum extraction and reservoir engineering

Through contacts with the petroleum industry, the petroleum service industry, universities and government agencies, important petroleum extraction problems were identified. For each problem, areas of aerospace technology that might aid in its solution were also identified, where possible. Some of the problems were selected for further consideration. Work on these problems led to the formulation of specific concepts as candidate for development. Each concept is addressed to the solution of specific extraction problems and makes use of specific areas of aerospace technology

An assessment of subsea production systems

The decreasing gap between technology and the its applicability in the oil industry has led to a rapid development of deepwater resources. Beginning with larger fields where the chances of economic success are high, to marginal fields where project economics becomes a more critical parameter, the petroleum industry has come a long way. However, the ever growing water depths and harsher environments being encountered are presently posing challenges to subsea production. Being able to develop a field and then proceeding to ensure flow for the life of the field comprises many situations where the production equipment can fail and falter or through external factors, be deemed unavailable. Some of the areas where most of the current developments in subsea production are being seen are in subsea processing, flow assurance, long term well monitoring and intervention technologies areas that pose some of the biggest challenges to smooth operation in the deepwater environment. This research highlights the challenges to overcome in subsea production and well systems and details the advances in technology to mitigate those problems. The emphasis for this part of the research is on multiphase pumping, subsea processing, flow assurance, sustained casing pressure problems and well intervention. Furthermore, most operators realize a reduced ultimate recovery from subsea reservoirs owing to the higher backpressure imposed by longer flowlines and taller risers. This study investigates the reasons for this by developing a global energy balance and detailing measures to improve production rates and ultimate recoveries. The conclusions from this energy balance are validated by simulating a deepwater field under various subsea production scenarios

Integracija "big data" analize i inženjerskog načina razmišljanja s ciljem upravljanja i kontroliranja inteligentnog opremanja i umjetnog načina podizanja nafte i plina za odabranu bušotinu s područja Hrvatske : diplomski rad

In order to reach more complex reservoir and increase ultimate recovery, engineers are searching for new technologies. One of these is intelligent completion which provides system monitoring, fluid production or injection control, and optimization. Operator can make decisions about managing completion based on real-time data coming from the downhole sensors. In addition, machine learning is becoming more popular in the oil industry. It finds application in automatization of processes and reducing time and error in decision making process. The aim of the thesis is to couple intelligent completion with machine learning (neural network) on the real example-gas well. The goal is to see if neural network can predict optimal interval control valve sizes for specific scenarios.Inženjeri kontinuirano istražuju nove tehnologije kako bi razradili kompleksnija ležišta i povećali njihov ukupni iscrpak ležišta. Jedan od načina je i inteligentno opremanje koje pruža mogućnost daljinskog nadgledanja i kontrole cjelokupnog procesa pridobivanja ugljikovodika, te optimizaciju cijelog procesa. U takvom sustavu operator donosi odluke na temelju podataka koji dolaze u stvarnom vremenu sa senzora postavljenih u bušotinu. Nadalje, strojno učenje (engl. machine learning) postaje sve popularnije i u naftnoj industriji. Primjenjuje se u automatizaciji procesa kako bi se smanjilo vrijeme i greške prilikom donošenja odluka. Cilj ovoga rada je spojiti inteligentno opremanje sa neuronskom mrežom na stvarnom primjeru plinske bušotine. Uz to, cilj je vidjeti može li neuronska mreža predvidjeti optimalne veličine intervalnog kontrolnog ventila za različite slučajeve

Development of Anti-Icing Airfield Heated Pavement Systems Using Solar Energy

This dissertation analyzes developing and assessing the viability of an anti-icing airfield heated pavement system using solar energy. This study includes two components, a field experimentation component and a numerical analysis component. Field experimentation investigates two systems: (1) an electrical heated pavement system with a photovoltaic energy system as its power source, and (2) a hydronic heated pavement system with a solar water-heating system as its heating source. The systems operate under an automated thermostat heating sequence for operation optimization and energy conservation. Study results found the solar systems capable of supplying enough energy to maintain pavement surface temperature above freezing and melt snow. A finite element model (FEM) extends the near-surface electrical heated pavement system analysis to assess the energy required to heat a cold region airport’s airfield pavement. A benefit-cost analysis (BCA) expands the hydronic system analysis to assess the viability for implementing a solar-hydronic heated pavement system at an apron area