Sami Alnuaim's new book pushes oil fields toward making their own decisions

Sami Alnuaim, a former SPE President, spent over 30 years at Saudi Aramco. His book, Digital Transformation of Oil and Gas: Intelligent Fields and Workflows, came out from John Wiley & Sons on November 2, 2026. It lays out a framework for putting adaptive, learning systems to work so that operations can make decisions autonomously, going further than conventional automation.

Wiley says the 240-page book connects academic rigor with real-world implementation, drawing on decades of upstream petroleum engineering leadership. Implementation is where the idea gets tested. Most digital projects in this sector stall at the pilot stage. That means a field that makes its own decisions depends less on its algorithms than on who funds it, who owns its data and who has the skills to run it.

Automation that learns

According to the publisher's summary, the book shows how real-time data, AI, robotics, digital twins and emissions monitoring combine inside upstream workflows across exploration, production and reservoir management. It also covers HSE management and how sustainability is built in, and it traces the shift from smart fields to fully intelligent field architectures.

The distinction matters more than it sounds. A smart field collects data in real time and runs on rules someone wrote. The intelligent field in Alnuaim's framing adapts: the system learns from operations and makes the call within its scope. That suggests a different kind of procurement question. Buyers stop asking what a tool monitors and start asking which decisions it can be trusted to make.

Academic work points the same way. In a paper published in the proceedings of the 2024 China Automation Congress, Jiyuan Liu, Zekun Ren and Cheng Dai wrote that integrating traditional information technology with oil and gas exploration engineering had greatly improved efficiency but was now approaching its limit in technological innovation and process improvement. The authors called for faster integration of big data, 5G, virtual reality and IoT with traditional exploration projects, leading to new application scenarios in intelligent drilling, intelligent geological research, intelligent equipment, intelligent diagnosis and early warning systems.

Their diagnosis was specific. Presented at the congress in Qingdao, China, in November 2024, the paper argued that existing technology and digital achievements were no longer enough to meet rising demand for oil and gas storage and production while also cutting costs. That reads as the ceiling Alnuaim's smart-to-intelligent progression is built around. Digitizing existing workflows pays off, then plateaus, and the next gain has to come from systems that do more than report.

A 2023 review by Salam Al-Rbeawi in Elsevier's Upstream Oil and Gas Technology reached a similar priority, concluding that digitalization should come first for most upstream companies. It also projected that upstream could take the largest share of digital transformation's added value over the coming decades, possibly more than 60% of total investments, against only 10% for midstream. If that projection holds, it helps explain why a book about intelligent fields keeps its attention on upstream workflows.

The 70% problem

Here's the catch. DXC Technology's April 2026 outlook for the sector says roughly 70% of oil and gas digital transformation initiatives never go beyond the pilot stage. The same outlook says Rystad Energy cites substantial upfront costs as a major hurdle, especially for smaller players burdened by legacy tech. It also notes that oil and gas companies increasingly rely on outside expertise, with the intensity and frequency of partnerships with technology firms rising sharply since 2021.

The stall isn't for lack of awareness. DXC says executives already understood the importance of enterprise-wide transformation five years ago, yet the investment has not carried most projects past the pilot. Among the challenges it lists are legacy infrastructure and uncertain ROI, which it says leave so many projects "left to rot in pilot purgatory."

That's the tension the book walks into. A pilot can prove that a digital twin predicts a pump failure on one pad. A system that adapts and decides across a field needs the data, the funding and the operating authority to work everywhere at once. Wiley pitches the book's strategic frameworks at executives trying to line digital investment up with profitability, sustainability and resilience. For an operator with a drawer full of successful pilots and few scaled deployments, that budgeting question is probably the more useful part of the book than the technology chapters.

The volume pressure keeps this from being an abstract debate. DXC cites EIA projections that US crude oil production will rise to 13.6 million barrels a day in 2026 and 13.8 million in 2027, against a backdrop of supply disruptions linked to events in the Middle East. It also notes that in many places, upstream production still relies on decades-old equipment, time-consuming manual processes and decisions made without adequate data.

That last item is the gap an intelligent field is meant to close. Yet the upstream fixes DXC lists, predictive maintenance, digital production modeling, remote monitoring and diagnostics, and automated data collection through wireless sensors, mostly inform a decision rather than make one. The distance between that list and Alnuaim's architecture is a fair measure of how far most fields still have to travel.

Money is less scarce than it might be. Citing Forbes, DXC says the energy sector surged nearly 40% in the first quarter of 2026, with many upstream, midstream and downstream players posting positive results. That could ease the funding constraint for larger operators. Rystad's warning about upfront costs, though, lands hardest on the smaller players with legacy tech, and a good quarter doesn't retire old equipment.

Who owns the field's data

A field can't decide on its own using data it can't reach. Oil & Gas Journal laid out the underlying shift back in 2018: equipment makers were building service businesses on condition-based maintenance, while operators, wary of vendor lock-in pushing up costs, increasingly wanted to own both the data their installations generate and the technology that manages it. The same piece noted how fast the economics had moved over the previous decade, with data processing costs improving by a factor of 60, bandwidth by a factor of 40, data by a factor of 1,000 and sensors by at least 50%.

Cheaper sensing did not settle who controls the output. The journal described oil and gas companies as historically hesitant to let equipment suppliers run maintenance programs, and said the fear of lock-in remained relevant even as the benefits for both vendor and end user became clearer. Suppliers also wanted operating data to sharpen design parameters for new products, which gives them their own reasons to hold on to it.

Al-Rbeawi's review treats the same issue as structural. It names data transfer and data sharing as two key factors in digital transformation and argues that integrated data platforms could improve collaboration across the oil and gas ecosystem and speed up new solutions. It also suggests the digital era may require new regulations, particularly around sharing data. For an intelligent field, that moves data rights from a contract clause to a design precondition: a system can only decide across exploration, production and HSE if it can see all three.

Choi was talking about asset valuation, not field control. World Oil described PetroAi, the collaboration formed in January 2020 that includes his company, as building AI tools for investors rather than operators. The same article said the Covid-19 pandemic significantly accelerated demand for digital oilfield technology in 2020, as companies sought to monitor and operate fields remotely. His point still transfers. More sensors produce more data, while an intelligent field depends on what the system does with it.

The strongest objection to the intelligent field as a near-term goal comes from the same sources. Oil & Gas Journal expected the largest fleet owners to lead because they capture the scale effects. Rystad's point about upfront costs, as DXC relays it, comes at the problem from the cost side. Read together, they suggest the first intelligent fields are most likely at operators with large fleets and the balance sheet to integrate them. Alnuaim's architecture looks more like a destination than a next purchase.

The engineer in the loop

The people closest to this change are the engineers. Writing in the Journal of Petroleum Technology's The Way Ahead in August 2025, Zainab Iyiola argued that the move to smart fields, driven by automation, AI and real-time data, requires petroleum engineers to master digital tools alongside their traditional skills. An intelligent field raises that requirement.

Al-Rbeawi's review sketches a starker version. It suggests the digital era may bring a sharp decline in the workforce and significant changes in job descriptions as human intervention in the production system is minimized. It also finds that the petroleum industry has not yet made the shift in mindset needed to embrace digitalization. Those two findings pull against each other usefully. The systems reduce hands-on intervention, yet getting them adopted depends on people changing how they work.

The book is written for petroleum, process, chemical and mechanical engineers, geologists, executives and policymakers. That breadth fits the problem. Pilots tend to belong to one team, while a field that decides across exploration, production and HSE needs every one of those groups to agree on what the system is allowed to do.

Where it could scale first

One clue sits in the 2018 Oil & Gas Journal analysis, which suggested that pooling data could be least contested in health, safety, environment and quality, where real-time monitoring and accurate compliance reporting were already spreading. Alnuaim's book gives emissions monitoring and HSE management their own coverage alongside production and reservoir work. Wiley frames its emissions strategies as supporting environmental compliance while maintaining production efficiency across field operations.

Vendor guidance makes the same pairing. Huawei's guide to oil and gas digital transformation describes analytics and IoT sensors that watch equipment and environmental conditions to predict problems while supporting compliance with regulations. When safety, emissions and maintenance draw on the same sensor stream, one shared data platform could serve several owners at once. That is the cross-team arrangement a pilot rarely needs and an intelligent field cannot do without.

If intelligent workflows break out of the pilot trap anywhere first, emissions and safety are the likely candidates: the data is easier to share and the compliance case is easier to make. One signal could be whether operators' next digital announcements move beyond pilots to field-wide emissions and maintenance systems. That shift would mean the industry is building toward the field Alnuaim describes.