Many column inches are filled with discussion of how companies need to operate in the lower-for-longer market that the upstream oil and gas industry continues to face. Much of what we read is focused around headline words such as “innovation” and “disruption.” I feel these are used too often and inappropriately such that we lose focus around their meaning. However, if asked for one word that I believe encapsulates what the oil and gas industry needs, it would be “transformation.”
The oil and gas industry has a history of delivering incredible engineering feats that are impressive in their innovation by any measure in terms of sheer physical or budgetary scale. Whether we look back to the early Gulf of Mexico and North Sea facilities designed to withstand extreme marine environments, the ultra-deepwater subsea developments offshore Africa and Brazil, or the megaprojects around floating LNG, the industry has a history of innovation. The rise of independent oil companies, redefining markets in certain regions, has been a good example of disruptive practices.
What we need to focus on now is how we transform the industry. This word can resonate with all of us working in the industry at every level within an organization; all of us can offer ideas for how we could transform what we do, whether at a company or personal level and with a long-term outlook or on a daily basis.
One of the key transformations occurring now is organizations coming together—through mergers, acquisitions, and alliances—to combine products and services from subsurface through subsea and pipelines to topside processing. The move is clearly toward integration of products and services, with companies trying to reduce the costs of interfaces of different aspects of field development and production and reducing the overhead of business operations. While the impact of the downturn cannot be ignored, it is my hope that these changes will offer the opportunity for transformation from an industry that has been heavily project-focused to one that is geared more to delivering standardized products designed to be flexible enough to be modified to cope with the range of projects and operating conditions in which they will deployed. This is a move toward a more efficient engineering industry in the long term.
The Digital Transformation of Engineering
Our industry has much to learn from the transformations made by successful automotive companies. Whether it is the global economic turmoil of 2008, new competitors entering the market, or changes in regulation, the auto industry has continually had to push to deliver innovative solutions in reduced time and cost. From an engineering perspective, those that have succeeded have done so by progressively reducing reliance on traditional methods (expensive and time-consuming physical prototyping) in favor of extensive engineering simulation.
As an example, beginning in 2008, at a time when automotive manufacturers were seeing cataclysmic market conditions, Jaguar Land Rover undertook a strategic shift. Its goal was not only to survive the downturn but to significantly expand product offerings and sales while increasing profit. The business drivers and challenges laid out by the company’s head of simulation, Andy Richardson, would sound familiar if delivered by the chief technology officer of an oilfield service company: “Develop new technologies while managing massively increased system complexity; identify failure modes and establish countermeasures to achieve right first-time design; reduce in-service failures; simulate the full range of use cases; optimized product, design efficiency, and reduced production costs.” The oil and gas industry shares many of the pains and challenges felt by the automotive industry in that post-2008 period.
Jaguar Land Rover is now well on the way to its goal: robust engineering design ready for sign off before the first physical prototype is built, and it is doing so while delivering financially with 15% or more EBITDA growth in each of the past 5 years.
Transforming Engineering and Product Development
As with the automotive example above, engineering design and product development for oil and gas needs to be led by computer-based simulation. The aim is to reduce time, cost, and risk to the delivery of an operating product or system. The more understanding we gain from a virtual environment, the less testing and prototyping we require. In this approach, a digital twin of the product or system is developed and used to simulate its behavior to identify and assess initial concepts, to test and select which option should be progressed, and then to optimize the product during detailed design.
The use of simulation in the oil and gas industry is not new; however, the transformation we need is in how we use it to lead our engineering and design rather than to verify or validate a design. This means that engineering simulation needs to move further upstream in the design process and also, critically, the activity needs to be more fully integrated into design activities. All too often, engineering analysis teams are exactly that—separate teams from the core design activity. The better integrated the engineering simulation activities can be within the engineering process, the better the chance that the value of simulation can be gained and that the understanding acquired from simulation can lead engineers to make informed decisions.
Cost has never been so critical to engineering design and operation, yet it is often somewhat detached from the engineering design process with costing being performed-based on design options developed. This can lead to complex interactions or numerous iterations in targeting designs that meet the required performance or constraints and at the same time meeting cost and budgetary needs.
However, if you can model or predict costs in terms of materials and manufacture them from an analysis or simulation perspective, it is simply another variable or constraint within the design process. It can be solved in parallel with the design and performance targets.
This approach can introduce a cost model to engineering analysis or physical simulation. A recent case study of applying this approach to the design of a riser demonstrated costs being reduced by more than 10% using an automated design-space exploration approach, where minimum cost was targeted with a wide range of design variables and operational constraints. Here a cost model was integrated with a dynamic riser analysis and the findings provided a very clear trade-off between cost and performance, enabling informed decisions to be made relating to cost and performance from the same data set obtained from within the same analysis.
Transform How We Use Engineering Time
Increased efficiency has been identified as one of the key targets in decreasing development and operating costs. Engineering analysis and modeling has often been seen as labor-intensive with many engineers spending too much time building and running models rather than as engineers using the data. The adverse result of this is that there is less time available to really analyze and understand the findings and outputs, and the real engineering value of analysis and simulation can be lost.
Automated design-space exploration can truly transform this, as can the increasing access to high-performance computing facilities or cloud-computing, which can enable quick turnaround of larger and more complex models. The aim of automated design-space exploration is to take control of a number of engineering simulation technologies so that they work together, sharing data and using this data to intelligently search the available design options. The process automation aspect is key to reducing the time engineers need to spend “babysitting” analysis, making sure we have set up all of the calculations that are needed to obtain a full set of data. The second important aspect is the bringing together of multiple engineering analysis or simulation tools (which could be flow and thermal, structural, costs, or many other predictive approaches or software tools) and enabling them to interact and share outputs so that findings do not have to be manually passed among engineers and teams.
If we automate more of the setting up and running of models, engineers will have more time to work on adding real value by using data to inform engineering decisions and designs. The digital twin approach can drive design, provide design teams with greater insight, and, critically, allow engineers to be engineers. Rather than replacing the requirement for an engineer, automation of engineering analysis frees up time for engineers to analyze and process the data they generate.
Major changes are taking place across our industry and the concepts discussed here are not conceptual or blue-sky thinking. The move toward oil and gas product development becoming truly simulation-led is already beginning, but the rate of change needs to increase. Change is never easy, but the opportunity and results will make it a worthwhile transformation for our industry.
