Jeffrey Maskell: Leveraging Your E&P Data Assets

Joseph

https://www.linkedin.com/pulse/l ... ets-jeffrey-maskell

Leveraging Your E&P Data Assets
Overwhelmed with Data?
Geo-scientists, completion engineers, and reservoir engineers have at least two things in common – they all work with data – and they all say:
“I want to quickly and easily find, access, and download quality-controlled data from a corporate repository that I can immediately use in my technical applications without preliminary data massage or manipulation and then just as quickly and easily upload my new, interpreted and analysed data back into the corporate repository.”
What these data users have actually just said is:
“I want a new way to work with my data – and not have to manage it!”
This at one point was described rather blithely as “the right data, to the right people, at the right time” but has since been revised and re-focused as “actionable intelligence, to the decision maker, when it is needed to support the business”.
Thus the shift from data to data analytics and intelligence that drives the business based on immediate and high quality data.
So, why is change in data management so hard?
Good Old-Fashioned Hard Work
One reason is that the professionalism and perseverance of hard-working geoscientists and engineers to “get the job done” lead them to develop work-around's that mask their frustrations about managing data.  Hence, to business leaders as long as “the job gets done” then any data users’ frustrations are considered regrettable but acceptable. Another is that the best geotechnical professionals are by definition non-linear thinkers, and given the opportunity to develop their own data flows, they will create processes that have the greatest benefit for themselves, not for the organization and its legacy of data value.
Can anything be done to help geoscientists and engineers spend their time working with the right data at the right time – and not managing it?
Opportunity Knocks
There is a solution to measure and quantify data management capability levels, which provides oil & gas companies a baseline comparison with their peers and identifies a road map to implement “quick wins”. It also allows the organization to measure quantitatively predict anticipated impacts on financial performance, finally achieving the goal of making data management tangibly relevant to the organization’s bottom line, more barrels of oil
A methodology or process, a solution, which is customized to each organization’s specific configuration, culture, geographic spread, and organizational complexity – and with higher value than other published systems in use today.
By selecting a subset from existing facets of both capability maturity and organizational complexity, weighted for surveyed and assessed priorities of the client organization, the consultant completes an initial capability assessment to provide the client with their present capability ranking among peers and a road map to achieve their desired future ranking, which is based on the industry standard business intelligence management maturity metrics matrix model (BIM5)
The key elements of the solution are:
An integrated survey, assessment, and deliverables package with a configurable set of weighted facets and peer selection that can be tuned to address each unique client situation.

• A specific road map and set of detailed recommendations customized for each client.
• A workflow that is repeatable in order to capture capability maturity over time, or to monitor and track continuous improvement. This allows an organization to adapt recommendations to changes resulting from organizational growth, entrance into new or unconventional plays, mergers and acquisitions, or shifting geographic focus.
  

Let’s let data managers manage data – so data users can focus on finding and producing more oil and gas.
Fundamental Challenges in Data Management include:

• The cost of data management for oil & gas companies is so small in comparison with other activities (e.g. wells drilling) that there is no cost limitation factor when it comes to data management. People, time and ‘not recognising what should be done’ are the main factors for not making a better use of the data.
• It is very difficult to find links between the quality of data management and the company’s gains and losses.
• Senior managers do not pay attention to data management, as nobody is coming to them and say ‘do you realise that we are losing money as a result of poor data management’? Also – managers do not like when you come to them with problems. So if you come with such a statement, they expect you to have a ready solution.
• When it comes to proposing data management improvements, it is always challenging to quantify the benefits and to justify them.
• There are not enough documented business cases that clearly show that company losses are directly related to poor data management. Lack of evidence makes it difficult to recognise a business case for a company to make a change.
• The direct benefits generated by robust data management systems are: better staff utilization and risk minimization. For example – if staff responsible for interpreting subsurface data can concentrate directly on their tasks rather than spending 20-40% of their time on looking for data, that makes a solid business case. As an interpreter gets data quicker, he can spend more time on analysis, and the quality of his interpretation will increase (added value).
  

When asked ‘What is your company doing to preserve and realise the value of their data?’ the following typical observations and comments are made:

• There are many data managers that expect business to tell them what is needed, while what the business expects from the data management teams is to propose new solutions. Another encountered problem was so called ‘human factor’: the resistance of some data managers to introduce any changes.
• There is an evident lack of a systematic approach to the data management throughout the project. There are people in the company obsessed with some bits of data at certain time, but when the project moves to the next stage, they do not care what is happening with it (for example – subsurface geologists are strongly after subsurface data, but once the field is acquired, the data management is passed to somebody else).
• The important thing is to make the status of data visible. For example: if the well testing data is not received on a regular basis, make that information visible to the management.
  

Outsourcing
Outsourcing remains controversial aspect of data management; the costs benefits ratios have changed over time the impact of which no longer makes this a “no brainer”. Complications of time zones, language issues and a degrading of in-house expertise open up significant arguments.

• A company can outsource data management, but it cannot outsource its responsibilities.
• Generally, the responsibilities of the contracted company are limited to data loading and QC of data – the folks are not a part of the team, so they cannot act proactively, for example, say: this is the way you can improve your data management.
• When outsourcing, nobody is looking to derive more value, everybody is looking to reduce the cost.
• The key issue is the integration with the business. Can the subcontractor for outsourcing recognise the organization’s needs?
• Some data managers are extremely reluctant to share ‘their’ data with ‘others’.
  

Conclusions:
After decades of trying the issue of data management and timely distribution, publication of that data to the end user community, is as problematic today as it has been over more than 20 years. The focus on integrated data management as a complete and key part of the overall business operations remains critical and can when implemented correctly bring significant benefits in terms of productivity, competitive advantage and in cost reduction. All these today are relevant in a $30 dollar a barrel world.
Data Analytics is part of that process, innovation; disruptive technologies only function and deliver enhanced capabilities when data is readily available.
The key is; data of quality, data that is relevant, data that drives the business process and delivers the key results that enable effective planning, extending the Life of a Field, enabling early access to data for Well Planning or addressing Well Production and Forecasting. All these factors and more can be so much more productive, when a consistent policy towards data management is in position.

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