"Go Ask That Guy” is Not a Data Management Plan

INDUSTRY - Oil and Gas, PRACTICES - Upstream

Imagine this scenario. You are a young petroleum engineer in your second assignment with a fast growing, mid-sized oil and gas operator. You have just been transferred to the Eagle Ford asset team after your initial assignment on a Gulf of Mexico deep-water capital project as a reservoir engineer. In short, you have a lot to do and many unanswered questions about fracturing techniques, completions programs and the production response from the new wells.

Not knowing where to start, you turn to the drilling and production databases you learned how to use in your offshore assignment. Unfortunately, you learn that the onshore shale group uses a different system of record and none of the wells in the Eagle Ford play are in the database you were taught how to use.

Next, you default to the good ‘ol “go ask that guy” method for locating key production and drilling data silos. It turns out the senior engineers, Paul and Fred, you ask are helpful ─ but their attention is fleeting because of project demands. Later you learn that neither of them have had the chance to update the database and the information you need is on the hard drives of the engineers and operators in the field.

The moral of the story is having the right network and relationships is not a substitute for a good data management plan. Industry surveys tell us that engineers can spend up to 50% of their time looking for data, ensuring it is the right version and making changes in data formats for analytics and modeling routines.

While your company probably has a few good resources like Paul and Fred, there is a better way ─ a good data management plan that develops a roadmap, architecture, and organization to support data management requirements. A successful data management plan has business, technical, data and governance framework tracks.

Imagine this scenario. You are a young petroleum engineer in your second assignment with a fast growing, mid-sized oil and gas operator. You have just been transferred to the Eagle Ford asset team after your initial assignment on a Gulf of Mexico deep-water capital project as a reservoir engineer. In short, you have a lot to do and many unanswered questions about fracturing techniques, completions programs and the production response from the new wells.

Not knowing where to start, you turn to the drilling and production databases you learned how to use in your offshore assignment. Unfortunately, you learn that the onshore shale group uses a different system of record and none of the wells in the Eagle Ford play are in the database you were taught how to use.

Next, you default to the good ‘ol “go ask that guy” method for locating key production and drilling data silos. It turns out the senior engineers, Paul and Fred, you ask are helpful ─ but their attention is fleeting because of project demands. Later you learn that neither of them have had the chance to update the database and the information you need is on the hard drives of the engineers and operators in the field. The moral of the story is having the right network and relationships is not a substitute for a good data management plan. Industry surveys tell us that engineers can spend up to 50% of their time looking for data, ensuring it is the right version and making changes in data formats for analytics and modeling routines.

While your company probably has a few good resources like Paul and Fred, there is a better way ─ a good data management plan that develops a roadmap, architecture, and organization to support data management requirements. A successful data management plan has business, technical, data and governance framework tracks.

 

Business Track
- Consists of interviews with key client individuals to understand the value of information, issues and opportunities related to data, priorities, and business benefits
- Business value assessment is performed and presented to allow for more quantitative values

Technical Track
- Conceptual reference architecture is aligned with clients’ objectives and future state direction
- Current physical architecture is reviewed to identify gaps that must be resolved in order to implement the future state architecture.

Data Track
- Data Sources, Systems of Records (SoRs), and Authoritative Source analyses are performed
- Data Supply & Integration current status is validated against the desired Data Architecture
- Unstructured and existing document sources and workflows are evaluated
- Metadata analysis for completeness and extensiveness of lineage information is performed
- Data profiling process is executed to identify data quality trends and issues at a high-level

Organization / Governance Framework Track
- Focus on Information Governance and definition, ownership and stewardship of data assets that need to be managed

 

Retaining corporate knowledge is one of the most noted pain points cited by companies facing the loss of time searching for information during the Big Crew Change, general turnover and reassignments. The more important that data is to the engineering, earth science and operations staff, the more important that a good data management plan becomes.

Just imagine how this story would go with a good data management plan in place. The young engineer gets his new assignment and as part of the transition, he gets a copy of the data management plan for the Eagle Ford asset team along with a short training session on how to use the new databases involved. Instead of running around trying to find who to ask, he goes right to the most up-to-date sources and begins his data gathering. He cuts weeks, if not months, of time getting to the analysis of best practices in fracturing and completions designs. And he also has confidence in his new role and in the data.

After analysis, he takes some ideas to the senior completions engineer and after some valuable advice on how to execute the new fracturing design, the new engineer is put in touch with the service company that has been hired to do the work on the 2015 drilling program. That service company also has a good data management plan and they are able to search their database to get best practices that their company recommends based on work with many operators.

Together they modify their ideas, develop a new approach that avoids the production problems from this year’s drilling program and the company starts off the 2015 drilling season with a much more successful completions program. Production is up 25% from the 2014 average and time spent in the fracking stage is reduced. The value to the company can be measured in several millions of dollars. One of the key enablers of that improvement is a good data management plan and the productivity and insight that the engineering and operations staff gain from it.

One thing a good data management plan does is allows success to spread. Imagine how success can spread across each capital project team when capturing and managing corporate knowledge is automated and governed. Noah Consulting helps oil and gas companies build a data management strategy and execution program so that rather than looking for data, young and senior engineers can get back to solving operations problems, creating competitive advantage and improving performance optimization.


 

About the Author: Jim Crompton

Jim Crompton is a distinguished leader in E&P data management, spending the last 37 years at Chevron and stacking up a truly impressive list of accomplishments. One of the many highlights of Jim’s work was leading one of the largest standardization projects at Chevron. He also led a study team for the IT Merger Integration Organization as part of the Chevron & Texaco merger. For this, he received a President's Award. In 2002, Jim was named a Chevron Fellow in acknowledgement of his contributions and he served as the chair of the Fellows Network from 2006-2008. Outside of his work for Chevron, Jim was elected chair of general committee for Petroleum Industry Data Exchange (PIDX. In this role, he was able to influence the direction of the standards setting activities towards emerging technologies, such as XML, and advanced electronic business models to complement the established EDI practices in the industry. He was also selected to participate in the SPE Distinguished Lecturer Program for 2009-2010. Jim resides in Colorado, with his wife, and enjoys writing.

 

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