The 19th PTC-Pipeline Technology Conference is scheduled for 8-11
April 2024, Berlin. Conference details are available at https://www.pipeline-conference.com/
One of the conference tracks is …
Digital
Transformation and AI: Shaping the Future of Pipeline Operations
The panel discussion "Digital Transformation and AI: Shaping the
Future of Pipeline Operations" delves into the transformative power of
digitalization and artificial intelligence (AI) and its potential to improve
pipeline operations. AI, combined with advanced data analytics, machine learning,
and big data management, holds immense promise for enhancing pipeline
efficiency, safety, and reliability. The panel discussion will provide
actionable insights for pipeline operators seeking to embrace digital
transformation – minimizing operational downtime, reducing maintenance costs,
and optimizing pipeline performance.
///////
For those of you who are interested in digital transformation in the pipeline
industry, the following articles may interest you …
Design And Analysis of a Pipe Inspection
Caterpillar Robot Using Different Materials (2023)
https://www.ijariit.com/manuscripts/v7i2/V7I2-1341.pdf
Three-dimensional mapping of pipelines using laser ranging and a gyroscope
(2023)
https://www.nature.com/articles/s41598-023-47856-5
Development of a novel virtual reality-enabled remote monitoring device for
maintenance of cathodic protection systems on oil and gas pipelines (2023)
https://www.nature.com/articles/s41598-023-43159-x
Investigation and Implementation of IoT Based Oil & Gas Pipeline Monitoring
System (2023)
https://doi.org/10.36079/lamintang.ijortas-0501.477
Machine Learning Algorithms for Failure Prediction Model and Operational
Reliability of Onshore Gas Transmission Pipelines (2023)
https://doi.org/10.14716/ijtech.v14i3.6287
Predictive Deep Learning for Pitting Corrosion Modeling in Buried Transmission
Pipelines (2023)
https://doi.org/10.1016/j.psep.2023.04.010
///////
NOTE that I came across these open
access articles as the result of an alert I created in Google Scholar …
TIP:
Use Google Scholar (https://scholar.google.com/)
to set up your own alerts. It’s easy. Perform a search. Then save the search
string as an alert.
///////
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
Thursday, December 7, 2023
Conference Alert: PTC Berlin 2024
Sunday, August 6, 2023
Conference Alert-Digitalization in Oil and Gas
Too much agreement kills a chat. --
Eldridge Cleaver
The Energy Conference Network (https://energyconferencenetwork.com/
) has announced the Digitalization in Oil and Gas conference
(https://www.digitalizationoilgas.com/23/2647079),
scheduled for September 14-15, 2023 in Houston, Texas.
REGISTRANTS include … Aramco, Dow, ExxonMobil, Sabic, Chevron … to name a few.
The conference is organized into FIVE TRACKS … IoT in Oil & Gas, Digital
Twins in Oil & Gas, Blockchain in Oil & Gas, Edge Computing
Technologies in Oil & Gas, and Connectivity in Oil & Gas.
TIP:
View a recent Desulfurization Blog
post: Reviews and
PDFs (http://desulf.blogspot.com/2023/06/reviews-and-pdfs.html)
for a list of free full text articles on the five conference tracks. Pick out
one or two in preparation for the conference.
For example:
///////
Industrial
Engineering with Large Language Models: A case study of ChatGPT's performance on Oil & Gas problems
Large Language Models (LLMs) have shown great potential in solving complex
problems in various fields, including oil and gas engineering and other
industrial engineering disciplines like factory automation, PLC programming
etc. However, automatic identification of strong and weak solutions to
fundamental physics equations governing several industrial processes remain a
challenging task. This paper identifies the limitation of current LLM
approaches, particularly ChatGPT in selected
practical problems native to oil and gas engineering but not exclusively. The
performance of ChatGPT in solving complex
problems in oil and gas engineering is discussed and the areas where LLMs are
most effective are presented.
Cite as: arXiv:2304.14354
source: https://doi.org/10.48550/arXiv.2304.14354
///////
And here’s another …
///////
Blockchain in Oil and Gas Supply Chain: A Literature Review from User
Security and Privacy Perspective (2023)
International Symposium on Human Aspects of Information Security and
Assurance
The oil and gas industry grapples with supply chain process challenges and is
known to have high risks, significant investments, and large asset volumes.
These include (i) tracking extracted resources, (ii) managing equipment and
assets, (iii) preventing company data leaks, (iv) managing data, and (v)
ensuring process security and integrity. Such issues can result in human
errors, misguided decisions due to inaccurate data, inflated operating costs,
transaction delays, and fraud. Blockchain technology could provide solutions to
some of these problems. For example, it facilitates reliable and secure data
storage, ensuring data integrity. Additionally, blockchain-based smart
contracts could deter oil trade fraud by automating contract execution when
agreement conditions are met. By incorporating blockchain technology,
businesses in the oil and gas sector can enhance their operations’ efficiency,
transparency, and security, leading to improved decision-making and cost
savings.
source: https://doi.org/10.48550/arXiv.2306.16576
///////
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
Tuesday, June 6, 2023
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
///////
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).
///////
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
Thursday, March 31, 2022
Transformers-The Conference
Hydrocarbon Processing’s IRPC-International Refining and Petrochemical
Conference is scheduled for May 17-18 2022, in Houston. The theme: Digital Transformation in the Refining and Petrochemical
Industry
As described in an email that landed in my inbox …
///////
For 14 years, Hydrocarbon Processing’s International Refining and
Petrochemical Conference (IRPC) has been the leading downstream technical
conference. Offering the flexibility and increased exposure, IRPC
2022 attendees will be able to connect with colleagues and potential
customers face-to-face as well as livestreaming, doubling your reach to
the global HPI.
For 2022, IRPC will be
held on May 17-18, 2022. This two-day in-person conference and exhibition will
provide a high-level technical forum in which key players in the global
petrochemical and refinery sector will meet to share knowledge and learn about
best practices and the latest industry advances.
///////
Three presenters featured in the email appear below …
///////
Digital transformation
in the refining and petrochemical industries: Looking back, looking forward
Presenter: Douglas White, Principal Consultant, Emerson Automation
Solutions
TIP: Search Google® Scholar (scholar.google.com): Douglas White
Emerson Automation Solutions
A few results …
///////
[PDF] Optimize
energy use in distillation
https://www.aiche.org/sites/default/files/cep/20120335.pdf
DC White - Chemical Engineering Progress, 2012 - aiche.org
… state-of-the-art, advanced energy automation and optimization systems
in process plants
around the world. White has published more than 50 technical papers on
these subjects. He …
[PDF] Advanced
automation technology reduces refinery energy costs
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.468.7389&rep=rep1&type=pdf
DC White - Oil and Gas Journal, 2005 - Citeseer
… • Advanced automation systems evaluate alternatives and … White@EmersonProcess.com)
is vice-president, APC services, for the process systems and solutions
division of Emerson …
[PDF] The
economics of advanced automation
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.195.6634&rep=rep1&type=pdf
DC White - Paper 73c, presented to the AIChE 2003 Spring …,
2003 - Citeseer
Advanced plant automation applications incorporate a wide variety of
devices and software
including" smart" sensors and other" smart'equipment, improved
software tools for modeling/…
[PDF] The
“smart” plant: Economics and technology
http://www.nt.ntnu.no/users/skoge/prost/proceedings/focapo_2003/pdffiles/papers/090.pdf
… Note that the advanced automation systems we are reviewing affect every … solution to this
problem is again to use plant measurements, modeling and analytical techniques to automate …
///////
Artificial intelligence-based distillation capacity improvement and energy optimization of a refinery.
Presenter: Rajendra Mutha, Chief Solution Officer, Flutura Business Solutions
TIP: Google® Rajendra Mutha Flutura
One result …
Rajendra Mutha
Chief Solution Officer at Flutura
Rice University - Jesse H. Jones Graduate School of Management
Houston, Texas, United States
About
I have experience in leadership roles and have led organizations through tough economic situations. I have led skilled professionals in digitalization in manufacturing, economics and planning, manufacturing operations, process technical support, research and development, and consulting organizations and consistently delivered results. I have helped organizations to expedite execution, improve performance and hone best practices. I have effectively worked across boundaries: worked extensively with business leaders for performance, engineers in manufacturing operations for executing strategy and R&D groups to inject innovation.
Specialties: Digitalization in Manufacturing, Performance Metrics and Control, Planning and Scheduling, Business Economics, Process Control and Optimization.
Industries: Oil & Gas, Polymer, Iron and Steel, Consulting
source: https://www.linkedin.com/in/rajendra-mutha-44b5014/
///////
Integrate artificial intelligence with natural intelligence:
Presenter: Sergio Fernandes, Chemical Market Leader, Yokogawa
///////
Google® Better!
Jean Steinhardt served as Librarian, Aramco Services, 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 letters@jeansteinhardt.com with questions on research, training, or anything else
Visit Jean’s Web site at http://www.jeansteinhardtconsulting.com/ to see examples of the services we can provide