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October 25, 2016

Why Hire a Consultant?

Like many people, I have a suspicious nature about consultants. Unlike many people, I have these feelings even though I am one. To be clear however, I have only worked for a consultancy for a little over four years; the rest of my career was spent on the other side of the desk. So, even though I work as a consultant, why do I remain suspicious of them? Probably because of some encounters earlier in my career. There was the newly minted MBA from a major consulting firm with no previous work experience, not even a summer job, who came into my office (at the direction of corporate headquarters) and told me she would exponentially improve my department's efficiency. She only needed us to give her access to everyone for two weeks because she had a foolproof methodology. Or the consultancy who lands two people to provide executive advisory services and six months later you have an infestation. There's a reason for the saying that a consultant is someone who borrows your watch and then charges you to tell you the time.

That being said, I began to change my negative outlook towards consultants about four years before I became one. Or I should say, I began to change my outlook towards some consultants, because I still view some with suspicion. There are times when hiring a consultant...the right consultant... is not only valuable, it's a necessity.

The right consultant is someone who understands your business, the challenges you face, and the spectrum of possible solutions to those business problems. Someone who wants to keep working for you, and hopes to do so not because they're just trying to sell resources, but because there is value in what they bring and you have more problems that need solving.

Let's look at some situations when this is the case.

Been there...done that. Nothing is a bigger waste of time and money than reinventing the wheel. If a problem needs to be solved and there's someone out there who's already solved it, take advantage of that. Not only will you get your problem solved, you'll probably end up with a better solution than the previous time he/she solved it. Even the best process has an aspect of "if I had the chance to do that again, this is what I would do differently". Be the recipient of the consultants' continuous improvement of the solution.

Skills gap. You may have a need for a particular capability that your team does not possess and you don't see a long term need for that skill. It doesn't make sense to increase your headcount, and it's not fair to an employee to bring them on knowing that they won't be there long. This is a good time to look at other staffing options, turning to experienced people in a staff augmentation role. Not only are you able to develop this solution, but you've identified a resource you can turn to when needed without staffing up, thereby expanding your capabilities yet keeping your flexibility. In many cases, an additional benefit is that some of your staff learn or improve capabilities during the time they spend working with the consultant, a nice bonus that the consultant leaves behind.

Lack of bench strength. I speak with people at companies all the time who, when asked how they're doing, respond with the fact that they have too few people and too much work. Even with the vast amount of available talent, companies such as those in the Energy sector are not going to increase their baseline costs by taking on more staff until there are higher, stable commodity prices. Being able to turn to consultancies with qualified individuals who will be there long enough to accomplish the task and then go away give the hiring companies a flexible way to keep costs down yet still improve their capabilities.

An apolitical view. Anyone who's been in corporate America (or corporate Canada or corporate wherever) long enough has seen business problems screaming for a solution remain unaddressed due to internal politics. There may be multiple options for a solution, each backed by a significant player, or players, in the company. Or one person's suggestion may be dismissed because they're perceived as empire building. This is the perfect situation for using an external resource who is not aligned with any side, understands the problem as well as the options and is a skilled facilitator. This impartial perspective can help the company agree on a way forward. Not to mention, there are times when an outside party's perspective carries more weight than one from an internal source. Of course, the outside parties themselves must have a neutral perspective. If you bring in someone who sells both services and software, there's a pretty good chance the proposed solution will include the use of their software.

Multiple perspectives. If a consultant is good, they've most likely had the opportunity to work with a number of companies. It doesn't matter if a company is a small independent company or a large multinational corporation, the problems they face are very similar. The solutions will vary, but this multi-perspective experience only adds to the value the consultant brings. Without putting confidentiality agreements at risk, they'll know what works and what doesn't, and under which circumstances success can be found (or lost).

Finding a consultant who not only talks the talk but has walked the walk can bring significant value to your company. The trick is finding the right consultant for the right reason...and the right one should be asking, "What business problem are we looking to solve?"


October 18, 2016

Re-Imagining Data Management in Oil and Gas

Technology advances enable oil and gas companies the ability to unlock reserves at lower costs, at greater depths and in more remote locations, but currently the oil and natural gas industry is in one of the deepest downturns since the 1980's. Prices have dropped over 70% since mid-2014, resulting in the expected decline in investment and drilling activity. The industry has reached the bottom of the cycle with slowly increasing oil prices and a few more rigs going back to work, but this downturn has the characteristics for a "lower for much longer" scenario. With prices this low, the industry is taking a hard look at every aspect of their business including how they can use their collected data to improve operational efficiency and increase profitability.

Every company must get more out of the data they are collecting. Maximizing efficiency is essential to lower production costs in complex oil developments (deeper subsea/subsurface, higher pressures and temperatures, remote operations, etc.), not to mention the increasing volume and velocity of data from the Internet of Things (IoT). The value of information in the oil field has been proven but we seek new ways to use it to increase insight into operations and complex reservoirs to make better decisions which result in increased productivity and profitability.

Driving the new information trends are: digital intensity (increase in number and variety of sensors, field automation, smart equipment, increase in documents, increase in size of seismic surveys and reservoir models) and interconnected devices (remote decision support centers, remote control of processes, decrease in the use of proprietary networks and growth of internet, plus connected supply chains).

Operations Technology (OT) is emerging as a steward for engineering applications, operations and field automation (SCADA) systems. This area is rarely a corporate department. COOs in the oil and gas industry are usually assigned to a business unit or assets in a local geographic area. The growth of OT is happening from the "ground up", so to speak. Some companies have field automation standards but with legacy properties and many mergers and asset acquisitions, there is a complex diversity of solutions found in the field. This community is usually driven by local champions and operational teams.

The connection between OT and corporate IT has traditionally not been very formal or visible, but they do have a number of common issues such as: telecommunications, protocols, data access, architecture, mobility, and cybersecurity. Often these groups are struggling to find common solutions for patch management, upgrade and version changes and ways to bring data to engineering teams.

All these advances are going to make life interesting for the parties involved. Many advances in the Industrial Internet of Things favors OT over traditional IT, but all the data needs to ride on a common ICT backbone. With the increasing number of interconnections, a total security solution is needed. In order for sensor and machine data to match with transactions, documents and structured data, data management solutions must mature. The current tensions and often separation between OT and IT have to evolve into converged approaches. It is time to make friends, not enemies, in order to enable the digital oilfield.

But is our data foundation ready to enable provide accurate guidance?

There is no question that we need to re-think and re-imagine the role of data management in oil and gas to meet these challenges, but we cannot ignore the elephant in the room - data quality and data governance! The subject isn't popular and no one wants to talk about it because it is difficult to accomplish though the principles are well understood. One of the problems is that data management and information strategies are not highly regarded or said more plainly - these projects don't win recognition or promotions so are considered low priority and are last to receive resources.

However, a poor data management foundation, ineffective data governance processes and lack of alignment between engineering, operations and IT present barriers to the adoption of workflow optimization and advanced analytics solutions.

Data is often considered a personal or asset-specific possession and not a corporate asset. There are precious few Chief Data Officers and the lack of a coordinated strategy is reflected in the amount of data kept in informal spreadsheets and on shared drives.  Add to the equation the strong belief that standards hinder innovation, the internal IT department doesn't get what the engineering, earth science and operations groups are trying to do and many technical experts prefer customization to standardization. Finally, there those who think the promise of new emerging technology will eliminate the need for the hard work required to develop a robust data foundation and effective data governance framework.

In some corporate cultures, line management is often supportive of mavericks who operate outside enterprise standards because of a belief that the engineer is more productive doing it their way rather than being restricted to consensus best practices. Application rationalization and agreement on a company standards computing or data platform has proven difficult to achieve despite the obvious cost reduction benefits of supporting only a restricted standardized portfolio of tools and a trusted single source of data. Support for standards, either industry or company, often takes a back seat to customization and personal preferences.

Does this leave a company in an either/or situation? Either you let mavericks have non-standard pigeon holes of data in spreadsheets, or institute a police state of data quality management? Where does trusted data live and who owns it? Or is there another option?

If companies do not want to do the required work of data management but only want end results, there is a way to develop an effective data foundation using emerging digital technologies. This data-as-a-service platform would fill the slot currently role that should be played by internal company data management practices. But if internal efforts prove an insurmountable challenge, then starting again with an external alternative just may get the industry digital oilfield back on track.

We'll explore more about the data-as-a-service options in a later post.

Is it time to re-imagine the way you manage data? 

October 4, 2016

Oil and Gas Meets Big Data

Oil and gas companies are familiar with the concept of Big Data but have not adequately addressed the volume, variety and velocity being generated nor capitalized value it could add to their organizations. Significant competitive advantage will be found by companies who learn to efficiently use their daily petabytes of data to identify trends and anomalies for timely, accurate and enterprise-wide decision making. Companies that harnesses the power of their data will discover a level of efficiency previously unobtainable. The key is to manage Big Data to interpret, react and predict the best course of action. It is not the company with the most data that wins, it is the company that uses their data on which to base their decisions.

The oil and gas industry has been severely challenged by the prolonged steep drop in market prices which led to streamlining of most operational processes.  How quickly and accurately an enterprise made course corrections is a large determining factor in its overall viability and long term value. The perception is that the next level of profitability, without additional large expenditures and headcount, will be obtained by optimizing incoming data. 

A well-managed Big Data program will also have company-wide benefits including increased productivity as teams will know where their data is stored, how to access it efficiently and won't waste time looking for and recreating data. There will be less delay between field and C-suite resulting in better alignment and understanding of the current state. Big Data's true value is realized when organizations use new statistical models and patterns identified by machine learning algorithms and take action on the results. Or said another way - analytics.

We've collected it - now how do we use it?

A substantial amount of data being generated is never utilized because companies don't have a comprehensive Big Data management plan and infrastructure.  One estimate suggests that corporations only process 20% of the data they collect. Another survey found that an operator only uses 5% of the data collected on an offshore drilling rig due to data storage, transmission and commercial constraints. Infrastructure goes beyond data storage, and is the capacity to warehouse, search and model data.

There are both technological and organizational issues to consider when moving to operationalize analytics. Technology is evolving to include embedding and integrating analytics into dashboards, databases, devices, systems, applications, processes and more. The organizational implications will need to be addressed, as well, such as who owns the data?  The success or failure of any Big Data and Analytics project is based on user adoption.  If the platform/program makes the data available, trusted and easy to use, the program will be successful.

Big Data requirements also depends on focus - be it by company or department. For example, an exploration group may find an advantage in evaluating new plays and understanding how their experience will transfer from field-to-field. A production group might focus on maximizing output, reducing risk while tracking and optimizing scheduled maintenance for decreasing down-time or non-productive time.


While it might sound intimidating, a Big Data infrastructure stack will be needed with an architectural design that introduces the analytics platform into the computing and processing ecosystem. Keep in mind, however, this technology doesn't replace legacy data management and processing environments, it complements them. Once analytics are running and real-time data starts streaming in, data should be evaluated against historical data to determine whether it's within anticipated bounds.

Emerging developments in Big Data technologies are proving essential to capture, store and process data that will provide a foundation for the real work. There are a number of ways to evaluate requirements to manage this data, but one approach is to categorize it into three attributes: volume, velocity and variety.


The volume increase isn't surprising, however, the amount is staggering. Just looking at the amount of seismic and well log data significant growth is due to larger surveys, more channels in acquisition methods and closer sample intervals (in time and space). Data gathered from actual drilling and logging activity can also be measured in smaller increments thanks to tools now available (including fiber optic cables), boosting the overall amount of data. The amount of data gathered from production activity has also increased due to placement of sensors and telecommunications networks that relay information to the operator on a real-time basis. Multiply the variety of sensors being deployed - (including downhole pressure, temperature and vibration gauges, acoustic, electromagnetic and flowmeters), by the number of wells - and well, there's your Big Data.

There are existing solutions for data volume challenges from data appliances, in-memory devices, unified storage grids and high performance computing grids (Linux based, using GPUs instead of CPUs and high-speed interconnect solutions with distributed computing techniques).


The new Big Data infrastructures are capable of ingesting large volumes of sensor data at high velocity. Information architects will have to be able to combine master and meta-data with tag names from historians in line with the governance in place. As more data is being collected in the field through some form of process control networks (including SCADA or DCS systems), more real-time data is available for operators and engineers. Collecting and analyzing the data in order to make better decisions, reduce downtime, increase production and optimize operations is most effective with a strong strategic plan that is executed by a collaboration between operations and IT staff.


Traditional Business Intelligence solutions have been developed for reporting and tracking pre-defined metrics from structured data from one type of data (e.g., management scorecards). The future holds new insights available from exploring all the relevant data types: structured, semi-structured, documents, transactions, field measurements. Add in new types of data from email, text, video and social media and the variety challenge becomes more apparent. The data volume is growing but you can't afford to get lost in the datasets.


Here's a snapshot of a possible future - operators and service companies sharing data to create a holistic view of an entire operation. Analysis will begin with individual wells, then by development fields and entire portfolios - all information will be integrated and provide near real-time information to the organization. Decisions will be based on the most current statistics without gaps or blind spots. Resource allocations, projections, supply chain, finance - every essential function in the organization will be both users and contributors on the same page of the data picture. As analytics becomes an integral part of a business' processes, more people will potentially touch the analytics until everyone becomes a data scientist on some level. Yes, Big Data can do all that!