Archive for May 2011

The Big Crew Change

May 17, 2011

“The Big Crew Change” is an approaching event within the oil and gas industry when the mantle of leadership will move from the “calculators and memos” generation to the “connected and Skype” generation. In a blog 4 years ago, Rembrandt observes:

“The retirement of the workforce in the industry is normally referred to as “the big crew change”. People in this sector normally retire at the age of 55. Since the average age of an employee working at a major oil company or service company is 46 to 49 years old, there will be a huge change in personnel in the coming ten years, hence the “big crew change”. This age distribution is a result of the oil crises in ‘70s and ‘80s as shown in chart 1 & 2 below. The rising oil price led to a significant increase in the inflow of petroleum geology students which waned as prices decreased.”

Furthermore, a Society of Petroleum Engineers study found:

“There are insufficient personnel or ‘mid-carrers’ between 30 and 45 with the experience to make autonomous decisions on critical projects across the key areas of our business: exploration, development and production. This fact slows the potential for a safe increase in production considerably”

A study undertaken by Texas Tech University make several points about the state of education and the employability of graduates during this crew change:

  • Employment levels at historic lows
  • 50% of current workers will retire in 6 years
  • Job prospects: ~100% placement for the past 12 years
  • Salaries: Highest major in engineering for new hires

The big challenge: Knowledge Harvesting. “The loss of experienced personnel combined with the influx of young employees is creating unprecedented knowledge retention and transfer problems that threaten companies’ capabilities for operational excellence, growth, and innovation.” (Case Study: Knowledge Harvesting During the Big Crew Change).

In a blog by Otto Plowman, “Retaining knowledge through the Big Crew Change”, we see that

“Finding a way to capture the knowledge of experienced employees is critical, to prevent “terminal leakage” of insight into decisions about operational processes, best practices, and so on. Using of optimization technology is one way that producers can capture and apply this knowledge.When the retiring workforce fail to convey the important (critical) lessons learned, the gap is filled by data warehouses, knowledge systems, adaptive intelligence, and innovation.”

When the retiring workforce fail to convey the important (critical) lessons learned, the gap is filled by data warehouses, knowledge systems, adaptive intelligence, and innovation. Perhaps the biggest challenge is innovation. Innovation will drive the industry through the next several years. Proactive intelligence, coupled with terabyte upon terabyte of data will form the basis.

The future: the nerds will take over from the wildcatter.

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Real-Time Data in an Operations/Process Environment

May 16, 2011

The operations/process environment differs from the administrative and financial environments in that operations is charged with getting the job done. As such, the requirements placed on computers, information systems, instrumentation, controls, and data is different too. Data is never ‘in balance’, data always carries uncertainty, and the process cannot stop. Operations personally have learned to perform their job while waiting for systems to come online, waiting for systems to upgrade, or even waiting for systems to be invented.

Once online, systems must be up 100% of the time, but aren’t. Systems must process data from a myriad of sources, but those sources are frequently intermit or sporadic. Thus the processing, utilization, storage, and analysis of real-time data is a challenge totally unlike the systems seen in administrations or financial operations.

Real time systems must address distinct channels of data flow – from the immediate to the analysis of terabytes of archived data.

Control and Supervision: Real-time data is used to provide direct HMI (human-machine-interface) and permit the human computer to monitor / control the operations from his console. The control and supervision phase of real-time data does not, as part of its function, record the data. (However, certain data logs may be created for legal or application development purposes.) Machine control and control feedback loops require, as a minimum, real-time data of sufficient quality to provide steady operational control.

Forensic Analysis and Lessons Learned: Captured data (and, to a lesser extent, data and event logs) are utilized to investigate specific performance metrics and operations issues. Generally, this data is kept in some form for posterity, but it may be filtered, processed, or purged. Nevertheless, the forensic utilization does represent post-operational analytics. Forensic analysis is also critical to prepare an operator for an upcoming similar process – similar in function, geography, or sequence.

Data Mining: Data mining is used to research previous operational events to locate trends, areas for improvement, and prepare for upcoming operations. Data mining is used identify a bottleneck or problem area as well as correlate events that are less than obvious.

Proactive / Predictive Analytics: The utilization of data streams, both present and previous, in an effort to predict the immediate (or distant) future requires historical data, data mining, and the application of learned correlations. Data mining may provide correlated events and properties, but the predictive analytics will provide the conversion of the correlations into positive, immediate performance and operational changes. (This utilization is not, explicitly AI, artificial intelligence, but the two are closely related)

The data-information-knowledge-understanding-wisdom paradigm: Within the data—>wisdom paradigm, real-time data is just that – data. The entire tree breaks out as:

  • data – raw, untempered data from the operations environment (elemental data filtering and data quality checks are, nevertheless, required).
  • information – presentation of the data in human comprehensible formats – the control and supervision phase of real-time data.
  • knowledge – forensic analytics, data mining, and correlation analysis
  • understanding – proactive and forward-looking changes in behavior characteristic of the proactive / predictive analytics phase.
  • wisdom – the wisdom phase remains the domain of the human computer.

Related Posts:

Data Mining and Data, Information, Understanding, Knowledge
https://profreynolds.wordpress.com/2011/01/30/data-mining-and-data-information-understanding-knowledge/

The Digital Oilfield, Part 1
https://profreynolds.wordpress.com/2011/01/30/the-digital-oilfield-part-1/

The Data-Information Hierarchy
https://profreynolds.wordpress.com/2011/01/31/the-data-information-hierarcy/


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