For a long time, non-operated joint venture (NOJV) management had a reputation problem. Not because they performed poorly, but because of how people thought about the function: a passive income stream, managed by accountants, funded by checks, ignored until a JIB arrived or an AFE deadline slipped by. However, serious non-op teams have been doing the hard work for years, it just rarely made the highlight reel to the market.
At Enverus EVOLVE 2026, Jeb Burleson, Director of Product at Enverus, hosted a panel on the operator/non-operator relationship by observing that the NOJV space is hotter than it’s ever been as an investment vehicle. The panelists described the rigorous technical work that their teams do every day, and why it is getting its share of the limelight recently.
Thomas Fitz
CFO Brigham Exploration
Andrew Armpriester
General Manager North America Non-Operated Joint Venture and Royalty Chevron
Scott Rice
Managing Partner & COORiverbend Energy Company
Aaron Tenenholz
VP of Land & BD Fortuna OpCo
How The Experts Approach Non-Operated Joint Venture Management
Scott Rice of Riverbend Energy has done extensive technical work with his team, arriving at operator meetings with independent estimated ultimate recovery (EUR) figures and completion design opinions, so that they can be a sophisticated part of the stack, rather than capital sitting quietly in the well. His team is known for bringing a strategic lens and a valued perspective to the table with their partners.
Aaron Tenenholz of Fortuna OpCo walked through six distinct deal types his team navigates, from white space leasing to wellbore market transactions, each with its own analytical demands and its own risk profile.
Andy Armpriester of Chevron’s North America NOJV and Royalty group described managing roughly 20,000 wells with a team of about ten people, synthesizing data from hundreds of operators who each format their information differently, use different nomenclature for the same formations, and in some cases still send AFEs by mail.
Thomas Fitz, CFO of Brigham Exploration, mentioned that they have a partner that wants feedback from their no-op partners to guide their decisions through benchmarking their cost data in the Permian to understand what they’re doing right and wrong, what other costs and mechanisms other folks are seeing. He emphasized that “creating that two-way street is going to be super helpful.”
The Market Has Caught On: Why Managing Non-Operated Joint Ventures Is Becoming More Strategic
Strategic joint ventures between E&P operators and private equity portfolio companies are increasing, driven partly by the desire to share costs on large development programs and partly by the consolidation wave reshaping which operators control which acreage. Northern Oil and Gas completed over 40 transactions in Q1 2026 alone and is currently evaluating more than $10 billion in asset packages. PE-backed non-ops face firm timelines, defined return hurdles, and reporting obligations that turn every consent decision into a capital allocation decision with downstream consequences for the fund.
Why NOJV Data Infrastructure Has Not Kept Pace With Strategic Ambition
Tenenholz described the standardization problem at EVOLVE: “There isn’t any uniformity. Every JIB looks different. Every division order looks different. Every well proposal looks different.” His team at Fortuna manages $1.4 billion in assets under management with a lean staff, where every hour spent on manual data processing is an hour not spent on the decisions that actually drive returns. Armpriester noted that even when Chevron receives data cleanly, they still have to translate it into their internal nomenclature before it’s usable. Thomas Fitz of Brigham Exploration was direct about where his team’s energy is going first: “The cleanliness of the data, the focus on the process; we think that’s the foundation that needs to be set.”
What The Leading Edge Of NOJV Management Looks Like Today
Riverbend has AI tools in the hands of every employee, built on a data foundation two decades in the making. NOG evaluates every development package against analyst-verified inventory and their own proprietary type curves before bidding, so they’re never working from seller representations alone. Some operator OBO teams now use GPS rig tracking to monitor non-op partner activity weeks before the JIB arrives. The common thread isn’t technology for its own sake. It’s having the right information at the moment of decision, rather than after it.
The teams without that infrastructure aren’t just slower. They’re making consent decisions, capital allocations, and operator evaluations on information that’s already stale.
Enverus Benchmark & Optimize solutions are built for exactly this gap. Real-time AFE benchmarking means you’re not consenting blind. Integrated cost analytics surface the outliers your lean team would otherwise catch weeks later, if at all. Portfolio forecasting puts cash flow timing and revenue impact in front of you before capital commits, not after. And unlike a consultant’s one-time analysis, the data updates continuously, so your team is always working from current market conditions, not last quarter’s snapshot.
Key Takeaways for Non-Operated Joint Venture Management:
The non-operated working interest is now a strategic function, not a passive one. Leading non-op teams run independent EUR models, track operator performance across multiple cycles, monitor partner activity in near real time, and manage capital allocation.
The operational infrastructure hasn’t kept pace with the strategic ambition. Disparate AFE formats, inconsistent JIB nomenclature, and reactive data flows mean that even sophisticated non-op teams spend significant time on manual processing rather than decision-making.
The gap between leading and lagging non-op operations is compounding. Teams using real-time activity monitoring, and analyst-verified inventory data are making faster, more defensible decisions than peers still relying on operator-provided information and spreadsheet models.
Automated decline curve analysis has changed how engineers handle large inventories. What used to take weeks of manual curve-fitting can now run across thousands of wells in a fraction of the time. Most teams have adopted it. Most teams have also run into the same wall.
The forecast comes back. The curves look reasonable. And then the question no one wants to answer out loud: do we actually trust these enough to use them?
For reserves reporting, the answer has to be yes or no, not “probably.” For an acquisition evaluation, a wrong curve on a key well isn’t a rounding error. For development planning, automated forecasts that haven’t been reviewed are often treated as a starting point rather than a deliverable, which means someone still has to do the work of validating and preparing them before they go anywhere useful.
That gap between generated and trusted is where most of the time goes.
Why Automated Oil and Gas Production Forecasts Still Need Expert Review
Automated forecasting solves a volume problem. A reservoir engineer (RE) or senior consultant who once spent hours building individual decline curves can now evaluate a hundred-well inventory in the time it used to take to do ten. That’s real. The problem is that review doesn’t compress the same way.
Once curves are generated, someone still needs to look at them. Not every well, maybe, but the ones with limited production history, anomalous behavior, or high economic weight all get scrutiny. That scrutiny takes time. And when review lives in a separate tool from generation, you lose more time in the handoff: exporting parameters, reformatting files, rebuilding what the automated system already calculated.
For consultants running evaluations across multiple clients and asset types, the handoff friction compounds fast. A project that involves fetching automated forecasts, adjusting a subset of curves, and then exporting to ARIES or PHDWin can easily add days of prep work that has nothing to do with engineering judgment. It’s data handling. And most of it is avoidable.
Where Production Forecast Review Slows Down
Use inventory filters and parameter distributions to identify wells that require closer forecast review
Talk to engineers who work with automated forecasts regularly and the complaints are consistent. Not about the quality of the curves, most of the time, but about what happens after they’re generated.
Wells with limited production history often don’t receive a curve at all, which means someone has to build one manually and track it separately. Outliers and segmentation issues show up across the inventory and require individual attention. When you want to adjust decline parameters, such as b-factor ranges or abandonment rates, doing it well means having controls that let you apply changes across groups of wells, not just one at a time.
And then there’s the export. ARIES, PHDWin, and Valnav each have their own import formats. Manually converting decline curve parameters to match those formats is exactly the kind of work that shouldn’t be done by a senior engineer or experienced consultant. It doesn’t require expertise. It just requires time, and it creates opportunity for error.
Keep Forecast Generation, Review and Adjustment in One Workflow
Compare production history with automated forecast fits and adjust decline assumptions within the same review workflow.
The engineers and consultants who’ve moved past these bottlenecks have done it by keeping review and generation in the same environment. When automated forecasts feed directly into a tool where you can validate curves visually, adjust parameters individually or across groups, add curves to wells that didn’t receive one, and export in ready-to-use formats for reserves software, the workflow changes.
You’re not switching tools to do review. You’re not reformatting exports by hand. You’re spending your time on the engineering decisions that actually require your judgment: which curves need adjustment, what the b-factor constraints should be for this asset type, whether a well with six months of production history deserves a conservative or aggressive decline assumption.
That’s the work that separates a thorough evaluation from a rushed one. The rest is friction.
Why a Connected Forecast Workflow Matters for Consultants
For independent consultants and advisory firms, the economics of every engagement depend on how long it takes to produce something defensible. Keeping review and generation in one place doesn’t just save time on one project. It changes what’s possible on the next one.
Being able to fetch automated curves, review and adjust them without switching tools, and export directly to a client’s reserves software compresses the delivery timeline without compressing the quality of the work. It also makes the review process more auditable. When you can show a client exactly which curves were adjusted and why, and back it up with a visual fit from the data viewer, the forecast carries more weight. That matters for acquisitions. It matters for reserves submissions. It matters any time the number you’re signing off on has real consequences.
The Enegineering Judgement Still Has to Come from You
Automated forecasting isn’t going away, and neither is the need to validate what it produces. The question is how much of your time goes into engineering and how much goes into the data handling that surrounds it.
If your current workflow involves multiple tools, manual exports, or rebuilding parameters that an automated system already calculated, it’s worth taking a closer look at what a better setup could do for your team.
An authorization for expenditure (AFE) lands in your inbox on a Tuesday. The well looks reasonable. You run the economics, the net present value barely clears your hurdle rate, and after a week of back-and-forth you non-consent. The operator moves forward without you.
What you didn’t know: they built the number to get exactly that outcome.
AFE inflation is one of the least-discussed practices in the non-operated joint venture (NOJV) space. It’s not illegal. It sits in a gray area that experienced operators know well and non-operators rarely talk about openly. The mechanics are simple: an operator submits an AFE priced higher than the expected actual cost, knowing the inflated figure will push marginal non-consenting partners out of the well. Under most joint operating agreements (JOAs), non-consent triggers a penalty of 200 to 500 percent, meaning the operator recovers multiples of the non-consenting party’s cost share before that party sees a dollar of production revenue. A well that costs $12 million gets proposed at $13.5 million. The economics look marginal. You pass. The operator captures your interest.
Enverus hosted a panel of experts at the Enverus EVOLVE 2026 conference, where they discussed operators who make a habit of it, and non-ops who’ve learned to read the pattern over years of watching AFE estimates versus actual costs. Some operators consistently hit what they say. Others consistently don’t. After enough cycles, you start to know which is which.
It only works when you’ve spent a decade watching the same operators in the same basin. It falls apart the moment you enter a new play, inherit assets through an acquisition, or face a spike in AFE volume from an operator you haven’t worked with before. Right now, all three of those situations are becoming more common. Industry consolidation is accelerating, non-op portfolios are growing through M&A, and the wellbore market is compressing decision windows to days rather than weeks. Northern Oil and Gas set a quarterly record with 41 ground game transactions in the first quarter of 2026, actively evaluating more than $10 billion in large asset packages across eight deals. At that pace, relationship-based knowledge can’t keep up.
Riverbend Energy has built something closer to a solution: a 23-year AFE database tracking thousands of wells across multiple market cycles. History informs every consent decision, letting the team see patterns that a single deal or a single operator conversation would never be able to reveal. But Riverbend built that over two decades. It’s not something you recreate in a new basin on a short timeline.
Closing the Gap: Independent AFE Cost Benchmarking
The only real defenseis independent cost benchmarking at the moment the AFE arrives, not after the fact. That means comparing an incoming AFE against what similar wells actually cost to drill and complete in the same formation, by the same operator, using normalized cost data derived from actual joint interest billing (JIB) records across the industry. When you have that context, a $13.5 million AFE for a $12 million well doesn’t just feel high. It shows up as an outlier with a number attached to it.
That changes the conversation. Instead of non-consenting because the economics looked marginal, you can go back to the operator with data. That’s a different negotiation entirely, and most operators who’ve been inflating AFEs quietly are counting on the fact that you don’t have it.
The non-op space is professionalizing fast. The teams that will outperform over the next cycle aren’t necessarily the ones with the best relationships. They’re the ones who show up to the table with something the operator didn’t expect: a benchmark they can’t argue with.
The operators who still treat non-ops as check registers are increasingly working with partners who walk in with better benchmarks than expected. The non-op teams still running everything through manual spreadsheets are competing against peers who can evaluate an AFE in hours, flag a cost outlier before consenting, and monitor what their operators are doing in near real time. That gap compounds over a fund cycle.
The EVOLVE panel closed on something worth sitting with: even as technology makes more of this work faster and more systematic, trust and relationships remain the differentiator that no amount of data fully replaces. But to be taken seriously as a partner rather than a capital provider, you have to earn a seat at the table first. Showing up with independent analysis, a track record of fast decisions, and data that holds up to scrutiny is how you get there.
The Market Has Decided Non-Op Is A Serious Investment Vehicle
The question is whether the operational infrastructure matches that ambition.
AFE inflation is a known practice, not an edge case. Operators sometimes submit cost estimates above expected actuals to induce non-consent, capturing the non-consenting party’s interest under JOA penalty provisions that typically run 200–500% of cost recovery. Non-ops without independent benchmarking data have no systematic way to identify it at the point of decision.
Institutional knowledge doesn’t scale. Experienced non-op teams build AFE-versus-actual track records over years in a single basin. That knowledge breaks down when entering a new play, inheriting assets through M&A, or evaluating operators outside a team’s core relationships, exactly the situations most common in today’s consolidating market.
Independent cost benchmarking at the moment of AFE receipt is the only reliable defense. Comparing an incoming AFE against normalized drilling and completion cost data from actual JIB records across comparable wells, operators, and formations turns an inflation pattern from a gut feeling into a number. That changes the consent decision, and the negotiation.
CALGARY, Alberta (July 15, 2026) — Enverus Intelligence® Research (EIR), a subsidiary of Enverus, is releasing a new report that analyzes the widening gap between the cost to build new combined-cycle gas turbines (CCGT) and the price paid for existing gas-fired power assets.
EIR’s analysis of 76 combined-cycle gas turbine projects with publicly disclosed capital costs and commercial operation dates from 2014 to 2033 found that new gas-fired power generation costs have roughly doubled, rising from an average of about $0.9 million/MW for plants online before 2023 to about $2.0 million/MW for the post-2027 cohort. At the same time, operating gas-fired power plant M&A multiples have also doubled, from about $0.5 million/MW before 2025 to about $1.0 million/MW last year.
The result is a replacement-cost wedge of roughly $1.0 million/MW, with buyers of existing assets paying about 50 cents on the dollar compared with the cost to build new capacity. EIR points to this explanation as to why capital allocators have favored acquisitions over greenfield merchant builds, particularly while current energy and capacity prices remain below levels needed to finance new construction.
“Newbuild CCGT costs have moved high enough that the economics increasingly favor buying existing, grid-connected capacity over building new merchant plants. That replacement-cost wedge is now a central factor in power-sector capital allocation, supporting incumbent asset values while limiting the near-term on-grid supply response,” said report author and EIR analyst Brynna Foley.
Key takeaways:
Costs for new gas-fired power generation have roughly doubled, from about $0.9 million/MW for plants online before 2023 to about $2.0 million/MW for the post-2027 cohort.
Operating gas-fired power plant M&A multiples have also doubled, from about $0.5 million/MW before 2025 to about $1.0 million/MW last year.
At about $2.2 million/MW, greenfield CCGTs require roughly $500/MW-day capacity payments in PJM Interconnection or about a $70/MWh power purchase agreement in ERCOT to be financeable.
EIR views the wedge as a structural floor under incumbent independent power producer valuations and a tailwind for capacity and scarcity pricing, while noting risks from turbine price normalization and slower-than-expected data center demand growth.
Figure 3 | Newbuild Cost vs. Gas-Fired Power M&A Over Time
EIR’s analysis pulls from a variety of products including Enverus ONE.
You must be an Enverus Intelligence® Research subscriber to access this report.
About Enverus Intelligence® Research Enverus Intelligence ® | Research, Inc. (EIR) is a subsidiary of Enverus that publishes energy-sector research focused on the oil, natural gas, power and renewable industries. EIR publishes reports including asset and company valuations, resource assessments, technical evaluations and macro-economic forecasts; and helps make intelligent connections for energy industry participants, service companies and capital providers worldwide. Enverus is the most trusted, energy-dedicated SaaS company, with a platform built to create value from generative AI, offering real-time access to analytics, insights and benchmark cost and revenue data sourced from our partnerships to 95% of U.S. energy producers, and more than 40,000 suppliers. Learn more at Enverus.com.
This is the second installment in a series on AI adoption in energy. If you missed the first piece, start here.
The question I hear most often from energy teams evaluating AI: “How do we know it’s right?”
It deserves a straight answer. Here’s mine.
Deterministic AI: The Tools Energy Teams Already Trust
The models engineers and planners have built careers on are deterministic. Same inputs, same output, every time. A decline curve model returns the same estimated ultimate recovery whether you run it on Monday or Friday. An economic simulator returns the same net present value given the same assumptions. You can trace every number back to an equation, defend it in a meeting and hand it to your CFO with confidence.
When your name is on a recommendation, you need to be able to show your work.
Deterministic tools make that possible by design.
How Probabilistic AI Works
AI models don’t calculate. They pattern-match.
A large language model processes an enormous volume of examples (measured in billions) and learns the statistical relationships between them. When it produces an output, it isn’t running a set equation. It’s returning the most likely response given everything it was trained on. Ask it the same question twice and you may get slightly different answers. Ask it about something at the edge of its training data and it fills the gap with something plausible that isn’t necessarily true.
That’s where hallucinations come from. The LLM isn’t malfunctioning. It’s doing what it was built to do: provide an answer based on probability derived from a vast but incomplete corpus of knowledge. It has no internal mechanism to flag the error because it doesn’t know what it doesn’t know.
Which makes it all the more important to understand what you’re working with when using AI, and it’s why every major AI company has a disclaimer telling you their LLM makes mistakes and you should double-check its work.
AI Explainability: Can the Tool Show Its Work?
Can the tool show you how it arrived at its output?
The actual data sources, the assumptions made, the places where the model is working near the edge of what it knows. If a vendor can’t show you that, they’re asking you to trust a black box. In energy, where a wrong answer can have far-reaching consequences, that’s not a standard worth accepting.
Systems returning answers without showing their work erode trust faster than they build it. Business decisions require traceability, not black-box outputs. As one supermajor put it: “An automated answer that’s wrong is worse than no automation at all.”
The gap between a generic model and one built on 25 years of structured energy intelligence isn’t just about accuracy. It’s about what the model was trained to know. A model that has processed 7 million wells, 350 million land records and the actual data structures and workflows of energy work isn’t pattern-matching across the internet. It’s pattern-matching across the decisions this industry has been making for decades. That narrows the hallucination surface significantly and gives the reviewer something real to check against.
AI doesn’t have to be unverifiable. The governance around the tool has to be built to compensate for what the model can’t guarantee on its own.
There’s a version of a good day in land work that most landmen know but rarely get. You’re working on a project you understand, in counties you know, with records that cooperate. The chain comes together clean, you deliver a run sheet your client can act on, and you have enough runway left in the week to take the next call.
Most days aren’t like that. Most days involve at least one document that’s harder than it should be, at least one county where the search doesn’t behave the way you expect, and a timeline that doesn’t leave much room for any of it. The title work itself, the judgment calls, the interpretation, the professional read on what a chain actually says, that part can’t be shortcut. But a lot of what surrounds it doesn’t have to eat your day. And when you start getting that time back across a full project, you start fitting more work into the same quarter without dropping your standard on any of it.
When the search doesn’t give you the whole picture
Most county clerk portals return exactly what matches the spelling you typed. That works when the records are clean, and they often aren’t. A grantor whose name was recorded in three different ways over a 150-year chain won’t surface in a single exact-match search, and neither will an instrument a clerk misindexed forty years ago. You finish the run, close out the county, and there’s still something in the back of your mind about whether you got everything.
That uncertainty has a real cost. You spend extra time double-checking work you’ve already done, pulling the index book to cross-reference what the portal returned, and building a buffer into your turnaround because you know the search isn’t airtight. None of that shows up on the run sheet, but all of it shows up in your day. Search that catches name variations, partial matches, and common clerk errors means you’re not building that buffer in anymore. You pull what’s there, you make the call, and you move to the next instrument knowing the search did its job.
What takes an hour doesn’t have to
Take one difficult document and multiply it by the size of a real acquisition project. Forty instruments, sixty, sometimes more, across multiple abstracts and multiple counties: deeds, leases, mortgages, probate orders. Some typed, some handwritten cursive from the 1880s that takes real effort just to get through. Each one has to be opened and read enough to know what it is before any interpretation even starts, and on a big project that assessment alone is where hours disappear.
AI built specifically for land documents handles that first read. You open a deed of trust and instead of working through it page by page to find what matters, you get a read in seconds covering the parties, the land description, the key provisions, and anything worth flagging. If you need to go deeper on a specific clause, you can ask a direct question and get back the answer with the exact page it came from. Handwritten documents are their own problem. A cursive deed from the 1800s can take an hour to work through if the clerk had a difficult hand, and that same document transcribes in seconds with AI trained on historical land instruments. That hour goes back into your day, into the instruments that need careful handling, or into the next project waiting behind this one. Sixty instruments, five minutes recovered per document: that’s five hours back on a single project, which is the difference between a project that fits your week and one that bleeds into the next.
Good work in a county should count the next time you’re there
Most land services firms have worked in the same counties more than once, and the second project in a county you know should be faster than the first. Usually it isn’t, because the work from the first project isn’t organized in a way anyone can build on. Run sheets live in someone’s personal files, notes from the prior search aren’t accessible, and if the person who did the first project isn’t available for the second, you’re starting over in a county where you’ve already done the work.
When run sheets, search history, and document notes are retained at the firm level and tied to the county they came from, the second project there starts ahead of where the first one did, and the third project more so. You’re not re-running the title you’ve already run. You’re building on it, and that compounds over a year of work in a way that shows up in how many projects you can take on and how quickly you can staff them.
More projects. The same standard on every one
The search uncertainty, the document backlog, the work that doesn’t carry forward: none of these are problems most landmen spend much time thinking about because they’ve always been part of the job. You build in the buffer, you do the extra checks, you start fresh in a familiar county because that’s what the work has always required.
When the search is airtight, when document review isn’t eating half your project time, and when prior work in a county is accessible, more fits into a quarter. Not because the title work got easier, but because less of your day disappears before you get to it. That’s what landmen who have brought AI into their workflow are finding. The work is the same, and there’s just a lot more room for it.
CALGARY, Alberta (July 14, 2026) — Enverus Intelligence® Research (EIR), a subsidiary of Enverus, the leading energy data analytics platform, is releasing a new analysis on how Asian LNG markets are responding to the loss of Qatari supply, including the role of replacement cargoes, fuel switching and demand rationing.
The report finds that Asia has primarily managed the LNG disruption by sourcing alternative cargoes rather than through widespread demand destruction. EIR estimates roughly 5-6 Bcf/d of the shortfall was replaced by higher-cost LNG sources, while about 2-3 Bcf/d came from demand rationing and fuel switching, making the adjustment largely price-driven for major buyers.
According to EIR, demand rationing has been concentrated among more price-sensitive importers, while larger Northeast Asian buyers continued to consume gas and pay higher prices for replacement cargoes. The report notes that weather-adjusted generation across major buyers showed no statistically significant weakness, while exposed buyers such as Bangladesh showed clear shortfalls of about 7%-11% below weather-adjusted demand.
EIR also sees bullish risk into 3Q26, when cooling demand peaks and inventories are thinner. The report’s 2026 price outlook is $18/MMBtu for JKM and $16/MMBtu for TTF, above forward strips of roughly $16/MMBtu and $14/MMBtu, respectively.
“Asia’s response to the loss of Qatari LNG has been less about broad demand collapse and more about who can afford to keep buying replacement supply. The large buyers have absorbed the shock through price so far, which means the market’s real test comes as cooling demand rises and supply constraints persist into the third quarter,” said Josephine Mills, senior analyst at EIR.
Key takeaways:
Asia replaced roughly 5-6 Bcf/d of lost Qatari LNG through alternative, higher-cost LNG sources, while demand rationing and fuel switching accounted for about 2-3 Bcf/d.
EIR estimates the March 4 effective closure of the Strait of Hormuz removed about 10 Bcf/d of Qatari LNG from global balances, while damage at Ras Laffan affected about 2 Bcf/d of capacity.
Major Asian buyers largely continued consuming gas, while demand rationing appeared more clearly among price-sensitive importers.
Bangladesh showed weather-adjusted output shortfalls of about 11% in March and 7% in April, while major buyers’ deviations remained within ordinary model noise.
EIR’s 2026 outlook is $18/MMBtu for JKM and $16/MMBtu for TTF, above forward strips of approximately $16/MMBtu and $14/MMBtu.
EIR’s analysis pulls from a variety of products including Enverus ONE.
You must be an Enverus Intelligence® Research subscriber to access this report.
EIR research reports cannot be distributed to members of the media without a scheduled interview. Journalists interested in learning more about this analysis are encouraged to use our Request Media Interview button to schedule a time to meet with one of our expert analysts, who can provide context, insight, and deeper discussion of the findings.
About Enverus Intelligence® Research Enverus Intelligence ® | Research, Inc. (EIR) is a subsidiary of Enverus that publishes energy-sector research focused on the oil, natural gas, power and renewable industries. EIR publishes reports including asset and company valuations, resource assessments, technical evaluations and macro-economic forecasts; and helps make intelligent connections for energy industry participants, service companies and capital providers worldwide. Enverus is the most trusted, energy-dedicated SaaS company, with a platform built to create value from generative AI, offering real-time access to analytics, insights and benchmark cost and revenue data sourced from our partnerships to 95% of U.S. energy producers, and more than 40,000 suppliers. Learn more at Enverus.com.
National Grid Ventures’ (NGV) $1.75 billion investment for a 35% stake in Joulent, announced July 1, shows what it now takes to justify building new gas rather than buying it. As we laid out in EIR’s latest report, Cheaper to Buy Than Build, greenfield combined-cycle economics have deteriorated to the point where a merchant build makes little sense. Post-2024 CCGTs now cluster near ~$2.0 million/MW to construct (Figure 1), while operating gas plants change hands for about $1.0 million/MW. That ~$1 million/MW replacement-cost wedge means acquirers are paying roughly 50 cents on the dollar versus building, which is why investors have overwhelmingly chosen M&A over greenfield.
The exceptions to that rule are joint ventures that spreads capital and risk, subsidized debt, a creditworthy long-term offtaker, or capacity prices high enough to finance new entry (At the Cap, Below CONE | Why PJM’s Capacity Market Needs a Reset). The Joulent deal checks two of those boxes. Joulent’s flagship 2.67 GW Project Kilby in West Texas is a 50/50 venture with Chevron, anchored by a 20-year power purchase agreement with a Microsoft-operated data center, with first power targeted for 2028 on secured GE Vernova turbines. A joint venture backed by a strong, long-dated PPA is a combination necessary to underwrite a greenfield build in ERCOT, where a merchant plant would otherwise need, at a 2.5MM/MW capital cost, roughly a $70/MWh contract to pencil, above current clearing levels. By buying into a partnership rather than building the plant itself, and by pairing a hyperscaler offtaker with a JV partner, the deal delivers a contracted, de-risked infrastructure return.
This blog offers just a glimpse of the powerful analysis Energy Transition Research delivers on the trending themes. Don’t miss the full picture.
Cheaper to Buy Than Build | The Replacement-Cost Wedge – Enverus Intelligence® Research analyzes 76 CCGTs with disclosed capital costs and commercial operation dates from 2014-33, finding that newbuild costs have roughly doubled to ~$2.0 million/MW for the post-2024 cohort while operating gas M&A clears near ~$1.0 million/MW. We read the ~$1 million/MW wedge as a structural floor under incumbent IPP valuations and a tailwind for capacity and scarcity pricing.
DID YOU KNOW?
Project Kilby’s 2.67 GW nameplate rivals the entire installed power capacity of some U.S. states including Vermont, Rhode Island, and Delaware.
1. Why is building new gas generation becoming harder to justify?
New combined-cycle gas plants have become significantly more expensive to build, with costs approaching $2 million/MW or more, while existing gas plants can be acquired for roughly $1 million/MW. This creates a substantial cost advantage for buyers, which is why investors have generally favored acquisitions over greenfield development.
2. What makes the Joulent project an exception to the “buy, don’t build” trend?
The project combines two critical ingredients that reduce risk: a joint venture structure that shares capital costs and development risk, and a 20-year power purchase agreement (PPA) with a Microsoft-operated data center. Together, these provide the financial stability needed to support a new-build project that would otherwise struggle to compete in today’s market.
3. What does the National Grid investment signal about future gas development?
The deal highlights that new gas projects are most likely to move forward when they have strong contractual backing and risk-sharing partners. Rather than relying on merchant market revenues alone, developers increasingly need long-term offtake agreements and strategic partnerships to justify new generation investments.
About Enverus Intelligence® | Research
Enverus Intelligence® | Research, Inc. (EIR) is a subsidiary of Enverus that publishes energy-sector research focused on the oil, natural gas, power and renewable industries. EIR publishes reports including asset and company valuations, resource assessments, technical evaluations, and macro-economic forecasts and helps make intelligent connections for energy industry participants, service companies, and capital providers worldwide. See additional disclosures here.
In our previous post, we explored why power and renewable M&A has become increasingly attractive for many developers. Load growth is accelerating, power prices are strengthening, and corporate demand remains robust. At the same time, developers are placing a growing premium on speed to market.
Most buyers are looking at many of the same assets. What increasingly separates successful acquirers is not access to deals, but the ability to identify value faster than the competition. In that environment, screening is no longer an administrative step in the M&A process. It has become a strategic capability.
This post walks through how that capability works in practice, moving from a nationwide market view to a prioritized shortlist with defensible economics behind each opportunity.
The workflow follows four stages:
Market → Asset → Location → Economics
Each stage narrows the field and increases conviction.
Start with a hypothesis, not an asset
Many M&A processes begin with an asset that is already on the market. The strongest acquisition strategies often begin somewhere else.
Rather than asking “which projects are available,” leading acquirers ask:
Which markets are benefiting from sustained load growth?
Where are supply and demand fundamentals tightening?
Which regions are attracting infrastructure investment?
Where is future power demand likely to concentrate?
Once those questions are answered, the field gets much smaller.
This matters because asset value is increasingly shaped by what’s happening around the asset, not just the asset itself. Two projects with similar operating characteristics can have very different long-term outlooks depending on where they sit relative to growing load centers, transmission infrastructure, and future development activity. The goal is not simply to find assets. It is to find assets positioned to benefit from the strongest underlying fundamentals.
In practice, four variables tend to separate the strongest markets from the rest:
Load growth trajectory: Is power demand in this region accelerating, flat, or declining? Where are data centers, EV fleet charging, and industrial loads being built?
Forward LMP levels: What are power prices doing at the zonal and nodal level, and where are they headed through 2045?
Existing generation composition: What technologies dominate, what is running inefficiently, and where is there capacity underleveraged relative to the demand being built around it?
Substation headroom: Where does available transfer capacity actually exist, and where has the grid gotten too tight to support new interconnection without significant upgrade costs?
Enverus PRISM® aggregates all of this from hundreds of sources, updated daily. The goal is simple: start with the full opportunity set before narrowing your focus.
Once you’ve identified the markets worth being in, the next question is simple: which assets are best positioned to benefit?
Bottom line: Market selection determines the quality of every decision that follows. The strongest acquirers begin by identifying where demand, pricing, infrastructure, and capacity dynamics are creating opportunity before narrowing their focus to specific assets.
From market thesis to asset shortlist
Once a market thesis is established, the objective becomes much more specific: identify the assets most likely to benefit from it.
Even after narrowing the focus to a specific market, there may still be hundreds of assets worth evaluating. The question is not how to find more opportunities. It is how to identify the handful that deserve deeper attention.
One effective approach is to look for assets that are both efficient and underutilized. On the thermal side, PRISM’s scatter plot tools let you filter the nationwide gas fleet by heat rate and capacity factor simultaneously. Draw a box around the range you care about and the map updates in real time.
The logic is straightforward: a plant with a low heat rate (efficient) and a low capacity factor (underutilized) may be a structurally undervalued asset. It has the equipment to compete, but something is suppressing dispatch. That could be ownership constraints, contract structure, or market positioning. All of those are addressable through acquisition.
Layering in the geospatial buffer filter adds the demand angle. Select the gas plants that clear your heat rate and capacity factor thresholds, apply a 10-mile radius, and restrict the view to assets within that proximity of announced or candidate data center load. The result is a map of efficient, underutilized generation sitting next to growing demand.
Spot market trends between under-utilized gas supply and load growth.
One data layer worth calling out specifically: candidate load sites. PRISM maps the land-leasing activity of companies that acquire land on behalf of hyperscalers before a data center is officially announced. These sites represent future demand that has not yet shown up in any public dataset. Tracking them alongside announced projects gives you a more complete picture of where load growth is actually headed.
Many acquisition processes focus on the asset itself. The more important question is often what is happening around the asset. Load growth, transmission capacity, queue dynamics, and infrastructure constraints can have as much influence on value as the asset’s operating characteristics.
By this point, the goal isn’t to find more opportunities. It’s to understand whether the opportunities you’ve identified still hold up under closer scrutiny. Of course, identifying a promising asset is only the first step. A project that looks attractive in a screen may look very different when infrastructure constraints, queue competition, and local pricing dynamics are considered.
Bottom line: The competitive advantage doesn’t come from having more data. It comes from identifying the handful of signals that matter and reaching a conclusion faster.
Zooming in on a target location
Asset quality and asset opportunity are not the same thing.
The highest-value targets are not always the most obvious assets on the market. They are often assets whose value is changing because the market around them is changing. In many cases, the market is still valuing assets based on current utilization rather than future demand exposure. As load growth shifts, that gap can create opportunity for buyers who identify it early.
At this stage, the objective shifts from identifying opportunity to validating it. The question is no longer whether an asset looks attractive. It is whether the surrounding infrastructure, market conditions, and physical constraints support the investment thesis.
Get ahead earlier in qualifying/disqualifying an in-queue opportunity.
Can this project actually connect without triggering major upgrades?
Available transfer capacity is often one of the first questions worth answering. A project may look attractive on paper, but limited headroom at the point of interconnection can materially change the economics. Historical and forward-looking ATC helps establish how much capacity is actually available before curtailment or network upgrades become a concern.
Who are you competing against for capacity?
When evaluating a queue position, you’re not just assessing the project itself. You’re assessing its position relative to everything else competing for the same infrastructure. Understanding where projects sit in the study process, what upgrade costs have been assigned historically, and how likely competing projects are to reach commercial operation provides critical context for valuation.
What has the asset actually earned?
Average market prices rarely tell the full story. Asset value is ultimately determined by what a project captures at its specific location. Looking at nodal pricing, basis differentials, congestion exposure, and long-term forecasts helps establish whether historical and future revenues support the investment thesis.
Can the project actually be built as planned?
A queue position or development asset may look attractive until physical constraints are considered. Land feasibility analysis helps determine what can realistically be constructed on a site by accounting for factors such as slope, setbacks, transmission infrastructure, and road access before detailed design work begins.
Infrastructure and location tell you whether an opportunity is viable. Economics tell you whether it’s worth pursuing.
Bottom line: Asset-level diligence is ultimately about context. Queue position, transmission capacity, nodal pricing, and land feasibility all help determine whether an attractive asset remains attractive under closer scrutiny.
Turning signal into conviction: asset-level economics
The purpose of screening is not to find interesting assets. It is to identify which assets deserve deeper diligence.
That requires an economic view.
By the time an opportunity reaches this stage, the question is no longer whether it looks attractive. The question is whether the numbers support the thesis.
Historically, detailed valuation often occurred late in an acquisition process. Today, many buyers are building preliminary economic views much earlier because speed matters.
Early economics are not about replacing diligence. They are about determining which opportunities deserve deeper diligence and which do not.
The challenge is that the underlying data lives in dozens of places and assumptions vary from analyst to analyst.
This is where screening and economics start to converge. The same data that helps narrow a universe of assets can also help determine which opportunities deserve deeper diligence. Building conviction ultimately comes down to answering three questions:
How has this asset actually performed?
Understanding how an asset has actually performed is the starting point for any credible valuation to help establish whether a plant’s historical performance supports the investment thesis.
For thermal assets, the foundation is CEMS data: hourly generation, emissions, and heat rates for every operating plant, rolled up to monthly views. This tells you nameplate capacity, fuel consumed, megawatt hours generated over time, and realized efficiency across seasons and dispatch conditions. Each gas plant is tagged to a local fuel hub, with observed price differentials built in, and assigned to an LMP node with full spark spread history available.
What assumptions are driving value?
FERC-regulated pipeline meter station data provides visibility into actual gas flow capacity, helping improve fuel cost assumptions and thermal asset valuation. Rather than relying on static pipeline information, buyers can see scheduled versus actual flows, available headroom, and capacity constraints at the asset level. Understanding what can realistically flow to a plant creates a more defensible view of future fuel costs and operating economics.
Does the opportunity hold up economically?
The goal is not to build a perfect valuation model. It is to create a consistent framework for comparing opportunities. When acquisition teams are evaluating multiple assets across multiple markets, consistency matters as much as precision.
PRISM provides a pre-populated DCF model for every asset in the platform. For a thermal plant, that means a seven-year forward power curve, Henry Hub forwards adjusted for the local hub differential, and a plant-specific capture rate based on observed nodal price volatility. Key assumptions, including capital structure, CapEx, OpEx, and cost of debt, remain fully configurable. The model outputs equity-level and unlevered project cash flows, DSCR, and IRR.
For renewable and storage assets, the framework is the same but the primary inputs shift. ATC at the point of interconnection replaces spark spread as the first filter. Queue completion probability replaces heat rate as the efficiency signal. Land feasibility and buildable area layer in alongside the economics. The DCF model is technology-agnostic and covers wind, solar, storage, hydro, and nuclear in addition to thermal.
You can export up to 50 assets in a single pass. For a portfolio screen, that means comparing normalized economics across a large universe before deciding where to focus deeper diligence.
Quantify power plant generation and economics.
Bottom line: Early economics are not about replacing diligence. They are about determining where diligence should be focused and building conviction before entering a competitive process.
What a prioritized shortlist looks like
Running this workflow end to end, from market screen to location diligence to asset economics, in a single platform changes what you can do in a single session. What used to require weeks of manual data collection and multiple vendor engagements can run from start to finish before you commit to a formal process.
The other benefit is auditability. Every assumption in the PRISM model is transparent and sourceable. Power price realization, fuel costs, nodal exposure, capacity revenues, long-term market fundamentals: when you take a shortlist to your investment committee or a lender, you can show exactly what data the economics are based on, where it came from, and when it was last updated. That conversation is easier than when the model came from a manual build or a third-party engagement where the inputs are harder to trace.
The real advantage isn’t more data
The next cycle may not be won by the teams reviewing the most opportunities, but by those that can identify which opportunities matter most.
In a market where timing has become a competitive advantage, the ability to move from opportunity to conviction quickly may be just as important as the opportunity itself.
In our final post, we will cover one of the most important variables in project diligence: network upgrade costs. We will examine why interconnection risk has become a major driver of project economics and how to quantify exposure before committing significant capital.
Ready to see the screening-to-economics workflow in action?
AUSTIN, TX — July 8, 2026 — Enverus today announced the acquisition of the A2D well log library and associated data products from TGS ASA, adding the world’s largest commercial well log database to the Enverus platform.
The transaction expands the subsurface foundation of Enverus’ energy intelligence platform and creates a faster path from interpretation to decision. By connecting A2D’s well logs, formation tops, petrophysical data and basin-scale attributes with Enverus production, completions, land, ownership, costs, economics and analytics workflows, customers will be able to reduce data preparation, improve cross-functional alignment and move more quickly from understanding the rock to understanding the return.
What This Means for Customers
Geoscientists and engineers gain access to more than 8 million depth-calibrated raster logs and 1.9 million digital LAS files spanning millions of wells across every major U.S. basin, including more than 5 million proprietary logs not available from any public or regulatory source
Teams can connect more than two million hand-picked formation tops, petrophysical interpretations and basin temperature models directly to the production, ownership and cost data they already track in Enverus
Analytics-ready log curve attributes and 3D log attribute volumes let customers move from single-well analysis to basin-scale characterization without switching platforms
Existing A2D licensing and subscription arrangements carry forward, so current customers see no disruption to access
A2D Technologies began building this library in 1993. Through more than three decades of continuous collection, processing and quality control, it has become one of the industry’s most trusted commercial sources for understanding the subsurface across every major U.S. producing basin, from the Permian and Eagle Ford to the Bakken and Marcellus.
“We have always believed energy data becomes more valuable when it is connected,” said Manuj Nikhanj, CEO of Enverus. “A2D brings subsurface depth and quality that customers have trusted for decades. When logs, tops and petrophysics can be connected with production, completions, ownership, costs and economics, teams can move from understanding the rock to understanding the return. That is the difference between a data library and an intelligence platform.”
The acquisition strengthens Enverus’ proprietary data position by expanding its well data and subsurface capabilities with A2D’s depth-calibrated logs, proprietary log inventory, formation tops and petrophysical interpretations and basin-scale attributes. Connected to production, completions, land, ownership, costs and economics, that subsurface context becomes a foundation for differentiated analytics and, over time, more useful energy-specific AI workflows.
For TGS, the transaction puts the A2D library on a platform built to extend its value across the full energy workflow.
“We built the A2D library into something the industry depends on, and this transaction ensures it keeps getting better,” said Kristian Johansen, CEO of TGS. “Our customers have always wanted to take this data further into their workflows. Enverus gives them the platform to do that.”
Well data products remain available as standalone subscriptions and through existing access platforms, while additional integrated capabilities across the Enverus platform will be introduced over time.
The acquisition of A2D follows Enverus’ recently announced acquisition of PDS Energy Information’s exchange assets, adding the operational network through which an estimated 80% of U.S. completions, production and drilling data is exchanged. A2D adds a different and equally important layer: the subsurface record behind well planning, landing decisions, reservoir characterization and asset development.
Together, PDS and A2D advance a single Enverus strategy: connecting the data energy companies use to understand the subsurface, plan and run field operations, measure production performance, understand costs and economics, value assets and execute commercial workflows. The result is a more complete energy decision platform spanning subsurface, operations, production, costs, economics and commercial workflows — from rock to revenue.
Terms of the transaction were not disclosed.
About Enverus Enverus is the energy industry’s AI and data platform, serving more than 8,000 energy companies across 50 countries. Built on 25+ years of proprietary intelligence, 2.7 petabytes of continuously updated data, 350 million+ courthouse records and $500 billion+ in annual transaction value across the full energy value chain — upstream, midstream, power, renewables, utilities and capital markets. Enverus is 100% dedicated to energy. Learn more at Enverus.com.
About TGS TGS is a leading provider of energy data and intelligence, offering subsurface, seismic and well data products to the global oil and gas industry. The A2D well log library, originally built by A2D Technologies beginning in 1993, has served as one of the industry’s primary commercial sources for well log data for more than three decades.
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