Impact on oil and gas
Impact on oil and gas

Technology developments and impacts for the oil and gas industry to 2030 will be driven both by economic factors and by sustainability issues. The industry faces an unprecedented combination of market forces, regulation, societal pressure over climate change, and challenges to integrate with other sectors like power and renewables. We predict: the emergence of a digital oil and gas value chain run by machines and software algorithms; wider adoption of low-carbon energy carriers, such as hydrogen for heating and transport; subsea facilities becoming all-electric, with huge capital cost savings for field developments.

Contributing to the energy transition

Efficiency is essential for competitiveness, with global primary energy supply set to peak in 2030, demand for oil declining from the mid-2020s, and renewables supplying an increasing share of energy.

These forecasts are from DNV GL’s Energy Transition Outlook 2019 (ETO). In addition, the industry needs to reduce carbon and environmental footprints from energy production, and to bring low-carbon products to consumers. Regulation of greenhouse gas (GHG) emissions is growing to mitigate global warming, and maintaining social license to operate will become tougher. Over and above issue of competitiveness, and environmental impact, managing safety will remain a pre-requisite.

While the energy transition is accelerating, oil and gas will continue to play a major role in the energy system for a long time. Oil and gas will still be the two largest energy carriers in 2030. Gas demand will keep rising to 2033 before levelling off such that it will account for 29% of the world’s energy mix in 2050. Since existing fields are declining at a rate of around 4% per annum, which is faster than the predicated rate of demand decline after peak oil and gas, continued investment in new oil and gas fields and life extensions of existing fields is therefore still needed.

The persistence of fossil fuels in the energy mix makes addressing the energy trilemma – how to provide a secure supply of affordable, decarbonized energy for the long term – all the more important. That is why improving efficiency, decarbonization and safety are critical, and why the technologies we focus on here are of signal importance.

Energy transition timeline
A digital value chain run by machines and algorithms

Machines will increasingly replace people in oil and gas operations, in a digital transformation already well underway. Traditional improvements involving automation, robots and software modelling are being blended with novel digital opportunities offered by sensor data, virtual and augmented reality, artificial intelligence (AI) and advanced use of simulation models and virtual testing.

"The digital value chain enables a virtual walkthrough of the asset life cycle at the desktop before development starts."

Assets are no longer purely physical or digital; their performance can now only be understood and optimized by considering them as cyber-physical systems. For a capital-intensive industry focused on efficiency, safety and the environment, digitalization can offer significant benefits by enabling:

  • more efficient projects and operations
  • greater collaboration by sharing data and models through cloud platforms
  • new business models for value-chain efficiencies
  • greater supply chain transparency to meet greater regulatory and public scrutiny
  • improved safety with less people offshore

The outcome will be the merging of project management and operations by 2030 into a digital value chain largely run by machines and algorithms.

Digitalization improving the capital value process

Exploiting digitalization can improve the efficiency and success of exploring for oil and gas, appraising discoveries, and developing infrastructure to produce and transport hydrocarbons. Relevant tools include cloud computing, advanced simulations, virtual system testing, virtual/augmented reality and applications of machine learning (ML), a subset of artificial intelligence.

We expect such tools to progressively merge in full digital twins combining data analytics and real-time and near-real-time data on installations, subsurface geology, and reservoirs. A digital twin created early in a field’s development can simulate and visualize the performance of the asset through its entire life cycle.

"Solving the digital trust challenge is key to creating by 2030 a full digital value chain run by machines and algorithms."

This can help in choosing development concepts (subsea, floaters, fixed installations, etc) and making major development decisions (drainage strategy, number of wells, capacities, etc).

This in turn allows better and early optimization of technical designs, production strategy and commercial models. Standard designs can be reused from a best practice library of geometries with automatic configurations, equipment catalogues, and weight and capacity estimates calculated automatically.

Technical requirements guiding construction can be checked automatically to ensure that designs comply with requirements. Finally, unconstrained reservoir production profiles for oil, gas and water can be simulated through the different potential development concepts to forecast actual production throughout field life.

Overall, this enables a virtual walk-through of the asset life cycle at the desktop before development starts. We call this the digital value chain, with the digital twin at its core. Operators and contractors have parts of it in place already, and we expect it to be a full reality before 2030 as a necessity for reducing development times and costs in the energy transition.

Building trust in algorithms

As operations become more automated and robotic, more autonomous systems will make decisions. Data from ubiquitous digital sensors will combine with real-time simulations to allow advanced AI and ML applications. Digital twins are laying the foundations for merging these technologies.

The major leap towards 2030 will involve AI and ML replacing humans for many major decisions. We call this ‘operations run by algorithms’. But the algorithms themselves must know and tell us when they cannot be trusted. Solving the digital trust challenge is key to creating a full digital value chain run by machines and algorithms.

Machine learning models are becoming more precise, using deep neural networks with increasing numbers of hidden layers. This improves the precision of the algorithms, but it can still be ‘confidently wrong’. We will therefore see more efforts to develop and apply probabilistic machine learning.

Probabilistic machine learning (ML)

In a probabilistic framework, a ML algorithm will measure and report its own uncertainty when making predictions and decisions. We could then let it take major decisions within a range of uncertainty, but prevent it from controlling processes if its erroneous usage may lead to safety-critical situations. Or, we could apply ML to control and activate safety-critical barriers if uncertainty reaches a threshold level. These describe data-driven approaches in areas where there is usually limited failure data to train algorithms, and uncertainty may be high.

To trust AI/ML and data-driven approaches in such cases we must combine them with causal physical models. This will ensure that knowledge encoded both in the data and the physical models goes into the algorithm and the decision. When probabilistic methods are combined with digital twins, we arrive at a concept that we call a probabilistic digital twin1.

By adding probabilistic models of risk and uncertainty to the digital twin, the algorithm can make decisions and also know its own limitations. Then – and only then – can we allow machines and algorithms to replace humans for major decisions.

We expect that both probabilistic ML and integrated systems of data-driven models and physical models will be in use towards 2030, making decisions that humans make today. The human role in 2030 will be to provide context – supervising the machines and ensuring that they operate within acceptable trust ranges. Where we currently consider augmented reality and augmented intelligence to be machines providing decision support to humans, the roles will be reversed by 2030. Machines and digital twins will make the decisions in their digital world, augmented with human supervision.

Asgeir Johan Sørensen from NTNU discusses the technology that will impact industry in the next decade.
Electrification going subsea and subsurface

Producing, processing and exporting oil and gas from an offshore platform requires up to 100 megawatts (MW) of power generation capacity, usually from onboard turbines fuelled by gas from the wells. To minimize weight and space demands, these are mainly simplecycle turbines with relatively low efficiencies of 25% to 30%. However, they emit more carbon dioxide (CO2) per unit of generated power than more-efficient onshore power plants using the same gas.

Gas turbines account for some 80% of CO2 emissions offshore, so electrification with a clean energy mix has high potential for reducing this2. Several offshore platforms (e.g.,in Saudi Arabia and Norway) currently receive power from shore via subsea cables.

Full electrification offshore requires new power infrastructure. High-voltage direct current (HVDC) power transmission systems may be needed for long distances, requiring transformers onshore and offshore. All of this is costly. Alternative ways of providing renewable power locally to platforms are therefore being considered, examples being the WIN WIN and Hywind projects.

Electrifying subsea production

We expect electrification offshore to have the most impact underwater. Electric and electronic applications for subsea control systems, choke valves and production valves have proven highly reliable. They have focused on production parts of subsea systems. Safety-critical parts, including Xmas trees and down-hole safety valves (DHSVs) still operate by hydraulic power from topside. Hydraulics are costly to install and maintain – good reasons to replace them with all-electric systems.

While cost-efficiency is the main driver for all-electric subsea solutions (see fact box), other advantages include removing topside high-pressure equipment (a safety risk to personnel), and eliminating the risk of hydraulic fluids polluting environments.

In the joint industry project ‘Safety 4.0’, several partners in industry, government and academia are working to develop a framework (work-processes, methods and tools) for standardized demonstration of safety for novel subsea technologies3. By 2030, we expect much closer integration between production and safety systems, calling for new safety philosophies with new safety frameworks and standards. In this work Safety 4.0 also draws on experience from relevant industries like aviation.

One trailblazing all-electric subsea system is operating today: an electric Xmas tree with electric downhole safety valve (DHSV) in Total’s K5F field off the Netherlands. This has been successfully operated since 20084, and we expect all-electric subsea systems to be the norm by 2030 due to cost and safety advantages.

Cost-efficiency to drive all electric subsea solutions

Replacing hydraulic fluid lines with electric cable can reduce capital expenditure (capex) by 15% for a 30-kilometre stepout. Electrifying the Xmas tree and downhole safety valve may further reduce capex by 10%, according to numbers from Total5. All-electric subsea systems create opportunities for monitoring and diagnostics to optimize the entire subsea chain.

For the Norwegian Continental Shelf alone, OG21 has estimated that all-electric systems may reduce costs by NOK 14 billion for the period 2018–20406. We expect more combinations of all-electric subsea with much simpler topsides that may be (normally) unmanned. This will enable additional cost-savings beyond those of the all-electric technology itself.

Interview with Gunnar Lille, Managing Director of OG21
Will hydrogen scale to power society?

Hydrogen fuelled the first combustion engine but is now mainly a chemical feedstock. Combusting hydrogen, or using it in a fuel cell, emits no GHGs, making it attractive as a clean-energy carrier. National initiatives worldwide are establishing the economic and technical feasibility of scaling up hydrogen production for safe industrial and societal use; it could even power entire cities7.

We believe that engineering can overcome safety issues. In our view, the main challenge is being able to create low-carbon hydrogen value chains with economic potential to scale globally.

Steam-methane reforming (SMR) of fossil fuels produces 95% of hydrogen today. The product is clean, the process is not; and because the CO2 produced is not captured, the end product is termed ‘grey hydrogen’. Gas network operators are therefore running extensive initiatives to combine SMR with carbon capture and storage (CCS), with most but not all of the captured CO2 to be permanently stored in offshore reservoirs. This results in ‘blue hydrogen’, offering a low-carbon value chain based on fossil fuels, which is of great interest to industry and society. Hydrogen can also be produced by electrolysis of water, powered by entirely clean and renewable power. This ‘green hydrogen’ is currently more costly to produce than blue hydrogen, but we expect cost parity around 20308.

We expect blue hydrogen to become a low-carbon option where gas infrastructure or industrial demand already exist. More globally, green hydrogen may power transport. All these uses rely on hydrogen infrastructure being installed. The oil and gas and power sectors may therefore share a common interest in seeing hydrogen scale.

Mounting pressure to reduce the carbon footprint of their production and products makes blue hydrogen an attractive opportunity. Furthermore, failure to deploy hydrogen and CCS at significant scale can strongly impact on the perception of natural gas as a bridging fuel in the energy transition. Hence, hydrogen without CCS is not really an option.

The oil and gas industry has the competence and experience to play a main role in managing CO2 transport and storage for CCS. With increasing carbon prices, this role may become a significant commercial opportunity. CCS is not normally cost effective for small emissions from point sources, such as turbines on offshore platforms, or emissions from natural gas for heating buildings. The default decarbonization option is electrification using power with a low-carbon footprint (from renewables, nuclear plants, or fossil-fuelled power plants with CCS). But an alternative could be replacement of natural gas with green or blue hydrogen, provided the oil and gas industry demonstrates that blue hydrogen is reliable and cost-effective compared with alternatives.

Recent DNV GL research provides detailed analysis of the factors affecting the future competitiveness of hydrogen production pathways. We see the oil and gas industry is well positioned to take a leading role in the hydrogen value chain. It has major stakes in gas transport infrastructure, liquefied natural gas facilities and terminals, natural gas storage sites, and CO2 storage operations. With industry having the capabilities, and society the needs, we anticipate growth locally and globally in hydrogen value chains towards 2030.

Main options for the production of hydrogen
Technology Outlook 2030 report cover
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