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August 25, 2016

Five Stages of Digital Transformation in the Oil and Gas Industry

The impact of emerging digital technologies is not new to the oil and gas industry. One of the first technologies to make an impact was high-performance computing in seismic processing, subsurface characterization and reservoir simulation. There still exists a perception the industry is a late adopter though the seismic data management teams were the first "big data" people and still lead in data volume metrics. They have adopted storage appliances, storage networks, high performance computing clusters and GPU (graphic processing units) to keep up with the growth in data and the requirement for faster processing on larger models. 

It is dangerous to generalize, but here are five-stages that take the industry through a journey of several decades. It is difficult to try to put a time bracket around each stage as you can still find today all five stages in the same company.

Stage 1: Traditional functional / regional work groups and traditional data management:

Most oil and gas companies are organized by either geography (e.g., Gulf Coast U.S., North Sea, onshore U.S., etc.) or function (drilling, production, facilities, etc.) or a hybrid of both. It is natural that data is captured, stored, processed and used primarily in those organizational units. This is often labeled as data silos but it has worked for many companies for many years.  The silos focus on structured data. In this stage, functional systems of record are often combined with commercial technology solutions that process and provide data access in one program or application suite. Data warehouses, data marts for specific analysis requirements and ubiquitous spreadsheets dominate this stage.

Custom made solutions for specific functional or asset centric analysis provide engineers and analysts with the tools to tackle design, operational and forecasting needs. Traditional business processes worked well for many years. So what changed?

Stage 2: Asset Teams:

The growing need for collaboration between earth science and reservoir teams led to a major organizational change by forming combined asset-focused teams in the 1990s. These first steps toward functional collaboration sparked the evolution of applications and data management techniques which integrated the full work flow including seismic processing, subsurface interpretation and reservoir simulation and production planning.  Asset teams did not bring together all functions as many companies still kept certain functional specialists (drilling, land, operations, etc.) separate.   

Data models needed to reconcile the functional data definitions of both the earth scientists and the reservoir engineers. Standards were developed to define the well and subsurface environments (Professional Petroleum Data Management Assoc. - PPDM) and critical data exchange protocols (Energistics). Though data was still sparse in many older fields, new facilities were being designed with more measurement points and field automation. Data warehousing solutions and subsurface interpretation systems brought together data with an integrated view of the reservoir, but still doesn't give a holistic view of asset performance.

Stage 3: Digital Oilfield:

The turn of the 21st century brought about affordable sensors, field automation and process control technology to the industry. The industry was enjoying high oil prices although natural gas prices were declining due to major new supply discoveries in the Marcellus/ Utica. New capital projects were built which collected large volumes and varieties of data. The availability of more data drove advances in workflow solutions that combined near-real-time data with structured data to produce the first predictive analytics solutions. Onshore decision support for offshore drilling, directional drilling for horizontal wells, equipment health for rotating equipment, water flood and steam flood optimization - at your fingertips.

The arrival of more, real-time and different varieties of data stressed the traditional data infrastructure. Data marts, virtual architectures and workflow orchestration were some of the approaches were tried to enable these new workflow demands. This was in the midst of a high-price environment, so a good business case could be made to increase production now or add reserves to produce later. Projects were scaled down or abandoned once the bottom dropped out of the oil market and the focus turned to reducing costs.

Stage 4: Big Data and Advanced Analytics:

Even though things slowed down in the oil and gas industry, the digital technology world kept moving forward. The three V's (volume, variety and velocity) hurdle was crossed by internet retail and social media with the development of the Apache Hadoop technology stack. This sparked interest in these developments and the potential for the industry, so many companies have set up pilots and test environments to learn more.

The focus is shifting from exploration (E), finding more barrels, to production (P), understanding where to exploit assets to generate positive cash flow and operate lean. The demand for analytics to understand spend and workflow inefficiencies has grown as fast as the price of oil has fallen. Centers for analytics experts have been started as well as new tools for "self-service" business intelligence. Data Science is the cool new discipline, but the demand greatly exceeds supply so most everyone has developed some analyst skills.

Stage 5: Industrial Internet of Things (IoT):

Technology continues to evolve, faster and more disruptive to the status quo. Newer advances are promising "hyper-connectivity" with machine learning where smart equipment with has algorithms to take over more processing and analytics responsibilities. Most companies, especially internal IT departments, are suffering from whiplash. They are still trying to cope with the demands of Stage 1 while new digital engineers demand stage 5 capabilities because they have become accustomed to having "an app for that".   

Driving this in the enterprise is often Operational Technology (OT), such as the field automation and process control environments, which are separate from traditional IT. Many of the new advances and early adopters are in the "shadow IT" world with the enterprise IT groups trying to get an invitation to participate. This can lead to some issues as IT, normally responsible for data centers, desktops, networks and enterprise applications (like ERP systems), is brought in late in the adoption process and often asked to support these programs.

Can the Oil & Gas industry be "disrupted" by digital technology?

Will the "Lower for Much Longer" price scenario lead to a strategic inflection point for the oil & gas industry and push companies towards stages 3, 4 or 5? Will access to information overtake access to reserves and capital as the competitive advantage for the future?

Technology advocates talk about the potential of digital disruption and point to other industries where a transformation has already taken place. It is easier to see the impact of digital technology in a functions' traditional ways of doing business (which has largely already happened) than with collaboration with the other functions like supply chain, ERP and HR where there has been some progress but plenty of opportunity remaining. It is even more difficult to see the impact on the industry engagement with their customers or to develop new products from digital channels.

However, experts and consultants agree that the industry is at a position of complacency not reality and that gradual progress will not be enough. They believe this is the time to start a more radical restructuring of the enterprise business model. There will be pain, no doubt as always when there is a disruptive force causing and enabling change. But remember the objective is to make a profit; recognizing the value of data and using it to find and maximize profit is the name of the new game in town!