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Business Challenges

Digital Oilfield technology is hampered by incompatible data formats, preventing it from being fully implemented and realizing the value to solve E&P business challenges.

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The ultimate goal of the E&P industry is to provide energy resources to the world. Some of the key challenges that E&P businesses face include:

  • Increased pressure for operators to maximize recovery and replace reserves in the face of still-strong worldwide energy demand forecasts.
  • Huge growth in expenses, uncertainty, and risk due to remote geographic locations and technically complex and unconventional plays.
  • Urgent need to bridge workforce shortages and experience gaps with technology solutions as senior petro-technical professionals retire in record numbers and a new, less-experienced work force climbs the learning curve.
  • Exponential growth in the volume of available data and difficulty in managing and using all that data to realize value.

Digital oilfield Challenges

Digital Oilfield (DOF) technology and solutions—the broad range of “intelligent” field instrumentation, equipment and computer hardware and software implemented along the E&P value chain—has the potential to help businesses address these challenges.

However, a key barrier to full implementation and realization of DOF value is incompatible data formats. The various technologies and systems along the value chain are proprietary solutions made by different vendors, service companies, and operators with different, often proprietary, data formats. Also, different systems used by multiple stakeholders in a project—service companies, operators, partners, and regulatory agencies—use different data formats.

The need to reformat data as it moves along the value chain or between stakeholders is inefficient, error-prone, and costly.

To find out how to overcome these challenges, see Solution: Standards.



According to a survey from Pitney Bowes Business Insight, companies have worked for years to improve data quality yet find it challenging to maintain accurate information and struggle to quantify the true business loss cost of that quality. According to the survey, one-third of survey respondents rate their data quality as poor at best, while only 4% rat it as excellent.


Working around poor data quality can be devastating to productivity and operational expenses. In an industry where the data volume is massive, it is critical to have standards in order to make decisions based on accurate, consistent data. Additionally, Standards eliminate the need to translate data into other formats, which reduces errors and increases efficiencies.