Late-life
assets present a myriad of challenges that operators must tackle to ensure
continued safety and integrity. Robust data management strategies play an
increasingly pivotal role in optimizing asset performance in mature assets to
ensure economic viability.
As the global oil and gas industry continues to mature, the number of
fields in the late-life phase of production has steadily increased. Currently,
more than 70% of the world’s oil and gas production comes via aging
infrastructure built over mature reserves that are rapidly approaching their
economic limit.1
As reservoirs age, the natural pressure that facilitates hydrocarbon
extraction diminishes, inevitably leading to reduced production rates and the
associated economic implications. Extracting remaining reserves becomes
increasingly challenging. To address this, the industry is increasingly turning
to advanced technologies. Techniques such as polymer and chemical injection,
gas injection, and thermal methods aim to maximize recovery rates and extend
the productive life of these fields. However, these solutions often come with
substantial upfront costs, requiring careful financial planning and investment.
Aging infrastructure presents another hurdle. Pipelines, wells and other
facilities are exposed to harsh environments, in addition to extreme operating
conditions. Wear-and-tear of equipment over time leads to increased inspection
and maintenance needs. The integrity of these assets becomes crucial, as any
failure can result in significant environmental and safety risks. Maintenance
and upgrading of facilities are imperative to ensure safety, operational
efficiency, and compliance with advancing environmental standards. Addressing
the maintenance requirements of aging infrastructure poses not only technical
challenges but also financial and logistical complexities for oil and gas
operators, with many assets in remote locations offshore and not easily accessible.1
Balancing the equation becomes a tricky task, requiring E&P
operators to seek innovative solutions to maintain profitability. The financial
viability of late-life fields becomes especially critical in the context of a
global push towards renewable energy sources, forcing companies to weigh up their
investments against the backdrop of a changing energy landscape.
However, within these challenges
lie opportunities for transformative change. Technologies that enhance the
efficiency of late-life field operations, reduce emissions, and minimize
environmental impact are becoming increasingly pivotal. Digitalization emerges
as a key player in this evolution. Advanced data analytics, artificial
intelligence, and the Internet of Things (IoT) empower operators to optimize
production, predict equipment failures, and streamline maintenance processes.
The digital transformation of mature fields not only extends their productive
lifespan but also enhances safety and environmental stewardship.
From upstream to downstream, for the energy industry to truly harness
the power of data, a robust data management and integrity philosophy is
critical to this success. This commitment ensures that the vast amounts of data
generated are not only utilized for operational excellence but also adhere to
the highest standards of security, integrity, and ethical use, underlining the
industry's path toward a more sustainable and technologically advanced future.
Data management for enhanced reliability. Managing data in mature assets poses a unique set of challenges stemming
from the complex interplay of aging infrastructure, disparate systems, and
evolving regulatory requirements that have changed in the decades since their
inception. As mature fields navigate the later stages of production,
maintaining data integrity is a critical requirement. Data integrity refers to
both static and stored data, and its reliability and accuracy as it flows
through interconnected processes, systems or components.
As these assets reach the later stages of their lives, the sheer volume
of historical data accumulated over years becomes a substantial hurdle. Legacy
systems, often outdated and incompatible with modern technology, hinder
seamless data integration and accessibility. Additionally, the diversity of
data sources, ranging from conditioning monitoring to equipment maintenance
records, presents a challenge in consolidating and analyzing information
cohesively.
The need to retrofit existing facilities for compliance with evolving
environmental regulations further complicates data management efforts,
demanding a difficult balance between historical data preservation and the
integration of new technologies. Navigating this intricate landscape requires
the sector to invest in robust data management strategies and embrace
digitalization, ensuring that insights derived from mature oil and gas assets
contribute meaningfully to operational efficiency and environmental sustainability.
Aging infrastructure and equipment also can lead to sensor malfunctions
or outdated instrumentation, introducing errors in data collection.
Furthermore, different systems implemented at varying points in the asset's
history often result in compatibility issues that can compromise the seamless
flow and integration of data. Establishing robust protocols for data validation
and safety, regular audits, and the implementation of advanced technologies is
imperative.
Assuring this reliability means ensuring data remains high-quality, consistent
and timely. However, the vast amount and speed in which data are generated
today can make data integrity more complex. This challenge is also not limited
to the amount of accumulated data, but also to ensuring its accuracy and
relevance over time.
Extracting and collating data in late-life assets. Imrandd is a data science and engineering business that specializes in
delivering data-driven insights and solutions for maintaining asset performance
throughout the entire life cycle. The company was approached recently by a
North Sea operator to support the development of late-life strategies for safety-critical
pressure systems on their assets. The scope of work required the business to conduct
analysis of integrity and inspection data to create optimized integrity and inspection
plans, targeting essential tasks only before reaching cessation of production
(CoP) dates. It was critical to prioritize safety-critical tasks, with
technical justification in the most cost-effective way possible.
The project’s focus was to maximize the best use of historical data to
identify equipment, where inspection requirements could be removed or modified.
In the initial stage of the project, an extensive review of the available
integrity and inspection data for pressure systems across all installations was
undertaken. This included an appraisal of data available, allocations for risk,
probability of failure and integrity status, historical inspections and current
planned intervals and dates.
Assessments were conducted on both internal and external damage
mechanisms for piping and vessel components with data retrieval first conducted
to gather inspection reports, including internal and external integrity
assessment data. This ensured that all legacy data were extracted to establish
a clear picture of each asset and its component history. Similar data
formatting was required, so full cleansing and correcting of data sources were
delivered to the operator, to provide a singular source of data.
The next step involved the determination of metrics across the quantity
and quality of data available, Fig 1. Throughout this phase, a
substantial number of improvements were made to the data, which identified gaps
and allowed the ability to address obvious errors and discrepancies. Inspection
data were then pulled separately to provide a view on remaining life and support
the Risk Based Inspection (RBI) process. This phase successfully identified circuits
and vessels, where current inspection requirements could be challenged prior to
CoP. These “quick wins” included finding 25 piping circuits on one asset with a
low-risk probability of failure. This Improved the short- and long-term
inspection workload across the asset, as inspection of these circuits was
pushed out to a later date.
Data extraction was carried out by Imrandd’s proprietary EXTRACT
software across multiple platforms. The software ingests and distils disparately
stored, effectively inaccessible data, then digitizes and consolidates it into
one centralized database, ready for analysis. It utilizes a combination of
techniques, including the most recent advances in Optical Character Recognition
(OCR) and computer vision to enable vast volumes of data to be processed fast.
The software produced more than 52,000 test points of data, dating back approximately
20 years. The extraction process was able to support the identification of
systems providing an opportunity for optimization, which was crucial for the analysis
and interpretation in phase 2 of the project. Continual checks and balances
were provided, which demonstrated an estimated 20%-to-30% reduction in
inspection scopes for phase 1.
Innovative software applications. During phase
2, Imrandd deployed its proprietary analytics tool EXACT to accurately
interpret the data it had collated, Fig. 2. The software cleanses,
corrects and interprets large data sets, then maps and predicts equipment
degradation to deliver actionable insights to significantly reduce OPEX and
improve asset integrity management and plant reliability. The extracted data were
run through a series of conditioning steps for input into the EXACT software.
Once prepared, the tool was utilized to identify “good data” and “extreme data.”
Good data have been collected, cleansed, quality-checked and screened as
suitable for trending. Segregated or “extreme data” have been collected,
cleansed and quality-checked and have been identified as an outlier that might potentially
pose an immediate integrity threat or give cause for further investigation,
usually conducted via desktop review by a discipline engineer. The output
provided remaining lifespans of equipment and pipework while identifying
circuits and equipment items where inspection intervals could be extended, or
removed, from future inspection requirements, Fig. 3.
The detailed analysis optimized inspection scopes, recommending an approximately
30%-to-50% inspection reduction across all assets. To date, Imrandd has justified
removing up to 50% of inspection efforts across the late-life North Sea assets.
The result of the work and recommendations is expected to deliver additional
savings and will provide the basis for further reduction during phase 3, which
will define appropriate minimum allowable wall thickness (MAWT) for use in the
remaining life analysis, which is projected to bring yet further optimization
and removal of inspections scopes.
Reducing inspections and enhancing safety. In a further demonstration of harnessing data for optimized inspection
strategies, Imrandd was contracted by a separate global operator to deliver data
integrity management, to enable better inspection planning and scheduling,
without compromising on the safety of the assets in the short term. In support
of growing sustainability objectives, a reduction in carbon footprint and ESG
savings were also key objectives. The scope included cleansing and rationalizing
topsides piping systems and associated inspection data before implementing the
analytics process and using the results for optimization.
Although a large amount of inspection data existed, there was a backlog
in analysis of this data, which resulted in concern that some short-term
integrity threats would not have been addressed soon enough. To mitigate the
concerns over the volume of data, a staged approach was proposed. There was an
increased risk of equipment failure and unplanned shutdowns during the planning
phase, so the operator requested that analytics gathered and performed encompassed
both internal and external asset conditions.
The company’s data team collaborated with the operator to prioritize
systems and pipework for the first phase, with focus given to those due for
inspection within two years. A series of additional analyses to interpret and
further visualize the findings of the analytics was then conducted, with models
built to accommodate external condition information, Fig. 4. By applying
analytics and robust engineering methods to the cleansed data, the company’s experienced
engineers transformed the planning and scheduling of inspection and maintenance
up to CoP, stated for 2030, giving the operator confidence that they were
following a plan that was based on analyzed, demonstrable insights.
This analytics scope examined and trended data from a total of 3,500
lines in 182 corrosion circuits. This included more than 95,000 wall thickness
measurements arising from inspections for internal corrosion. Data from over
500 external inspection reports were also extracted and analyzed. The optimized
strategies resulted in a substantial reduction in the amount of inspection to
be performed up until CoP. This varied by asset, but in total resulted in a 26%
reduction in effort and spend on inspection related to piping. This represents
a cost-saving of £2.65 million. The project also delivered significant
sustainability benefits with an average saving of 26 tons of CO2 per
asset on a yearly basis. Further optimization, as new data become available is
expected to lead to additional cost-savings.
Seven of the assets were completed as part of the full integrity
management contract. This project has delivered a sound basis for the accurate
scheduling of inspection activity in the short term and has paved the way for
phase two, which will complete the analytics and give recommendations up to CoP
in 2030.
Supporting a data-driven future. Balancing the
challenges posed by late-life production requires innovation, and the digital evolution
of mature fields emerges as a key driver for the industry. Advanced
technologies, such as data analytics, artificial intelligence, and the IoT,
offer a lifeline for operators seeking to optimize production, predict
equipment failures, and streamline maintenance processes.
Legacy systems, disparate data sources, and evolving regulatory
requirements demand a balance between preserving historical data and
integrating cutting-edge technologies. However, recent examples underscore the
tangible impact of utilizing data for optimized inspection strategies, offering
a roadmap for cost-savings, sustainability benefits, and enhanced mechanical integrity.
As we look to the future, the significance of robust data management
strategies becomes increasingly apparent. The emphasis on data integrity, from
static stored data to real-time sensor readings, is paramount in enabling
data-driven decision-making. The challenges are vast, but the opportunities for
positive change are equally immense.
As the oil and gas industry grapples with the complexities of late-life
production, it is clear that the road ahead must be paved with innovative
technologies, strategic investments, and a steadfast commitment to data
excellence. Only through such concerted efforts can the industry not only
overcome its challenges but also emerge stronger and more resilient in the face
of a rapidly transforming energy landscape. WO
REFERENCE
STEVEN SAUNDERS is global head of new business at Imrandd. He has more than 26 years of industry experience, starting his career as a project engineer. He has held several asset assurance, senior inspection, lead integrity and asset integrity manager positions at Lloyds Register, Shell and BG Group. Prior to joining Imrandd, he was principal consultant—asset integrity management lead at Risktec Solutions, part of TUV Rhineland, based in the Middle East.