Henrique Paula, of ABS, explains how data analytics can be used to improve safety, environmental protection, operational efficiency, and profitability in emerging energy markets such as Mexico.
One of the most promising and legitimate ideas to emerge from the hype around “Big Data” and the “Internet of Things” is data analytics, the science of examining data to inform and improve business and technical decisions. Data analytics has been transforming other industries, and now it is beginning to transform the offshore sector by improving safety, environmental protection, operational efficiency, and profitability, with applications ranging from basic equipment optimization to enterprise-wide fleet and asset performance.
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Data analytics is a way to move from data to actionable knowledge. Courtesy of ABS. |
Defining the terms
Although they are related, data analytics and big data are different. Big data is a broad term for data sets so large or complex that traditional processing applications are inadequate. Gartner, the IT research and advisory company, defines big data as high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision-making.
Data analytics is the science of examining data with the purpose of drawing conclusions about the information. It is applied in many industries to allow companies to make better business decisions, and it is used in the sciences to verify or disprove existing models or theories.
In a developing region like the Mexican Gulf of Mexico, data analytics has the potential to make an enormous impact.
Mexico’s emerging offshore
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The upsurge of data analytics comes from four contemporaneous trends in sensor, communication, storage and processing capabilities. Courtesy of Energective LLC. |
According to Clarksons Research, while Mexico is developing only one offshore gas field at present, there are 67 offshore oil fields in development, and there are many more fields with development potential, including nine gas fields and 39 oil fields.
Offshore oil production has seen a steady decline over the last 10 years, standing at slightly more than 1.7 MMb/d at the end of 2015. Analysts predict production will drop by 1% year on year in 2016 due to declining oil supply from maturing fields, including the shallow-water Cantarell Complex. Mexico’s gas production also is falling. The country ended 2015 with approximately 4.2 bcf/d of gas production from 51 fields in production.
Great efforts have gone into field delineation, and new technologies have been implemented to improve production volumes. In the face of progressively falling output, leveraging data from these assets could provide asset owners with a valuable tool that could be put to use to improve production.
This is particularly important at present because Mexico’s 2015 licensing program saw only two blocks awarded in the first stage of bidding in Round 1 in July and five of nine fields awarded in September’s second stage. It will be vital for Mexico to get the greatest production from its producing fields until the country is successful in enticing international investors to the region.
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There are seven steps to follow to achieve successful data analytics. |
The potential for data analytics
Data analytics is being used through most of the lifecycle of offshore activities. During seismic and reservoir characterization studies, data sources with 3D seismic data, well logs and faults, are integrated and analyzed to support decisions related to achieving key targets in flow assurance, field optimization, drilling performance, well categorization and so forth. Benefits range from attaining optimal reservoir exploitation rate to forecasting the decline of new wells.
For fixed, floating and subsea assets, data analytics starts with collecting data at the asset level, including operating parameters, equipment status, structural stresses and environmental data. For moving assets such as offshore support vessels and dynamic positioning floaters/vessels, data collection can also include location, direction and speed.
These data sets are transmitted securely by satellite to a storage/processing center, which can offer services such as performance optimization, asset tracking and structural integrity monitoring. Significantly more value can be realized by applying data analytics to an entire fleet, where the objective goes beyond optimizing the performance of individual assets to optimizing every unit in the fleet.
Offshore and subsea assets are remote and isolated most of their operating life. Collecting and analyzing more data while an asset is operating can provide more knowledge, improved planning, and reduced maintenance down time – previously unknown conditions generally are more costly and take longer to repair.
In many of these applications, data from disparate, multifarious sources is analyzed to secure new macro-level insights. Typically, an enterprise organizes data across many systems and applications, and data analytics includes techniques for combining different data sources, sometimes significant in volume, and analyzing them in innovative ways to create new insights and knowledge. The key to getting the best value out of data analytics is performing analysis on the “right data” – data appropriate to a particular problem or opportunity – to deliver actionable knowledge or insight.
The upsurge of data analytics comes from four contemporaneous trends in sensor, communication, storage and processing capabilities. There has been a proliferation of small, intelligent sensors that measure changes in physical attributes and transmit the resulting data through extensive, easily accessible, and fast wide area communication networks. The data can be stored in massive data centers, and subsequently processed and analyzed by extremely powerful computers and processors to deliver critical insights. Those insights can then be acted upon and facilitated by computer-aided interventions. A key point is that today all this can be done at a relatively low cost.
ABS and its affiliated companies are researching the applications of data analytics and data management to the oil and gas industry. While data analytics has the potential to improve production and profitability, it also can improve the classification process. One of the primary objectives of this research is to find ways for data analytics to be applied to the classification process, allowing it to become continuous, more focused, less intrusive, and more efficient, bringing about a shift from the current calendar-based inspection process to a more condition-based process. Defined as FutureClass, this approach could transform the classification process and change the entire concept of inspection and classification.
Dr. Henrique Paula has 37 years of engineering experience with expertise in oil and gas regulatory regimes, integrity management, risk and safety management, process safety management, risk and reliability analyses and project quality management. He has provided consulting and training services in more than 30 countries and authored/co-authored more than 100 documents, including journal articles, conference papers and technical reports. He holds a PhD from the University of Tennessee.
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