Reviews and PDFs

“I never read a book I must review; it prejudices you so.” -- Oscar Wilde

Actually, I like reviews. Over the years I have developed a number of Google Scholar alerts that help me keep up with developments in various areas of interest to me.

One problem with the results of these alerts is that much of those results reside behind a paywall. So as I browse the alerts that appear in my inbox, I focus on two things … reviews, and PDFs. In many cases, both are available in full text at no charge.

TIP: Create Google Scholar alerts in your field of interest. To save time, look at the items labeled review or PDF.

Here is a list of titles that have come up as a result of my Google Scholar alerts over the past few months.

HINT: If any of the titles seems interesting to you, just copy the title and paste it into a Google search to find the full text article.

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Machine Learning and internet of things in industry 4.0-A review
Artificial intelligence-based solutions for climate change-a review
Developing an IoT-Based Internal Pipe Inspection Robot Challenges, Solutions & Future Directions
Effective Tracking of Unknown Clustered Targets Using A Distributed Team of Mobile Robots
How Blockchain Technology Can Address Circularity and Trace Emission in the Energy Sector
Utilizing Blockchain Technology for Oil and Gas Industry
A Review of Nanomaterials and their Applications in Oil and Petroleum Industries
Data Security and Cybersecurity in Saudi Arabia
Identification of Fourth Industrial Revolution technologies using PATSTAT data
Industry 4.0-Features Adopting Digital Technologies in the Oil and Gas Industry
Strengths and Weaknesses of Science and Technology Institutions in Arab countries
Use Case of Digital Technology Creating Value in Today’s Oil & Gas Industry by Reducing Cost and Enhancing Drilling Performance
Utilizing Remote Sensing and Data Analytics Techniques to Detect Methane Emissions from the Oil and Gas Industry and Assist with Sustainability Metrics
Advances in gas-to-liquid technology for environmentally friendly fuel synthesis-Analytical review of world achievements
Challenges and Opportunities of Low Viscous Biofuel─A Prospective Review
Improving Smart Contracts Management And Designs For Blockchain Systems
Industrial Engineering with Large Language Models-A case study of ChatGPT’s performance on Oil & Gas problems
Machine Learning Algorithms for Failure Prediction Model and Operational Reliability of Onshore Gas Transmission Pipelines
Predictive big data analytics for drilling downhole problems-A review
Renewable diesel Production-A Review
Zero programming robotics in additive manufacturing repairs
The+benefits,+Challenges,+and+Future+of+Blockchain+and+The+Internet+of+Things
A Bibliometric Analysis of the role of Industry 4.0 Sensors in Digital Transformation
A Brief Introduction to Microbial Corrosion in the Oil Industry (2022)
A current review on electron beam assisted additive manufacturing technology
A Review of Advances in Cold Spray Additive Manufacturing
A Review on the Implementation of the BIM Methodology in the Operation Maintenance and Transport Infrastructure
A Short Review on the Corrosion Behaviour of Wire and Arc Additive Manufactured Materials
A Systematic Approach to Determine the Cutting Parameters of AM Green Zirconia in Finish Milling
A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring
A techno-economic approach for decision-making in metal additive manufacturing-metal extrusion versus single and multiple laser powder bed fusion
Accelerating the renewable energy sector through Industry 4.0-Optimization opportunities in the digital revolution
Additive Manufacturing Processes in Selected Corrosion Resistant Materials A State of Knowledge Review
Advancing Refining Petrochemicals Integration In The Arabian Gulf
AI-Now-2023-Landscape-Report-Confronting tech power
An assessment of the processing parameters and application of fibre-reinforced polymers (FRPs) in the petroleum and natural gas industries-A review
Analysis of Hydrogen Production Methods Using the Analytic Hierarchy Process
Application of machine learning and artificial intelligence in oil and gas industry
Applications of AI in oil and gas projects towards sustainable development-a systematic literature review
Challenges and recent progress on the application of rapid sand casting for part production-a review
Changes and improvements in Industry 5.0-A strategic approach to overcome the challenges of Industry 4.0
Current Downhole Corrosion Control Solutions and Trends in the Oil and Gas Industry-A Review
Cyber Attacks on Unmanned Aerial Vehicles and Cyber Security Measures
Cyber Security in Blockchain Technology
Deep Learning for Magnetic Flux Leakage Detection and Evaluation of Oil & Gas Pipelines A Review
Dibenzothiophene Removal from Fuel Oil by Metal-Organic Frameworks Performance and Kinetics
Digital transformation in asset-intensive organisations-The light and the dark side
Digital Transformation in Oil and Gas Industry-Opportunities and Challenges
Edge Computing Vs. Cloud Computing-An Overview Of Big Data Challenges And Opportunities For Large Enterprises
Experimental Investigation on the Wear Performance of Nano-Additives on Degraded Gear Lubricant
FCC Catalyst Accessibility—A Review
Green Energy Sources Reduce Carbon Footprint of Oil & Gas Industry Processes-A Review
Green Future Index 2023 (MIT)
Identify blockchain technology strategies in the oil and gas industry
Industrial Decarbonization Roadmap
Industrial internet of things (IIoT) in energy sector
Integration of experimental hydroprocessing and FCC data with process (Aspen Plus) and refinery optimization (Aspen PIMS) models
Investigation and Implementation of IoT Based Oil & Gas Pipeline Monitoring System
IoT and Blockchain Integration-Applications, Opportunities, and Challenges
Leveraging Blockchain Technology to Create a More Resilient Supply Chain for Energy Industry
Life cycle assessment of metal products-A comparison between wire arc additive manufacturing and CNC milling
Manufacturing of carbon fiber reinforced thermoplastics and its recovery of carbon fiber-A review
Modeling and Simulation of an Octorotor UAV with Manipulator Arm
Modeling-aided coupling of catalysts, conditions, membranes, and reactors for efficient hydrogen production from ammonia
Potential Use of Construction Waste for the Production of Geopolymers-A Review
Predictive Deep Learning for Pitting Corrosion Modeling in Buried Transmission Pipelines
Preparation, characterization, applications and future challenges of Nanomembrane-A review
Recent Advances, Future Trends, Applications and Challenges of Internet of Underwater Things (IoUT)-A Comprehensive Review
Refinery 4.0, a Review of the Main Challenges of the Industry 4.0 Paradigm in Oil & Gas Downstream (2022)
Security threats, countermeasures, and challenges of digital supply chains
Smart Spare Parts Management-A digital supply network perspective
Solar fuel production from hydrogen sulfide-an upstream energy perspective
Suitability assessment of high-power energy storage technologies for offshore oil and gas platforms-A life cycle cost perspective
Systemic circular economy solutions for fiber reinforced composites
Technological Modernizations in the Industry 5.0 Era-A Descriptive Analysis and Future Research Directions
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Google® Better!
Jean Steinhardt served as Librarian, Aramco Americas (https://americas.aramco.com/ ), Engineering Division, for 13 years. He now heads Jean Steinhardt Consulting LLC, producing the same high quality research that he performed for Aramco.

Follow Jean’s blog at: http://desulf.blogspot.com/ for continuing tips on effective online research
Email Jean at jstoneheart@gmail.com with questions on research, training, or anything else

Vive la révolution! The Fourth Industrial Révolution, that is …

The Fourth Industrial Revolution is a phrase that has been bandied about for the past several years. It suggests that we are on the verge of fundamental changes in the way we produce the goods that make life worth living.

But what is the Fourth Industrial Revolution exactly? And how do we identify the technologies that will make it happen?

Here are two sources that can help us start down that long and winding road.

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Identification of Fourth Industrial Revolution technologies using PATSTAT data
Menéndez de Medina, M., Nomaler, Ö., & Verspagen, B. (2023). Identification of Fourth Industrial Revolution technologies using PATSTAT data. UNU-MERIT. UNU-MERIT Working Papers No. 023 https://www.merit.unu.edu/publications/wppdf/2023/wp2023-023.pdf
Published: 02/06/2023
Maastricht Economic and social Research institute on Innovation and Technology (UNU-MERIT)
email: info@merit.unu.edu | website: http://www.merit.unu.edu
Abstract
This document provides a methodological procedure to identify the Fourth Industrial Revolution technologies using patent data. Attempts to distinguish these technologies have frequently relied on the European Patent Office (2017, 2020a) methods or have mainly leaned on technical codes and keyword classifications. Frequently, these studies have the limitation of collecting technologies arbitrarily and without a deep justification. Only the latest report from the European Patent Office (EPO, 2020b) attempts to detail the procedure to recognize the Fourth Industrial Revolution technologies. However, it does not offer the possibility of being replicated by scholars outside the organization. This article delivers a procedure to collect Fourth Industrial Revolution patents relying on key concepts from a detailed literature review - focused on whether they make up a new revolution and its conceptualization over timeand the EPO (2020b) report identification method. Subsequently, the evolution of these technologies and the principal trends are exposed. Finally, the search queries and the list of identified patents are available (in the Appendix) to replicate or adapt for other academic purposes.
“4IR technologies are understood in this paper as co-evolving “technological systems” that combine innovations in the fields of digital data transmission, smart connected devices, computing, communication and connectivity technologies, the scope of which goes beyond the manufacturing sector and includes a broad variety of technological domains such as agriculture, health, home, infrastructure and services.”
A recent study of Confraria et al. (2021) is not directly focused on the identification of specific 4IR technologies but on the identification of emerging technologies.
https://cris.maastrichtuniversity.nl/en/publications/identification-of-fourth-industrial-revolution-technologies-using

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Patents and the Fourth Industrial Revolution: The global technology trends enabling the data-driven economy (EPO)
This study explains the trends in a variety of digital technologies like faster wireless internet, smart sensors, “big data” and artificial intelligence. Taken together these are transforming healthcare, transport, agriculture and many more sectors – they account for over 10% of all global innovation.
https://www.epo.org/service-support/publications.html?pubid=222#tab3
https://www.epo.org/news-events/in-focus/ict/fourth-industrial-revolution.html
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Google® Better!
Jean Steinhardt served as Librarian, Aramco Americas (https://americas.aramco.com/ ), Engineering Division, for 13 years. He now heads Jean Steinhardt Consulting LLC, producing the same high quality research that he performed for Aramco.

Follow Jean’s blog at: http://desulf.blogspot.com/ for continuing tips on effective online research
Email Jean at jstoneheart@gmail.com with questions on research, training, or anything else

Big Data Analytics … Whaaat?

Whaaat?
To be amazed by something and you can't believe it is real. Not to be confused with the question "what." It is an expression of disbelief or amazement. – Urban Dictionary (https://www.urbandictionary.com )

I admit to being a little hazy on what “big data analytics” actually means. An article that popped up in a recent Google Scholar Alerts search result helped me out on that front.

TIP: Set up a Google Scholar alert on any topic of interest to you. Results, in my experience, are about 3 (three) per cent relevant to my specific interests. But it requires only two minutes of my time to browse the results for the rare truffles that tantalize my taste buds.

The name of the article … wait for it … is …

Predictive big data analytics for drilling downhole problems: A review

Do not be misled by the title of the article … It is about so much more than downhole drilling. In reading the article, I came to understand …

The goal of big data analytics: To analyze enormous sets of unstructured data in real time to enable nearly instantaneous decisions.

So, here are excerpts from the article. The full text of the article, by the way, is available for the low, low price of nothing at: https://www.sciencedirect.com/science/article/pii/S2352484723007710

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EXCERPTS
Energy Reports 9 (2023) 5863–5876
Predictive big data analytics for drilling downhole problems: A review
Aslam Abdullah M. ∗, Aseel A., Rithul Roy, Pranav Sunil
Petroleum Engineering Research Group, School of Chemical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
ABSTRACT
With the recent introduction of data recording sensors in exploration, drilling and production processes, the oil and gas industry has transformed into a massively data-intensive industry. Big data analytics has acquired a great deal of interest from researchers to extract and use all the possible information. This paper presents an outline for predictive big data analytics to forecast and analyze some downhole problems such as pipe sticking, dog leg and pipe failure depending on several variables. Different methodologies were studied under big data, enabling the identification of the paradigm change in data storage and processing while handling vast diversified data generated in a short span of life. The evaluated data pattern sets are fed into different established predictive models and risk prediction windows to highlight future irregularities for the prevention of accidents. Finally, the game theory is used to evaluate the best predictive model to discover the optimal model for the identification of downhole problems.

One of the key phases of the oil and gas industry’s digitization seems to be big data (BD) analytics. The oil and gas exploration and production industries now generate enormous datasets on a daily basis as a result of recent technological advancements.
Big data consists of unstructured (not ordered and text-heavy) and multi-structured data (containing many data formats arising from interactions between humans and machines) (Trifu and Ivan, 2016). The phrase big data (also known as big data analytics or business analytics) identifies the magnitude of the accessible data collection. There are further properties of the data that make it suitable for big data applications. IBM appropriately identifies these features as three V’s. These three V’s refer to volume, variety, and velocity (Pence, 2014). However, recent publications have added two additional V’s to provide a detailed explanation of big data. The other Vs consist of veracity and value (Ishwarappa and Anuradha, 2015).
Big data is not the outcome of a single silver-bullet technology, but rather the highly complementary combination of several technologies and creative concepts (Perrons and Jensen, 2015). Despite the fact that this type of analytics depends on solid data science foundations, there are a number of key considerations for putting these approaches into effect (Kezunovic et al., 2020). The storage and processing of large data sets, as well as the transformation of large data, sets into knowledge, are the primary challenges connected with big data. It is often believed that the massive amount of big data means that useful information is hidden and must be unearthed, but analysts cannot simply intuit the data’s value content (Shull, 2013). In any industry, big data analytics can give new perspectives. It may result in the accurate identification or forecasting of new scientific hypotheses, consumer behavior, societal phenomena, weather patterns, and economic situations (Jayalath et al., 2014). Table 1 compares traditional data and big data analytics.
5.2. Big data storage and management
The downhole drilling data obtained is an enormous, unstructured, and complicated data collection that is challenging for conventional data processing technologies to manage (Chen et al., 2014). The information gathered by the sensors is multidimensional, and due to the ever-increasing amount of data being created, quicker and more effective methods of data analysis have become necessary. Along with the necessary infrastructures for storing and managing enormous data, there are also specific tools and methodologies for big data analytics that are essential for making successful judgments at the proper time (Elgendy and Elragal, 2014). There are techniques and tools which can analyze (process, decode, and interpret) the operation status and the change in parameters simultaneously (Kale et al., 2015; Pritchard et al., 2016). The data are gathered by drilling operators or service providers, and downhole data acquired from global geographic drilling operations will continually amass and grow in quantity, ultimately becoming a dataset that exceeds the storage and processing capacity of a single server (Chen et al., 2014). Multiple distributed servers are used to store, transmit, and process the collected data before extracting, transforming, and loading it into different databases for advanced analytics. These data are very large, ranging in size from Terabytes (TBs) to Petabytes (PBs) (Meeker and Hong, 2014). Moreover, large data sets may have considerable variability, hindering the data processing and administration, and varying integrity as a result of data inconsistency, incompleteness, complexity, delay, deceit, assumptions, and horizontal scalability to merge dissimilar information (Chen et al., 2014; Elgendy and Elragal, 2014; Hu et al., 2014). Relational databases, data marts, and data warehouses are classic techniques for storing and retrieving structured data.
Several solutions, such as distributed databases and Massive Parallel Processing (MPP) databases for delivering high query productivity and platform stability, as well as non-relational databases, were utilized for big data. Non-relational databases, such as NoSQL, are created to store and manage non-relational data. NoSQL seeks huge scalability, a flexible data format used for streamlined creation and deployment of applications. Compared to relational database systems, NoSQL decouples data storage and management. These databases emphasize scalable, highperformance data storage and enable data administration operations to also be done at the application level as opposed to database-specific languages (Bakshi, 2012).
After storing the data, it has to be analyzed using big data tools and techniques. According to (He et al., 2011), there are four essential needs for processing large amounts of data. The foremost prerequisite is rapid data processing. Due to the fact that disk and internet traffic conflicts with request performance during performing data, it is vital to minimize the time necessary for performing data. The next criterion is the speed of query execution. Many queries are response-time essential due to the demands of high workloads and real-time requests. As a result, the data placement structure needs to be able to maintain high query processing rates as the number of inquiries grows quickly. Consequently, the prerequisite for large-scale data collection is the efficient use of storage capacity.
Due to limited disk space, it is essential that data storage must be carefully handled throughout processing and that challenges regarding the storage of the data should be minimalized. The quick expansion in user behavior might need extensible storage space and processing speed. The ability to adjust well to workload patterns that are extremely dynamic is the final need. Massive datasets are processed by a variety of applications and consumers, for a variety of purposes in a diverse range of ways, requiring the operating system to be highly adaptive to unforeseen processing dynamics and not specific to each of these workload patterns (He et al., 2011).
5.3.1. Challenges in big data analysis
Data mining is the extraction of relevant information and insights from large datasets using statistical and computational methods. Data mining is an integral part of big data analytics, which entails processing, analyzing, and interpreting large and complex datasets to discover patterns, trends, and insights that can assist organizations in making informed decisions. Nonetheless, data extraction for big data analytics is not error-free. During the data collection process, these errors can influence the quality and dependability of the insights generated from the data (Amirian et al., 2015). Examples of common data collection errors include:
• Sampling errors: When the sample data used for analysis is not representative of the population as a whole.
• Measurement errors: When the data collected is not accurate or trustworthy.
• Data entry errors: Errors in data entry occur when information is erroneously recorded.
• Processing errors: Errors in processing occur when data is improperly processed.
Data cleansing, data validation, data normalization, and data transformation are some of the methods used by data mining and analytics practitioners to reduce the likelihood of these errors. Data cleansing is the process of identifying and rectifying data errors and inconsistencies, such as absent values, outliers, and duplicate entries. Validating data involves validating its accuracy and completeness, such as by ensuring that all required fields are populated. The normalization of data entails transforming the data into a standard format, such as converting all dates to a common format. For example, consider a scenario in which a drilling company wishes to improve the precision of its drilling operations by analyzing data collected from downhole sensors. The collected data include measurements of temperature, pressure, and other parameters that help the company determine the characteristics of the drilled rock formation. Nevertheless, errors may occur during the data collection procedure, such as faulty sensors or incomplete data due to technical issues. These errors can result in erroneous analysis and may lead to drilling in the incorrect location, resulting in increased costs and decreased efficiency (Liu et al. 2022).
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Google® Better!
Jean Steinhardt served as Librarian, Aramco Americas (https://americas.aramco.com/ ), Engineering Division, for 13 years. He now heads Jean Steinhardt Consulting LLC, producing the same high quality research that he performed for Aramco.

Follow Jean’s blog at: http://desulf.blogspot.com/ for continuing tips on effective online research
Email Jean at jstoneheart@gmail.com with questions on research, training, or anything else