DUG Elastic MP-FWI Imaging is a unique approach to seismic processing and imaging. (Image source: DUG)

DUG has released the latest results from its elastic multi-parameter full waveform inversion (MP-FWI) imaging technology which it launched in 2022, since when more than 70 successful projects have been completed worldwide

DUG Elastic MP-FWI Imaging is a unique approach to seismic processing and imaging which is not only a complete replacement for the traditional processing and imaging workflows, it also replaces the subsequent inversion workflow for elastic rock properties.

With the traditional processing workflow, projects can take many months to years to complete. It involves the testing and application of dozens of steps such as deghosting, designature, demultiple and regularisation, all designed to overcome the limitations of conventional imaging. These workflows are complex, subjective, and very time-consuming and they rely on many assumptions and simplifications. All of these issues impact the output data quality. The resulting, primary-only data then undergoes a similarly complex model-building workflow to derive an estimate of the subsurface velocity, which is used for depth imaging. Post-migration processing is performed before the pre-stack reflectivity undergoes another workflow to derive rock properties that feed into interpretation, also relying on simplifications of the actual physics.

As well as three-component reflectivity and velocity, DUG Elastic MP-FWI Imaging enables the estimation of fundamental rock properties like P-impedance, density and Vp/Vs from field data, without the need for a secondary amplitude variation with angle (AVA) inversion step. DUG Elastic MP-FWI Imaging simultaneously resolves not only subsurface structural features but also quantitative rock property information while avoiding the need for extensive data pre-processing and (post-imaging) AVA-inversion workflows.

“Elastic MP-FWI Imaging accounts for both compressional and shear waves, handling variations in seismic wave dynamics as a function of incidence angle, including in the presence of high impedance contrasts and onshore near-surface geological complexity,” said Tom Rayment, DUG chief geophysicist. “Multiples and converted waves are now treated as valuable additional signal, increasing sampling, resolution and constraining the inverted parameters.”

DUG managing director, Dr Matthew Lamont, added, “We have invested over a decade of R&D to realise this opportunity. Our new Elastic MP-FWI Imaging technology is the product of a multi-year, significant and ongoing R&D effort, which has seen the continuous integration of complete-physics FWI imaging including viscoelasticity, anisotropy and multi-parameter updates. When using the full wavefield for simultaneous velocity model building, rock property inversion and true-amplitude imaging, a multi-parameter solution is a necessity.”

“The fact that DUG MP-FWI Imaging is delivering material imaging uplifts using field-data input is very powerful, but to couple this with high-resolution elastic rock property outputs for quantitative interpretation is even more exciting, providing immediate opportunities for new surveys and maximising the value of legacy datasets,” said Martin Stupel, geophysical manager, Geophysical Pursuit Inc.

NOCs across the region are making tangible progress in applying AI to boost performance.

James Thomas, partner, Shantanu Gautam, principal, and Pavel Evteev, senior manager, with Strategy& Middle East, highlight the importance of culture change for national oil companies (NOCs) to harness the full benefits of AI

The GCC’s national oil companies (NOCs) must put AI to work if they are to keep delivering the world’s lowest cost and lowest carbon footprint barrels. To achieve this, NOCs need organisational cultures that can quickly produce many small, high-impact artificial intelligence (AI) applications.

AI-powered solutions are the next major cost and efficiency frontier in the oil and gas industry. Leading oil majors are already using them to produce oil faster, at lower cost and resource intensity. For example, AI can accelerate subsurface analysis, reduce uncertainty, and optimise capital allocation. Shell partnered with startup Avathon (formerly SparkCognition) and is using AI-powered deep learning to reduce seismic shots by 99%, maintaining image accuracy while cutting exploration time from nine months to just nine days.

Beyond exploration, AI is transforming well planning, automating drilling, predicting conditions, and streamlining workflows. ExxonMobil, collaborating with IBM, used AI to reduce well planning and design time from nine to seven months, and cut data preparation time by 40%.

Drilling optimisation is another area seeing major gains. AI can now analyse real-time downhole data, optimise rate of penetration, and predict failures. Machine learning can adjust drilling parameters dynamically, reducing non-productive time, cutting costs, and improving well economics. ConocoPhillips used three years of drilling data to develop a machine learning model that improved vertical rate of penetration by 20% and reduced premature drilling-motor failures by 65% – saving US$30,000 per well.

Environmental performance is improving too. AI can track emissions in real time, detect leaks, and increase carbon capture. Chevron deployed AI to optimise methane emissions reduction in upstream operations, helping cut methane emission intensity by 60%.

NOCs across the region are also making tangible progress in applying AI to boost performance. Aramco, for example, deployed 40,000 sensors across 500 wells, enabling AI-driven process control that increased production by 15% and halved troubleshooting time. ADNOC’s Emission X tool helped abate 1 million tonnes of CO2 in one year through AI-powered emissions prediction and optimisation.

Building on successes

Now is the time to build upon these successes. The GCC’s NOCs can develop a broad-based, AI-solutions portfolio that drives immediate, incremental gains and mitigates the risk of being outpaced in the AI race. These solutions can drive down costs, support better exploration and investment decision-making, accelerate field development, optimize drilling efficiency, and reduce emissions without cutting production. We estimate that AI applications can reduce the upstream operating costs of the GCC’s NOCs by 10–15%, with approximate annual savings of US$3–US$4.5bn.

The region’s NOCs need a new approach. In the past, they have gone slow-and-steady with large-scale digital initiatives. That meant a robust cycle of requirements definition, vendor selection, and at-scale implementation that took years to come to fruition.

Instead, GCC NOCs need a distributed AI capability as well as a cultural shift that would encourage employees to quickly learn from peers, experiment with applications, and rapidly deploy effective applications. By enabling simultaneous AI development across businesses and functions, NOC’s could realise steady, incremental gains over time.

To build this AI capability, NOCs can forge strategic partnerships with AI startups, collaborate with technology providers, and invest in developing in-house expertise. Importantly, this effort should be business-led, not IT-led. It should teach engineers and analysts to use AI tools efficiently and bring them together into small, cross-functional teams that contain the business and technical knowledge needed to drive improvement.

These teams need a three step, agile approach to AI experimentation. First, a rapid review to identify initial, high-priority pockets of inefficiency in which traditional methods have reached their limits, and AI can unlock productivity gains with minimal complexity. Second, a careful selection of AI use-cases based on their impact and ease of implementation, and to prioritize high value, quick wins. Third, rapid prototyping and scaling to ensure the most effective solutions reach enterprise-wide adoption.

To succeed, such an approach requires a cultural shift. Speed must be prioritised over perfection, while leaders must empower their teams to experiment, learn, and even fail early – all with minimal oversight and bureaucracy.

NOCs that quickly build a capability for AI and successfully foster a culture of experimentation and empowerment will be able to rapidly produce many small, high-impact AI applications that drive immediate gains. Over time, as more solutions emerge, they can increasingly connect like pieces of a larger puzzle, creating a cohesive, AI-driven organisation.

Strategy& Middle East is part of the PwC network.

Mark Moffat, CEO of IFS.

IFS, a leading provider of enterprise cloud and Industrial AI software, has launched IFS Nexus Black, a strategic innovation programme to drive ahead AI adoption for industrial organisations

Built on the foundation of IFS.ai, Nexus Black combines advanced AI technologies, deep industrial context and a dedicated delivery team, partnering with customers to tackle bespoke, complex challenges in asset-intensive industries. It enables rapid development and deployment of AI capabilities in a matter of weeks.

Initial use cases include predictive maintenance, manufacturing scheduling optimisation, AI copilots for service and sales, and intelligent automation for finance and supply chain.

Nexus Black benefits include:

• Agile, sprint-based co-creation and prototyping. A proven co-development model that is safe, scalable and fast
• Structured four phase model: Problem Definition; Proof of Value; Accelerated Development; Digital Continuity
• Access to dedicated AI engineers, domain experts, and solution architects, with deep expertise in industrial contexts and enterprise architecture
• Collaboration on agentic AI and contextual intelligence with industrial scalability

IFS’s deep industry footprint and proximity to rich industrial asset data enables the combination of context and AI, resulting in trusted contextual AI into live operations, quickly and securely.

“Too many businesses are stuck choosing between inflexible enterprise tools or niche AI vendors with no roadmap to scale. Nexus Black changes that,” said Mark Moffat, CEO of IFS. “Nexus Black is IFS’s commitment to rapid, high-impact AI innovation for leading industrial organisations. It combines the agility of a start-up with the industrial context, security and delivery strength IFS is known for. It’s how we help our customers leap ahead - not just catch up.”

“AI capabilities like co-pilots and embedded agents are no longer a differentiator, they’re expected,” said Matt Kempson, SVP, Commercial at IFS. “What sets IFS apart is how we apply AI – rapidly, with deep industrial context, and in direct collaboration with our customers. Nexus Black gives us a razor-sharp edge, engaging deeply with complex industrial challenges, rapidly building proof points, and translating innovation into scalable impact across our AI portfolio.”

At its IFS Unleashed event last October, the company highlighted the transformative power of industrial AI and underlined its ambition to become the undisputed category leader in industrial software, for the benefit of all its customers.

Retina at-bit imaging provides precise measurements at the critical point of first contact between the drill bit and the formation. (Image source: SLB)

SLB has launched Retina at-bit imaging, which converts measurements taken at the drill bit into detailed, high quality borehole images

Successfully tested in the Middle East, Retina at-bit imaging provides precise measurements at the critical point of first contact between the drill bit and the formation, providing unsurpassed image clarity in large hole sizes as drilling commences and the borehole diameter reduces progressively toward the reservoir section, thereby enabling identification of formation characteristics to optimise drilling efficiency, formation evaluation and safety.

It works by using a PDC drill bit tailored with the latest design, architecture and cutter technologies to accommodate sensors for measuring forces at the bit face. It captures formation details to visualise and characterise layers, bedding dips, thin beds, fractures, and textural variations without interference from any type of drilling fluid. The raw data is processed to create a high-resolution borehole image for formation characterisation and rock properties qualification. High-resolution images are important for geological characterisation from the outset of drilling, revealing potential well-related expenditures such as mud-loss zones, hard drilling intervals and stuck-pipe events — which can add days to complete the well, incur non-productive time, and increase safety risks.

Retina addresses the limitations of traditional imagers, which are often restricted by operational factors like drilling fluid type and wellbore damage, and often not usable in the reservoir access intervals due to borehole size limitations.

Field test data from land operations in the Middle East have demonstrated Retina's versatility and effectiveness. In one field test in the Middle East, Retina revealed the presence of fractures and vugs (small cavities or pores within the rock) in carbonate formations, providing critical information for predicting and preventing mud losses. During another field test, the tool also provided high-resolution images of stylolites, which act as pressure barriers, allowing for a better understanding of productive zones.

“Retina provides an unparalleled view of the formation and its characteristics downhole, enabling better-informed decisions from the start,” said Cecilia Prieto, president, Well Construction, SLB.

“This leading-edge technology marks a significant advancement in borehole imaging, providing drillers with critical insight in situations where it was previously impossible. With imaging at the bit, before the formation is affected by drilling fluids, the exceptional resolution unlocks new opportunities around drilling, fluid management, and completions, leading to significant risk reduction and production optimisation.”