Posted tagged ‘Digital Oilfield’

Making new IT work for the business

September 23, 2011

I found an EXCELLENT article in the Digital Energy Journal by Dutch Holland. In this article he explore different strategies for transforming operational requirements into successful initiatives.

Without stealing too much of his well articulated article, the five approaches normally used are:

  • The by-the-book business analyst
  • The business-experienced analyst”
  • The businessman CIO
  • The IT expert Inside the business
  • The operations-led interface

I encourage anyone attempting to implement a operations-centric technological solution to read his article.

http://www.findingpetroleum.com/n/Making_new_IT_work_for_the_business/d1a1861b.aspx

“When trying to connect technology innovation with business, an intelligent interface between the two is required. It must be able to translate business opportunity into technical requirements; innovate, test and evaluate; and seamlessly implement new technology into the business.” ~Dutch Holland

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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.

Information Theory and Information Flow

January 30, 2011

(originally posted on blogspot January 28, 2010)

Information is the core, the root, of any business. But exactly what is information? Many will immediately begin explaining computer databases. But only a small portion of information theory is actually computer databases.

Information is a concrete substance in that it is a quantity that is sought, it is a quantity that can be sold, and it is a quantity that is protected.

Wikipedia’s definition: “Information is any kind of event that affects the state of a dynamical system. In its most restricted technical sense, it is an ordered sequence of symbols. As a concept, however, information has many meanings. Moreover, the concept of information is closely related to notions of constraint, communication, control, data, form, instruction, knowledge, meaning, mental stimulus, pattern, perception, and representation.” (http://en.wikipedia.org/wiki/Information)

Information Theory then is not the study of bits and bytes. It is the study of information. Moreover, the quantification of the information. And fundamental to Information Theory is the acquisition of information, along with the extraction of the true information from the extraneous. In Electrical Engineering, this process is addressed by signal conditioning and noise filtering. In the mathematical sciences (and specifically the probability sciences), the acquisition of information is the investigation into the probability of events and the correlation of events – both as simultaneous events and as cause-effect events. Process control looks to the acquisition of information to lead to more optimum control of the processes.

So the acquisition of a clear signal, the predictive nature of that information, and the utilization of that information is at the root of information theory.

C. E. Shannon published a paper in 1948 Mathematical Theory of Communication whicherved to introduce the concept of Information Theory to modern science. His tenet is that communicatioin systems (the means for dispersal of information) are composed of five parts:

  1. An information source (radio signal, DNA, industrial meter)
  2. A transmitter
  3. The channel (medium used to transmit)
  4. A receiver
  5. A recipient.

Since the information flow must be as distraction and noise free as possible, digital systems are often employed for industrial and parameterized data. Considerations then focus on data precision, latency, clarity, and storage.

Interestingly, the science of cryptography actually looks for obvuscation. Data purity, but hidden. Withing cryptograsphy, the need for precise, timely, and clear information is as important as ever, but the encapsulation of that information into shucks of meaningless dribble is the objective.

But then the scientist (as well as the code breaker) is attempting to achieve just the opposite: finding patterns, tendencies, and clues. These patterns, tendencies, and clues are the substance of the third pahse of the Data –> Information –> Knowledge –> Understanding –> Wisdom. And finding these patterns, tendencies, and clues is what provides the industrial information user his ability to improve performance and, as a result, profitability.

The Digital Oilfield is a prime example of the search for more and better information. As the product becomes harder to recover – shale gas, undersea petroleum, horizontal drilling, etc. – the importance of the ability to mine patterns, tendencies, and clues is magnified.

Hence the Digital Oilfield is both lagging in the awakening to the need for information and leading in the resources to uncap the information.

The Digital Oilfield, Part 2

January 30, 2011

(originally posted on blogspot January 18, 2010)

“The improved operational performance promised by a seamless digital oil field is alluring, and the tasks required to arrive at a realistic implementation are more specialized than might be expected.” (http://www.epmag.com/archives/digitalOilField/1936.htm)

Seamless integrated operations requires a systematic view of the entire exploration process. But the drilling operation may be the largest generator of diverse operational and performance data and may produce more downstream data information than any other process. Additionally, the drilling process is one of the most legally exposing processes performed in energy production – BP’s recent Gulf disaster is an excellent example.

The seamless, integrated, digital oilfield is data-centric. Data is at the start for the process, and data is at the end of the process. But data is not the objective. In fact, data is an impediment to information and knowledge. But data is the base of the information and knowledge tree – data begets information, information begets knowledge, knowledge begets wisdom. Bringing data up to the next level (information) or the subsequent level (knowledge) requires a systematic and root knowledge of the data available withing an organization, the data which should be available within an organization, and the meaning of that data.

Data mining is the overarching term used in many circles to define the process of developing information and knowledge. In particular, data mining is taking the data to the level of knowledge. Converting data to information is often no more complex that producing a pie chart or an x-y scatter chart. But that information requires extensive operational experience to analyze and understand. Data mining takes data into knowledge tier. Data mining will extract the tendencies of operational metrics to fortell an outcome.

Fortunately, there are several bright and shinning examples of entrepreneurs developing the data-to-knowledge conversion. One bright and promising star is Verdande’s DrillEdge product (http://www.verdandetechnology.com/products-a-services/drilledge.html). Although this blog does not support or advocate this technology as a matter of policy, this technology does illustrate an example of forward thinking and systematic data-to-knowledge development.

A second example is PetroLink’s modular data acquisition and processing model (http://www.petrolink.com). This product utilizes modular vendor-agnostic data accumulation tools (in particular interfacing to Pason and MD-TOTCO), modular data repositories, modular equation processing, and modular displays. All of this is accomplished through the WITSML standards (http://en.wikipedia.org/wiki/WITSML).

Future blogs will consider the movement of data, latency, reliability, and synchronization.

The Digital Oilfield, Part 1

January 30, 2011

(originally posted on blogspot January 17, 2010)

The oil business (or bidniz as the old-hands call it) has evolved in drilling, containment, control, and distribution. But the top-level system view has gone largely ignored. Certainly there are pockets of progress. And certainly there are several quality companies producing centralized data solutions. But even these solutions focus on the acquisition of the data while ignoring the reason for the data.

“Simply put Digital Energy or Digital Oilfields are about focusing information technology on the objectives of the petroleum business.” (www.istore.com, January 17, 2011)

Steve Hinchman, Marathon’s Senior VP of World Wide Production, in a speech to 2006 Digital Oil Conference says “Quality, timely information leads to better decisions and productivity gains.” and “Better decisions lead to better results, greater credibility, more opportunities, greater shareholder value.”

“Petroleum information technology (IT), digitized real-time downhole data and computer–aided practices are exploding, giving new impetus to the industry. The frustrations and hesitancy common in the 1990s are giving way to practical solutions and more widespread use by the oil industry. Better, cheaper and more secure data transmission through the Internet is one reason why.” (The Digital Oilfield, Oil and Gas Investor, 2004)

Future Digital Oilfield development will include efforts to integrate drilling data into its engineering and decision making. This integration consists of:

  1. Developing and integrating the acquisistion of data from all phases of the drilling operation. The currently dis-joint data will be brought together (historical and future) into a master data store architecture consisting of a Professional Petroleum Data Model (www.ppdm.org), various legacy commercial systems, and various internal custom data stores.
  2. Developing a systematic real-time data approach including data processing, analysis, proactive actioning, and integrated presentations. Such proactive, real-time processing includes collision avoidance, pay-zone tracking and analysis, and rig performance. Included is a new technology we a pushing for analytical analysis and recommendations for the best rig configuration and performance.
  3. Developing a systematic post-drill data analysis and centralized data recall for field analysis, offset well comparison, and new well engineering decisions. Central to this effort will include data analysis, data mining, and systematic data-centric decision making.

Watch the Digital Oilfield over the next few months as the requirements for better control, better prediction, and better decision making take a more significant center-stage.


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