Digital Oil And Gas

Large-scale capital projects sit at the heart of the oil and gas industry, and across all infrastructure sectors (power, petrochemicals, rail, water, telecoms). These projects require tight coordination of people, equipment, and timelines, often under pressure to deliver quickly and safely. Despite heavy investment in planning tools and scheduling systems, the day-to-day reality remains fragmented, with teams working across disconnected systems and making decisions in isolation. The issue is not a shortage of data, but a lack of connected context. Teams make decisions that work locally, but cannot see the ripple effects across the broader project. When disruptions occur, such as missing materials or shifting priorities, organizations fall back on manual replanning, pulling experts together to work through scenarios. The result is familiar: delays, rework, misalignment, and a persistent focus on reacting to problems rather than anticipating them. A new approach is emerging that focuses on execution intelligenc

One thing I've learned from producing lots of videos is that you can't really trust with your own eyes what you see presented to you anymore. Green screens, video editing, and AI are so good now, and so inexpensive, that anyone can create compellingvideo content of scenes that didn't happen in real life. As a result, I'm now very skeptical of claims that companies make that they don't offer to back up. Consider the 'organic' chicken at the butcher shop. How do you really know that the chicken has led an exemplary life free from chemicals, pesticides, and growth hormones? Carbon emissions fall into this category. Carbon dioxide, the invisible by-product of engine output and cement making, has become the poster child for energy transition. Today, producers and (some) consumers are expected to not only reduce carbon emissions but account for them with surgical precision. Yet we need to face an uncomfortable fact: carbon data lacks credibility. It's assembled from fragmented systems, manually reported and manipul

Industrial operations have spent decades optimizing for safety, reliability, and uptime. Control systems, sensors, and field equipment were designed to be stable and predictable, often isolated from the outside world. Cybersecurity, by contrast, evolved largely in IT environments, on a separate track, with different tools, assumptions, and incentives. That separation is no longer holding. Operational technology is becoming more connected, more digital, and more automated. Sensors stream data to the cloud, vendors require remote access, and AI-driven tools increasingly influence operational decisions. At the same time, cyber threats are moving faster, targeting physical systems with the potential for real-world safety and production impacts. One response is data meshing: combining traditional cyber telemetry with operational data such as vibration, maintenance history, and asset performance to create a richer, more reliable picture of what is really happening inside industrial environments. When these signals

Oil and gas companies generate enormous volumes of operational, geological, and production data. Despite this abundance, much of that data remains fragmented, inconsistent, and difficult to trust. Teams often spend a significant portion of their time preparing datasets rather than analyzing them. The result is delayed decision-making, inflated costs, and reduced operational agility. The core complication lies in data quality, data governance, and data readiness. Duplicate records, null values, drift, and structural inconsistencies make it difficult to move quickly from raw data to actionable insight. Asset teams frequently work semi-independently, each rebuilding transformation processes from scratch. Without reliable data foundations, scaling analytics, automation, or advanced modelling becomes difficult and costly.  In this episode, I'm in conversation with Shravan Gunda, CEO of Kaarvi, to discuss how a structured approach to data ingestion, anomaly detection, ETL transformation, and data lineage can reduce

Diesel generators have long been the default answer for powering upstream and midstream oil and gas sites. They are familiar, mobile, and deeply embedded in operating practice. Even in regions with abundant natural gas, operators often rely on fleets of diesel gens to run pumps, wireline units, and auxiliary equipment, treating gas as either waste or something to move to market while importing fuel to keep operations running. That status quo is becoming harder to defend. Diesel is expensive, noisy, logistically complex, and increasingly misaligned with emissions rules, carbon pricing, and community expectations. Operators face growing pressure to cut operating costs, reduce flaring, and lower emissions, while still maintaining reliability in the field. Yet infrastructure change moves slowly, driven more by habit and organizational friction than by technical limits. In this episode I'm speaking with Michael Lawson, Vice President of Business Development at Enterprise Group, about using stranded or low-value na

Data centers have shifted from supporting enterprise IT to running large parts of the digital economy. They now host AI model training, AI inference, and many critical digital services. As this shift accelerates, data centers are placing greater demands on power systems and operations teams. Managing energy use, uptime, and physical infrastructure has become central to how these facilities operate. The challenge is that data center growth is moving faster than the tools used to manage it. Many operators still depend on legacy monitoring systems and spreadsheets to understand complex environments. These approaches struggle with rising power density, sustainability targets, and security requirements. At the same time, data sovereignty and grid access are influencing where new infrastructure can be built. In this episode, I speak with Jad Jebara, CEO and co-founder of Hyperview, about how data center operations are changing. We discuss why cloud-native platforms are replacing legacy tools for managing power, fac

Oil and gas operators have always known that surplus equipment is part of doing business. Projects get cancelled. Long-lead items are over-ordered. Assets are parked in yards, warehouses, and sea cans "just in case." Over time, those decisions quietly turn into idle capital sitting on balance sheets, often forgotten until space runs out or write-downs loom. What's changed is the cost of ignoring it. Tight capital markets, tariffs, long lead times, and supply chain friction make surplus harder to justify. At the same time, operators face pressure to improve capital efficiency, demonstrate sustainability, and recover value from assets that still have useful life. Leaving equipment to rust is no longer neutral. It is a choice with a price tag. There is, however, a better way. With the right data, photos, documentation, and market reach, surplus assets can be exposed to real demand, often well beyond the local buyer network. Digital marketplaces create price discovery, expand the buyer pool across industries and

Upstream oil and gas companies continue to be very reliant on spreadsheets, legacy systems, and manual workflows to manage thousands of wells, compliance filings, and capital decisions. It's labor-intensive, error-prone, and slow. In light of global energy transition moves, operators are now facing ongoing margin pressure, a supply glut, tighter emissions regulations, and a shrinking pool of skilled labor. Digital solutions to soften the impacts of these pressures too often end up in "pilot hell", with limited results, stalled momentum, and no path to scale. Core systems like SCADA and ERP can't be easily adapted, and the early stage AI tools are often dismissed as too risky, inaccurate, or incompatible with real-world operations. Capital markets frown on any moves that sacrifice short term ROI for the possibility of better results later. New agentic AI tools look perfectly placed to address these constraints, but getting started is daunting. In this episode, I speak with AI strategy advisor Jeff McKee who ou

The energy industry is rapidly adopting artificial intelligence. Its promises of improved productivity, safety, and operational insight are too good to ignore. But these tools are only as good as the data that feeds them. And therein lies the problem: data across the sector is often incomplete, inconsistent, and scattered. This historic lack of discipline around data now has consequences. Poor data quality when used in AI undermines any project leveraging AI, exposing companies to greater audit risk, slowing down decision-making, and derailing expensive digital programs. Worse, AI tools amplify these flaws, making unreliable data more visible than ever. In one instance, a company's emissions breach turned out to be a data error that triggered fines, audits, and reputational damage. Companies struggle to respond, given the scale of the challenge. Getting control of enterprise data often feels like boiling the ocean, compounded by organizational practices that empower every business unit to do things their own

Have you ever given thought to the possibility that the suppliers of your core business technology, brands like Microsoft, Oracle, and SAP, might simply turn you off with no warning? It sounds fanciful, absurd, a black swan event so far beyond any reasonable risk matrix as to be unworthy of consideration. Yet it happened this year, 2025, to a major oil company in the world's most populous country. What was once unimaginable is now here, and with it, lessons for every oil and gas organization, indeed every company, about the new risks in the digital landscape. ⚒️ Additional Tools & Resources:

Most large enterprises rely on a handful of expansive technology platform solutions to run their business, and the most prominent and widely deployed in oil and gas is SAP. As I've outlined in my books, enterprise solutions such as SAP are also migrating to digital technologies, which triggers a major question: what is the optimal upgrade path for an SAP customer, or any enterprise technology, to adopt? Broadly speaking, there are two strategies to this vexxing question: a brownfield migration or "lift and shift" and a greenfield re-implementation or "start afresh". The problem is that brownfield upgrades rarely deliver any ROI while greenfield is seen as too risky, costly, or complex to execut, especially at the global scale one encounters among the oil and gas majors. Is there a third path, where organizations can preserve their historical data, stay compliant, and still deliver tangible business outcomes? In this episode I speak with Don Mahoney,  Global Head of Products and Innovation at SNP Group about a

Motors are the quiet workhorses of industry. They drive pumps, fans, compressors, and heaters, and they consume more than sixty percent of the power in most industrial operations. When operators need to control motor speed, they historically relied on mechanical adjustments or trial-and-error testing to keep processes stable and safe. As motors get larger and drive trains more complex, traditional testing approaches no longer work. Bringing every component together for a full string test adds months of delay and millions of dollars in logistics. The industry is conservative and reference driven, which makes it hard for operators to trust new configurations without seeing them proven first. This is where simulations shine. In this episode, I speak with Anand Jha, Vice President of Global Sales for ABB System Drive – Process Industries, about simulation twins that let operators model the entire system before it's built. These "virtual plants" replicate the grid, the drives, the motors, and the compressor. With

Canada's energy sector has long struggled with low productivity on the front line, as indeed the entire Canadian economy. Despite heroic efforts by tradespeople, their effectiveness is hamstrung by badly dated processes, old disconnected systems, and paper-based workflows. The problem isn't the workers. It's that they're too often sent out with the wrong drawings, the wrong tools, the wrong permits, or even to the wrong location. Multiply that by a hundreds of thousands of jobs, and you've got a national productivity drag. One company, MSCP Heat Management Solutions, set out to build a completely paperless, digitally connected operation, starting at the job site. By linking trades, materials, safety workflows, and quality processes into a seamless digital flow, MSCP has achieved something few believe is possible: cutting required manpower by 40% to 45% on major jobs. In this episode, I speak with Chris Maki, MSCP's founder and CEO, on lessons he took from Fort McMurray's distant job sites and turned them into

Oil and gas companies are finally confronting the huge communications and stakeholder challenge they face with their asset owners and stakeholders. Production assets such as oil and gas wells almost always have many part owners (land owners, JV partners, interest-holders, trusts, first nations tribes). Managing these hundreds or thousands of parties across tens of thousands of wells is really demanding. These relationships are complex, sensitive and often layered with legacy ownership structures, royalty flows, regulatory demands and reputational risk. This is much more than an operational hassle. It's a brand and trust issue. When owners ask: "Where's my check?" or "What's this charge on my statement?", slow responses can damage goodwill, complicate legal compliance and create unnecessary risk. Companies try to manage but have often saddled themselves with dueling systems in the land department, operations, legal, and corporate communications.  I was delighted to learn about Firm App, a clever digital solut

Latin America is entering a period of rapid economic growth, urbanization, and industrial expansion. Unlike North America and Europe, where primary energy demand has been flat for more than a decade, the region's energy consumption is rising sharply. A young, increasingly urban population is pushing electricity and fuel demand higher, placing new pressure on infrastructure and supply. This demand surge is colliding with a second global shift: the explosive rise of AI and hyperscaler data centers. These digital megaprojects require enormous volumes of power, often sourced from natural gas, renewables, or hybrid portfolios. Latin America, with its prolific basins, high solar irradiation, hydropower potential, and significant natural gas reserves, is strategically positioned to supply this emerging demand. Yet the region remains capital constrained, and traditional financing channels have not kept up. New financial tools, such as tokenized assets, sustainable linked bonds, and AI-enabled verification, offer a pa

Electricity powers nearly everything in oil and gas, from pumps and motors, to compressors and digital systems. But while production engineers obsess over volumes and temperatures, the quality of the electricity driving their systems is often overlooked. Most teams only discover power issues after equipment fails, leading to unplanned downtime and costly repairs. Unfortunately, traditional power quality meters and relay monitors don't catch the early warning signs. They lack resolution, require manual data pulls, and don't provide actionable insights. Worse still, the frontline technicians tasked with using these tools rarely have time to analyze the data. As the industry electrifies, and hyperscalers start co-locating AI data centers with natural gas fields, the demand for clean, reliable power is rising fast, and the risks of ignoring it are getting more expensive by the day. In this episode, I speak with Denis Kouroussis why "ghost electrical problems" aren't ghosts at all, but just data you haven't seen y

In all my years of experience in energy, I rarely worked in pure regulatory areas, but regulations loomed large over everything I touched. The energy sector is very highly regulated, and for very good reasons. From environmental standards to carbon pricing, energy companies are held to a high standard and must demonstrate that compliance to operate locally, regionally, and globally. The regulatory landscape is highly dynamic and under constant change. New regulatory frameworks emerging from Europe and the United States will reshape how energy companies, particularly in North America, do business domestically, and abroad. Three new frameworks—the European Union's Deforestation Regulation (EUDR) and Carbon Border Adjustment Mechanism (CBAM), as well as the U.S. Inflation Reduction Act (IRA)—will impact North American energy companies that export to Europe. If you're an energy executive working in exportable energy, such as LNG, oil, methanol, hydrogen, or refined petroleum, you might find this post of interest.

In the UK oil and gas sector, the record on major accidents looks encouraging. Serious incidents are very rare, and the industry appears to be operating safely. Beneath the surface, the data tell a different story. One-third of safety inspections fall below the legal standard, and more than half of process-safety professionals are expected to retire within the next decade. At the same time, ageing assets, shrinking budgets, and weaker regulatory oversight are straining existing  safety systems. Operators must sustain high safety performance when experience is walking out the door, equipment is ageing, and new technologies are flooding the scene faster than they can be tested.  In this episode I speak with David Jamieson of Salus Technical, on making process safety straightforward. We explore why process safety remains an invisible threat, how AI can capture the wisdom of the best engineers, and what it takes to keep legacy assets safe as they wind down.

Unconventional oil and gas is massive. Every year, over $100 billion is poured into steel, sand, and water just in Canada and the US. Yet, most of the planning behind these extraordinary investments still runs on Excel spreadsheets. Spreadsheets were fine in the 90s, but today they struggle to handle the complex interdependencies and real-world constraints of modern tight plays. The hidden cost to the industry is huge. Expensive inefficiencies, wasted capital, and missed opportunities to improve well design and execution. Other industries abandoned spreadsheets long ago when margins got thin, but oil and gas clings to them, and keeps burning the billions. My guest in this episode is Sean Hervo, a former Shell upstream engineer, who saw firsthand how spreadsheets literally strangle unconventional well planning. He co-founded PrePad, an innovative digital solution, that replaces bulkly and clunky spreadsheets with digital simulation and optimization tools, giving operators the ability to make faster, smarter, m

Oil and gas operations rely on heavy machinery and equipment that perform critical tasks, yet most of this equipment remains disconnected from the digital landscape of cloud computing, analytics, and autonomy. This lack of connectivity leaves operators with higher costs, inefficient maintenance, and limited visibility into how their assets are really performing. The traditional approach to equipment design is no longer enough. Operators face pressure to improve efficiency, reduce emissions, and cut costs, but without better data and smarter tools, these goals remain out of reach. The industry cannot afford to keep treating its infrastructure as “dumb metal.” You might think that only big equipment suppliers can make smart metal, but IJACK, a Canadian equipment manufacturer, is proving otherwise. By embedding remote monitoring, cloud connectivity, and AI-driven analytics into its products, the company enables operators to troubleshoot issues without rolling a truck, optimize performance across entire fleets, a