Control design, simulation and validation of a turbo-machinery auxiliary plant

In the oil and gas industry, the testing of auxiliary lubrication plants represents an important preliminary activity before the whole turbo machinery train (including the auxiliary lubrication plant) can be put in operation. Therefore, the employment of both efficient and accurate plant models becomes mandatory to synthesize satisfactory control strategies both for testing and normal operation purposes. For this reason, this paper focuses on the development of innovative real-time models and control architectures to describe and regulate auxiliary lubrication plants. In particular, according to the Bond-Graph modelling strategy, a novel lumped parameter model of the lube oil unit has been developed to properly optimize the behaviour of this unit if it is controlled. The code has been compiled and uploaded on a commercial real-time platform, employed to control the pressure control valve of the physical plant, for which a new controller has been developed. The comparison between the data obtained from the simulated system and acquired from the physical plant shows good agreement and the good performance and reliability of the proposed model and control strategy. The modelling approach and the control strategy have been developed in collaboration with GE Nuovo Pignone S.p.a. while the experimental data were acquired in a plant located in Ptuj (Slovenia).

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Iot Enabled Pipeline Leakage Detection and Real Time Alert System in Oil and Gas Industry

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.e6380.018520 ◽

2020 ◽

Vol 8 (5) ◽

pp. 2582-2586

Keyword(s):

Real Time ◽

Oil And Gas ◽

Detection System ◽

Oil And Gas Industry ◽

Petroleum Products ◽

Time Data ◽

Gas Industry ◽

Processing Plants ◽

Automation And Control ◽

And Control

Automation and control systems are necessary throughout oil & gas industries, to production and processing plants, and distribution and retailing of petroleum products. Pipelines are the efficient mode of transportations of fuels for processing plants over long distances. At present Automation is achieved by using PLC’s that are communicated through SCADA. But it is complex and remote operation is not possible. With the introduction of IoT, the pipeline leak detection system is improved through real-time monitoring of the pipelines. Our Proposed system is designed to detect even small leakage that occurs within the pipeline. The implementation of IoT in oil and gas industries prevents accidents and to make quick decisions based on real-time data

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Development of Real-Time Time Gated Digital (TGD) OFDR Method and Its Performance Verification

Sensors ◽

10.3390/s21144865 ◽

2021 ◽

Vol 21 (14) ◽

pp. 4865

Author(s):

Kinzo Kishida ◽

Artur Guzik ◽

Ken’ichi Nishiguchi ◽

Che-Hsien Li ◽

Daiji Azuma ◽

...

Keyword(s):

Real Time ◽

Optical Fibers ◽

High Speed ◽

Oil And Gas ◽

Scattered Light ◽

Oil And Gas Industry ◽

High Signal ◽

Chirp Pulse ◽

Sound Waves ◽

Industry Applications

Distributed acoustic sensing (DAS) in optical fibers detect dynamic strains or sound waves by measuring the phase or amplitude changes of the scattered light. This contrasts with other distributed (and more conventional) methods, such as distributed temperature (DTS) or strain (DSS), which measure quasi-static physical quantities, such as intensity spectrum of the scattered light. DAS is attracting considerable attention as it complements the conventional distributed measurements. To implement DAS in commercial applications, it is necessary to ensure a sufficiently high signal-noise ratio (SNR) for scattered light detection, suppress its deterioration along the sensing fiber, achieve lower noise floor for weak signals and, moreover, perform high-speed processing within milliseconds (or sometimes even less). In this paper, we present a new, real-time DAS, realized by using the time gated digital-optical frequency domain reflectometry (TGD-OFDR) method, in which the chirp pulse is divided into overlapping bands and assembled after digital decoding. The developed prototype NBX-S4000 generates a chirp signal with a pulse duration of 2 μs and uses a frequency sweep of 100 MHz at a repeating frequency of up to 5 kHz. It allows one to detect sound waves at an 80 km fiber distance range with spatial resolution better than a theoretically calculated value of 2.8 m in real time. The developed prototype was tested in the field in various applications, from earthquake detection and submarine cable sensing to oil and gas industry applications. All obtained results confirmed effectiveness of the method and performance, surpassing, in conventional SM fiber, other commercially available interrogators.

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A Novel Corrosion Monitoring and Prediction System Utilizing Advanced Artificial Intelligence

10.2118/204580-ms ◽

2021 ◽

Author(s):

Klemens Katterbauer ◽

Waleed Dokhon ◽

Fahmi Aulia ◽

Mohanad Fahmi

Keyword(s):

Real Time ◽

Oil And Gas ◽

Salt Water ◽

Oil And Gas Industry ◽

Training Dataset ◽

Metal Loss ◽

Corrosion Monitoring ◽

Prediction System ◽

Well Performance ◽

Data Driven Approach

Abstract Corrosion in pipes is a major challenge for the oil and gas industry as the metal loss of the pipe, as well as solid buildup in the pipe, may lead to an impediment of flow assurance or may lead to hindering well performance. Therefore, managing well integrity by stringent monitoring and predicting corrosion of the well is quintessential for maximizing the productive life of the wells and minimizing the risk of well control issues, which subsequently minimizing cost related to corrosion log allocation and workovers. We present a novel supervised learning method for a corrosion monitoring and prediction system in real time. The system analyzes in real time various parameters of major causes of corrosion such as salt water, hydrogen sulfide, CO2, well age, fluid rate, metal losses, and other parameters. The data are preprocessed with a filter to remove outliers and inconsistencies in the data. The filter cross-correlates the various parameters to determine the input weights for the deep learning classification techniques. The wells are classified in terms of their need for a workover, then by the framework based on the data, utilizing a two-dimensional segmentation approach for the severity as well as risk for each well. The framework was trialed on a probabilistically determined large dataset of a group of wells with an assumed metal loss. The framework was first trained on the training dataset, and then subsequently evaluated on a different test well set. The training results were robust with a strong ability to estimate metal losses and corrosion classification. Segmentation on the test wells outlined strong segmentation capabilities, while facing challenges in the segmentation when the quantified risk for a well is medium. The novel framework presents a data-driven approach to the fast and efficient characterization of wells as potential candidates for corrosion logs and workover. The framework can be easily expanded with new well data for improving classification.

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Technology Focus: Offshore Drilling and Completion (October 2021)

Journal of Petroleum Technology ◽

10.2118/1021-0045-jpt ◽

2021 ◽

Vol 73 (10) ◽

pp. 45-45

Author(s):

Martin Rylance

Keyword(s):

Machine Learning ◽

Real Time ◽

Oil And Gas ◽

Hybrid Approach ◽

Data Communication ◽

Oil And Gas Industry ◽

Oil Field ◽

Real Time Processing ◽

National University ◽

Seoul National University

Communication and prediction are symmetrical. Communication, in effect, is prediction about what has happened. And prediction is communication about what is going to happen. Few industries contain as many phases, steps, and levels of interface between the start and end product as the oil and gas industry—field, office, offshore, plant, subsea, downhole, not to mention the disciplinary, functional, managerial, logistics handovers, and boundaries that exist. It therefore is hardly surprising that communication, in all its varied forms, is at the very heart of our business. The papers selected this month demonstrate how improved communication can deliver the prediction required for a variety of reasons, including safety, efficiency, and informational purposes. The application of new and exciting ways of working, partially accelerated by recent events, is leading to breakthrough improvements on all levels. Real-time processing, improved visualization, and predictive and machine-learning methods, as well as improvements in all forms of data communication, are all contributing to incremental enhancements across the board. This month, I encourage the reader to review the selected articles and determine where and how the communication and prediction are occurring and what they are delivering. Then perhaps consider performing an exercise wherein your own day-to-day roles—your own areas of communication, interfacing, and cooperation—are reviewed to see what enhancements you can make as an individual. You may be pleasantly surprised that some simple tweaks to your communication style, frequency, and format can deliver quick wins. In an era of remote working for many individuals, it is an exercise that has some value. Recommended additional reading at OnePetro: www.onepetro.org. OTC 30184 - Augmented Machine-Learning Approach of Rate-of-Penetration Prediction for North Sea Oil Field by Youngjun Hong, Seoul National University, et al. OTC 31278 - A Digital Twin for Real-Time Drilling Hydraulics Simulation Using a Hybrid Approach of Physics and Machine Learning by Prasanna Amur Varadarajan, Schlumberger, et al. OTC 31092 - Integrated Underreamer Technology With Real-Time Communication Helped Eliminate Rathole in Exploratory Operation Offshore Nigeria by Raphael Chidiogo Ozioko, Baker Hughes, et al.

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Digital Oil Field; The NPDC Experience

10.2118/207169-ms ◽

2021 ◽

Author(s):

Henry Ijomanta ◽

Lukman Lawal ◽

Onyekachi Ike ◽

Raymond Olugbade ◽

Fanen Gbuku ◽

...

Keyword(s):

Real Time ◽

Oil And Gas ◽

Data Gathering ◽

Oil And Gas Industry ◽

Oil Field ◽

Battery Life ◽

Gas Industry ◽

Field Management ◽

Digital Oilfield ◽

Data Analytic

Abstract This paper presents an overview of the implementation of a Digital Oilfield (DOF) system for the real-time management of the Oredo field in OML 111. The Oredo field is predominantly a retrograde condensate field with a few relatively small oil reservoirs. The field operating philosophy involves the dual objective of maximizing condensate production and meeting the daily contractual gas quantities which requires wells to be controlled and routed such that the dual objectives are met. An Integrated Asset Model (IAM) (or an Integrated Production System Model) was built with the objective of providing a mathematical basis for meeting the field's objective. The IAM, combined with a Model Management and version control tool, a workflow orchestration and automation engine, A robust data-management module, an advanced visualization and collaboration environment and an analytics library and engine created the Oredo Digital Oil Field (DOF). The Digital Oilfield is a real-time digital representation of a field on a computer which replicates the behavior of the field. This virtual field gives the engineer all the information required to make quick, sound and rational field management decisions with models, workflows, and intelligently filtered data within a multi-disciplinary organization of diverse capabilities and engineering skill sets. The creation of the DOF involved 4 major steps; DATA GATHERING considered as the most critical in such engineering projects as it helps to set the limits of what the model can achieve and cut expectations. ENGINEERING MODEL REVIEW, UPDATE AND BENCHMARKING; Majorly involved engineering models review and update, real-time data historian deployment etc. SYSTEM PRECONFIGURATION AND DEPLOYMENT; Developed the DOF system architecture and the engineering workflow setup. POST DEPLOYMENT REVIEW AND UPDATE; Currently ongoing till date, this involves after action reviews, updates and resolution of challenges of the DOF, capability development by the operator and optimizing the system for improved performance. The DOF system in the Oredo field has made it possible to integrate, automate and streamline the execution of field management tasks and has significantly reduced the decision-making turnaround time. Operational and field management decisions can now be made within minutes rather than weeks or months. The gains and benefits cuts across the entire production value chain from improved operational safety to operational efficiency and cost savings, real-time production surveillance, optimized production, early problem detection, improved Safety, Organizational/Cross-discipline collaboration, data Centralization and Efficiency. The DOF system did not come without its peculiar challenges observed both at the planning, execution and post evaluation stages which includes selection of an appropriate Data Gathering & acquisition system, Parts interchangeability and device integration with existing field devices, high data latency due to bandwidth, signal strength etc., damage of sensors and transmitters on wellheads during operations such as slickline & WHM activities, short battery life, maintenance, and replacement frequency etc. The challenges impacted on the project schedule and cost but created great lessons learnt and improved the DOF learning curve for the company. The Oredo Digital Oil Field represents a future of the oil and gas industry in tandem with the industry 4.0 attributes of using digital technology to drive efficiency, reduce operating expenses and apply surveillance best practices which is required for the survival of the Oil and Gas industry. The advent of the 5G technology with its attendant influence on data transmission, latency and bandwidth has the potential to drive down the cost of automated data transmission and improve the performance of data gathering further increasing the efficiency of the DOF system. Improvements in digital integration technologies, computing power, cloud computing and sensing technologies will further strengthen the future of the DOF. There is need for synergy between the engineering team, IT, and instrumentation engineers to fully manage the system to avoid failures that may arise from interface management issues. Battery life status should always be monitored to ensure continuous streaming of real field data. New set of competencies which revolves around a marriage of traditional Petro-technical skills with data analytic skills is required to further maximize benefit from the DOF system. NPDC needs to groom and encourage staff to venture into these data analytic skill pools to develop knowledge-intelligence required to maximize benefit for the Oredo Digital Oil Field and transfer this knowledge to other NPDC Asset.

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Remote Operations: The New Norm

10.2523/iptc-21802-ms ◽

2021 ◽

Author(s):

Rachel Gajanan Kakade ◽

Pawandeep Singh Bagga

Keyword(s):

Real Time ◽

Swot Analysis ◽

Oil And Gas ◽

Resistance To Change ◽

Cost Optimization ◽

Oil And Gas Industry ◽

Global Scale ◽

Service Companies ◽

The World ◽

Remote Operations

Abstract In recent years, we have seen some refined drilling technologies crop up all over the world. These have given rise to implementation of remote centers to work on real time decision making with the wells. While drilling is in process, there are technologies that enable real time transmission of data and voice to and from remote sites, helping in real time intelligent commands and responses. It is hence now possible to form a single team of experts to monitor and control drilling operations. The development of remote operations in the oil and gas industry has evolved over years starting 2004 at different speeds in different regions of the world. For example, it took longer to reach the US land market because of resistance to change at the rig site. The decrease in oil prices in 2014 however, pushed remote operations into existence to reduce cost. Due to challenges such as either oilfield culture, company strategy, human factor, legal factor etc., it was not exactly the "norm". Fast forward to 2020 when the Covid-19 pandemic hit the oil industry into another slump, service companies have been pushed into the remote operations world. To learn with the times, this may be the new norm and maybe an excellent one. Many service companies have successfully performed operations wells globally increasing not only the efficiency of wellsite operations but also contributing to cost optimization and safety. During implementation, it is observed that remote operations are less a technical challenge, and more a value challenge requiring confidence from all stakeholders. In terms of drilling and operational efficiency, the results observed globally are significant, with fewer trips for M/LWD failure, as well as significant reductions in M/LWD NPT while drilling. This paper discusses the implementation of remote operations at global scale, lesson learnt on day-to-day basis, optimization opportunities, business workflow, positives such as business continuity, safety aspect and last but not the least, the environmental impact. The paper also talks of changes and effects of Covid-19 Pandemic on these operations. Remote operations prepare us well for such pandemic and it may be the safer way to operate now on. Also discussed are the keys to successful remote operations and various examples of remote operations establishments throughout the globe. Lastly a SWOT analysis is done to conclude how remote operations will help operators to add more value to operations and show that remote operations is the new future.

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