Oil and gas organizations are looking increasingly to embrace technology and improve processes to move further down the path toward transformation in a post-Covid world. To accomplish these objectives will require structure, pragmatism and agility while navigating the road ahead.

As the upstream oil and gas sector continues to navigate Covid-19, many things will return to normal. For example, the industry will continue to face analyst pressure for higher EBITDA performance through lower operating and administrative costs, while also achieving optimal well spacing and lateral lengths through technology. Exploration and production operators will continue to face threats from renewables, amid carbon footprint regulations, field safety risks, and challenges attracting talent. On the other hand, Covid-19 forced a cultural reset that presents an opportunity for positive change.

For some, the pandemic was needed to spark a digital transformation. As an example, Covid-19 demanded rapid adoption of technology and remote working to keep non-essential personnel away from field operations. Support functions were especially asked to manage their responsibilities through technology. Even in the field, things were different, as rotational crewing patterns (to minimize the risk of infection) and remote supervision of work were conducted through video conferencing and recordings. Even corporate management worked differently, using technology and office rotations to steer the business through financial, operational and safety challenges.

The relocation of personnel greatly affected the way that organizations interact to make decisions, both vertically and horizontally. What many professionals fail to recognize is that the upstream oil and gas industry has long embraced both the concept of remote work, and using technology advances to make smarter decisions.  


In the early 2000s, amid pressure to reduce costs and minimize safety risks, several major players standardized and rationalized their offshore operations by identifying work that could be relocated onshore and centralized into one operating hub. This reduced complexity by taking non-essential “people-on-board” out of the offshore workspace, thereby lessening the need for platform bed space, as well as helicopter/boat seats, to and from the platform. With significant offshore operations consolidated and transitioned onshore, these organizations then tackled their supply chains, streamlining material and equipment movements and rationalizing shore-based warehouses and critical material inventories. 

At the same time, on the back of newly implemented ERP systems following the Y2K scare and several emerging data analytic applications, new initiatives such as “oilfield of the future” and “digital oilfield” emerged. Major and large independent E&P operators began using data-driven analytical tools to optimize production from wells and maximize reservoir recoveries. Production measurement technologies were developed, to allow centralized monitoring of well and facility performance, alarming offshore personnel when targets weren’t met or critical equipment failed. Some majors and larger independents created centralized control centers that spanned an entire floor and had multiple visual screens, collaborative planning and problem-solving rooms, as well as advanced telecommunications to maintain contact with field operations. 

It wasn’t long before these digital solutions were applied to onshore basins and fields. Larger independents took the first move to remove and relocate field personnel to central control centers, typically one per basin or large multi-field area. More sophisticated ways of optimizing operator rounds were developed to reduce windshield time by segmenting and prioritizing wells and leases according to profitability and operating risks.

The emergence of digital analytics played a large role in this trend, as E&P operators began to realize they could improve production monitoring from a regional field office versus a field operator or pumper visiting each lease to check on wells and tank batteries. Instead, companies discovered they could increase production rates while lowering lease operating expenses through centralized field and well monitoring. They could also better leverage their most skilled technical engineers and geoscientists to support multiple fields across a large geographical area. 

For the last decade, the industry has increasingly leveraged technology for business-level operating decisions. As a foray of new technologies facilitated such thinking, companies realized they could handle big data through the cloud, edge computing and machine-learning applications. We saw data technology applied to exploratory, appraisal and drilling operations.

E&P operators applied visual recognition to geosciences, as well as predictive analytics and, eventually, machine learning to various drilling and completion activities, from mud systems to directional bit controls and frac-staging optimizers. Most recently, we’ve seen multi-algorithmic analytic models applied to EOR injection and circulation systems. It seems the pace of technology application is accelerating dramatically with each passing year.


While the concept of working remotely isn’t new to the industry, the pandemic is having a significant social impact on how people work in distributed environments. The opportunity to interface and communicate directly through technology and rely on data analytics and models for decision-making has been fast-tracked. We’ve seen a small breakthrough, with people becoming more comfortable in challenging and supporting each other remotely. More importantly, ad-hoc decision channels (those challenging organizational hierarchy) are emerging, bypassing previously trending centralized structures that depended on “in-person” cross-functional teamwork.


Over the next decade, we can expect transformation to manifest around two key principles. First will be the use of digital sciences across the entire field development process, over and above the drilling, completion and production operations level. This will allow for macro or total ‘cognitive process’ optimization, Fig. 1. With this shift, the entire set of E&P processes, including land leases, X/Y targets, drill pads, well integrity, reservoir life, and both safety and environmental constraints will be treated as one continuous and integrated network of three-dimensional real estate plots. Comprehensive decision models will be used, simulating wells manufactured through moveable workstations, with supporting networks of equipment supply, service providers and infrastructure requirements. 

Fig. 1. The total chain integrated algorithmic modeling involves the five key pillars of social acceptance, renewables competition, carbon footprint, investment capital, projected ROI & FCF and the economic life of the asset.

Soon, field development planning will look more like urban development planning for “cities of the future.” The majors are already experimenting with complete cognitive value chain modeling. This attempt to optimize volumes from the reservoir and wellhead to the sales meter considers and incorporates indirect operating parameters, such as transportation delivery networks, carbon-footprint impacts and full life-of-the-field costs, including abandonment.

Secondly, we’ll see more “distributed decision-making” and a re-thinking of cross-organizational roles and responsibilities, especially regarding “role-based” performance accountabilities, Fig. 2. In some respects, this will be a reversal from previous decades, as E&P operators realize not every decision must be controlled with a top-down approach or delayed while waiting on technical experts to make the call. When Covid-19 hit, most organizations adjusted decision-making protocols to allow for less formal actions and responses to operating conditions. There was much less discussion about why something should or should not be done—instead, organizations were forced to adopt a “get it done” mindset, allowing on-site workers with intimate knowledge of operations to take control. If mastered through technology, the ability to avoid going to the top or center to make decisions that then trickle back to the field will be very beneficial in the future.

Fig. 2. The analytic capability progression is derived from five core areas of analytics: descriptive, diagnostic, predictive, prescriptive and cognitive. This progression is the basis to deliver, assess and replicate KPIs and supporting performance metrics.

This newest wave of decision-making innovation will consider how existing and emerging technologies allow us to retain the cost containment and technical expertise advantages of centralized monitoring and control while placing “decision rights” for day-to-day actions with those closest to the drill pad or wellhead. This will significantly impact organizational effectiveness and allow for redistribution of job responsibilities and managerial or supervisory oversight. As the supporting technologies improve, the question will then become, “where can we merge and consolidate organization roles and departmental functions next, whether vertically or horizontally?” 

We’ll see a consolidation of mid-management, as those who execute work gain more capability to analyze their individual contributions and decide where “prescriptive” corrective/preventive actions are required. This will leave mid-management with more time to forecast, plan, allocate resources and schedule work more accurately, which is where their highest value lies. Meanwhile, upper management will have greater visibility into overall performance through advanced predictive tools that alert them to plan deviations and performance shortcomings. All of this points to organizational structures, roles and responsibilities that look very different than they did before COVID-19.

Delivering Value

Fig. 3. The core processes of a digital management system are to streamline, automate, eliminate and add value. Taken together to drive the overall system, the end-result should be to improve timeliness, accuracy and visibility.

As oil and gas organizations continue to embrace technology and improve processes to move further down the path toward transformation, they must do so with structure, pragmatism and agility. Without this “framed” approach, it’s only a matter of time before an organization realizes it has invested large amounts of money into “digital toys” that deliver very little measurable value across the value chain and business processes, thereby detracting from the earnings and/or balance sheet, Fig. 3.


Create a roadmap based on an architectural blueprint or framework to help everyone understand your objectives and which investments are most important. It’s a lot like buying furniture for a newly-built home. You wouldn’t go shopping for furniture without knowing the layout of the house. Otherwise, you would end up returning a lot of furniture. Technology investments aren’t easily returned, and the cost of maintaining numerous low-value applications can be enormous. My advice for a framework, depending on which management science you prefer, is either a management-system blueprint that considers the flow of information and decisions required throughout the organization or a business-process blueprint that offers opportunities to streamline, eliminate work and accelerate value. 

Set a stake in the ground regarding your operational goals, starting with the end in mind, and work your way backward. If you are trying to sustain lower headcount, align every investment with that outcome or, for example, if your goal is to support growth, ensure your investments survive on those merits.

Find a balance between value and technology disruption. It’s important that you’re not using new technology to simply recreate the past. The key is an ability to continually measure the value from each digital investment, not as a one-off, but as a daily and weekly contributor to profitability and capital efficiency. This requires a structured portfolio of value options, each stage-gated from ideation to outcome and linked to resources and timelines required for realization. Within this managerial review must be an iterative experimentation loop so that pilot trials minimize risks without impeding innovation velocity.

Think fast but take it slow. You don’t need to invest in every technology under the sun, and you likely won’t have all the skills needed to implement and manage technology in-house anyway. Start by investing in a logical framework, but not in everything at the same time. Buy a few pieces of furniture and see how they look in the house before buying more. Remodel one room at a time and evaluate fashion changes as you go. With the accelerating pace of technology invention, you’ll be outdated before you finish, so think of each investment as a “base” to build and expand upon.

Identify internal early adopters and thought leaders and empower them with opportunities to learn, so they can then be ambassadors of change to the rest of the organization. Let these change-agents do the hard selling internally, so their peers say, “If those solutions helped him/her so much, I’d like to have them too.” Make people throughout your organization envious of the performance step-change and show them how your champions gained more capacity for work and capability to lead through their investments.

Above all, be willing to take risks but also willing to stop and reverse direction. Not every road is free of congestion or construction crews, so prepare to take an alternate route. With relentless ambition, you will get there.

Gary Traylor is a senior vice president for Myrtle Consulting Group, a leading operations management consulting firm based in Houston, Texas. Since joining Myrtle’s leadership team, Mr. Traylor has led business transformation programs for oil and gas operators around the world, and today he helps clients develop digital strategies for next-generation capabilities. He began his career with Amoco (BP) as a completions and production engineer, progressing to multi-field management responsibilities. Mr. Traylor earned a BS degree from LSU and an MBA from the Anderson School at UCLA. He also attended executive programs at Stanford and INSEAD.

Published on WorldOil.com