By Emmanuel Udeh, Subsurface Data Analyst at Shell

For any large business to remain competitive, it needs the ability to make accurate and timely decisions. This depends heavily on good quality and reliable data. In the Oil & Gas industry there are imperative reasons for this:

  • There are huge safety and environmental implications of taking the wrong decisions (as witnessed in the 2010 BP oil spill off the Gulf of Mexico);
  • The industry is very diverse, with several business units all having various unique strategic needs and requirements
  • Given the volatility of today’s economy, most major Oil & Gas companies seek to optimize and balance their business models in the most cost effective manner, whether by reducing the level of investment required in the Exploration & Production of new oil, by maximizing production through a tailored and fit-for-purpose Wells, Reservoir and Facilities Management (WRFM) plan, or by determining if continued improvement and investment in equipment is required to extend plant life. All these efforts to improve the bottom line and deliver business value can only happen when decisions are based on accurate data.

Accurate data implies high data quality. This is one of many data management challenges faced by the Oil & Gas industry, as well as many other businesses such as healthcare, utilities and finance. According to the Data Management Body of Knowledge (DAMA-DMBOK Guide), Data Quality Management (DQM) is a critical support process in organizational change management. Data quality is synonymous with information quality, since poor data quality results in inaccurate information and poor business performance. Many organisations approach data quality problems by employing data cleansing methodologies, resulting in short-term and costly improvements that have little effect in the long run, as they do not address the root causes of data defects.

For any data set to be qualified as being of good quality, it must possess some essential characteristics. Although several publications differ on the number, almost all align on the following key characteristics:

  •  Accuracy: Data accuracy measures the degree to which data correctly reflects the realities of the conditions modelled. The Exploration & Production (E&P) business is highly reliant on accurate data because of the high risks involved. For instance, planning a well using inaccurate formation pore pressure data could result in serious complications – formation fracture or borehole stability – which could lead to fatalities and cause significant damage to equipment and the environment while drilling the well.
  • Completeness: One approach to completeness is to ensure that certain attributes always have assigned values in a data set. Another approach is to ensure all appropriate rows in a data set are present. For instance, a complete list is required in a tabular form of all marker top and fluid fill predictions for all zones to be penetrated (or avoided!). This table is expected to include both vertical and lateral uncertainty, clearly highlighting areas of geologic concern such as fault zones, loss zones, and so on.
  • Consistency: Quality data must be consistent across the board, such that data set values or naming conventions must be uniform wherever such data sets occur. This effectively ensures that data values in one data set are consistent with values in another data set. The expectation is that similar data values drawn from separate data sets must not conflict with each other.
  • Timeliness: This refers to the degree to which information is current with the environment that it models. This measures how fresh the data is, as well as its correctness in the face of possible time-related changes, which is particularly essential in the Oil & Gas industry. An example of this is where Integrated Production Simulation Models (IPSM) are run using old or stale field production forecasts that do not reflect the changes to the reservoir since the forecast was first made, especially where current production data is available.

Data quality-related problems such as those indicated above can cost E&P companies millions of dollars annually. Revenue opportunities are lost as a result of the inability to make strategic business decisions in a timely manner. To solve data quality problems and achieve the key characteristics above, it is necessary to address the root causes of data quality issues.

Data quality issues can be caused by various factors, but data entry processes are arguably the Number 1 culprit. While it is difficult to achieve 100% data quality and eliminate all data errors, it is possible to implement several processes or procedures that can help to mitigate and improve the quality of information and data assets in organizations:










  • Implementing a data quality programme is the first step to delivering high quality data. Data quality programs seek to provide a structured approach for identifying and mitigating data quality problems. This approach allows every business within the Oil & Gas industry to standardize, implement, assure, monitor and audit their processes and procedures. These are used when collecting, storing and analyzing the organization’s information and data assets. For any data quality programme to succeed, it is imperative that the programme gets the support of top management.
  • Automation of systems and processes is another way of preventing data quality issues. This can be part of a data quality program or a standalone ERP deployment. There are several proprietary software and application packages that help to monitor data quality on corporate data stores. These ERP solutions can also perform quality checks and flag defects for appropriate action. Automation helps to reduce quality defects from data entry processes or during data conversion procedures, while also monitoring change logs to prevent errors from sudden unexpected changes to source systems.
  • However, data quality programmes, together with the implementation of automated systems and processes, are not enough to prevent data defects. There needs to be a step change in employee attitudes and organizational culture towards the optimization of business processes. This is necessary because most people see data management as additional or peripheral to their day-to-day work. To resolve this will involve educating, training and initiating a reward system for data quality champions within the business, so that everyone gets to understand that quality data is their personal priority not just that of the Data Management department.

A 2011 study by Gartner Inc identified that:

  • Poor data quality is a primary reason for 40% of all business initiatives failing to achieve their targeted benefits;
  • Data quality affects overall labour productivity by as much as 20%;
  • As more business processes become automated, data quality becomes the limiting factor for overall process quality.

Oil & Gas companies which employ these strategies can leverage high quality data to deliver on a range of business initiatives such as:

  • Increasing efficiency in exploring, developing and producing new hydrocarbon resources. This will largely reduce time and cost, thus improving the corporate bottom-line (more profit to the business);
  • Reducing the time required for data reconciliation and cleansing. According to the Aberdeen Group , this could translate to an average savings of five million man-hours for an average company with 6.2million records.

Most importantly, implementing a data quality programme ensures higher quality data, translating to a single source of truth. This results in greater corporate confidence during analysis, planning and decision-making enhancing business integrity and value.

Further reading

Achieving Business Success through a Commitment to High Quality Data TDWI REPORT SERIES by Wayne W. Eckerson; Measuring the Business Value of Data Quality October 2011 by GARTNER; The DAMA Guide to The Data Management Body of Knowledge (DAMA-DMBOK Guide), First Edition 2010; The Big Data Imperative, Why Information Governance Must Be Addressed Now by Nathanie Rowe of Aberdeen Group 2012; Data management trends in oil and gas by Soubhi Chebib 2011.

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