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Five FERC Changes in Five Minutes: What Energy Investors Need to Know

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For financial institutions financing, investing in or valuing power-intensive projects, the rules governing large-load interconnections are becoming a material consideration for project timing, capital requirements and portfolio strategy.

Electricity demand from data centers, advanced manufacturing and other large industrial users is growing faster than many transmission systems were designed to handle. On June 18, 2026, the Federal Energy Regulatory Commission (FERC) issued show-cause orders directing all six regional grid operators to justify or reform their tariff rules for large-load interconnections within 60 days and file resource adequacy reports within 30 days.

These are not final rules. They open six parallel regional proceedings, and outcomes will likely vary by market. But the direction is unmistakable: FERC wants large-load projects to connect more efficiently, bear an appropriate share of the costs they create and demonstrate a credible relationship between new demand and available supply.

For lenders and investors, that means regulatory decisions could directly affect development timelines, financing needs, asset valuations and long-term portfolio strategy.

What FERC’s Large-Load Order Means for Energy Investors

FERC is not simply trying to make it easier for more large loads to connect to the grid. It is attempting to establish clearer terms for how those projects enter the system, who bears the associated costs and how new electricity demand will be matched with reliable supply.

For energy lenders and investors, that means interconnection is moving out of the technical appendix and into the center of the investment decision. Regional differences will matter — lenders and investors should expect different requirements, risks and opportunities across the country as each grid operator responds to the order.

The projects most likely to attract capital will not necessarily be those with the largest announced demand. They will be the projects that can demonstrate a credible, flexible and financeable path to power.

What These Changes Mean Together

FERC’s proceedings point toward a new model for large-load development, a model that demands more from projects before they can access the grid. Going forward, developers will increasingly be expected to demonstrate greater commercial readiness, clearer responsibility for grid costs, a realistic source of generation, operational flexibility where applicable and meaningful coordination between load, generation and transmission timelines.

For financial institutions, this redefines what project bankability means. Strong demand signals are not enough. A financeable project needs credible answers to five specific questions and those answers will vary across the six regional markets affected by the FERC order.

Figure 1: The five questions that define project bankability under the new FERC framework.
Figure 1: The five questions that define project bankability under the new FERC framework.

Here Are the Five Changes Investors Need to Understand

Figure 2: FERC's five pillars of large-load interconnection reform, issued June 18, 2026
Figure 2: FERC’s five pillars of large-load interconnection reform, issued June 18, 2026

1.  Faster and More Disciplined Study Processes

FERC is directing regional grid operators to overhaul how large-load transmission applications are submitted, evaluated and studied including consideration of alternative transmission technologies that could expand grid capacity more efficiently.

According to Enverus Intelligence® Research (EIR), the goal is to create a process that can distinguish credible developments from speculative, duplicative, or commercially unready requests. Projects that are genuinely ready may move through faster; less mature applications could lose their place in queue or face higher requirements.

Why this matters for financial services

A utility interconnection application or a proposed energization date should not be treated as evidence that power will actually be available. Before committing capital, lenders and investors should evaluate:

  • Whether the developer controls the site and has made meaningful financial commitments
  • Which interconnection studies have been completed — and which are still outstanding
  • What network upgrades are required and whether the proposed timeline accounts for them
  • Whether the load request reflects a realistic development schedule, not just an option

The core financial question:

Is this a real project with an executable path to power — or an attractive development story that hasn’t been stress-tested against grid realities?

2.  Stronger Protection Against Cost Shifting

FERC is focused on bringing transparency to transmission and network upgrade costs by ensuring those costs are borne by the projects that trigger them, not shifted to utilities and existing ratepayers.

A new data center or industrial facility can require substantial grid investment. If that project is delayed, downsized or cancelled, the infrastructure built to serve it may still need to be paid for. FERC wants clearer accountability for those outcomes.

ENVERUS INTELLIGENCE RESEARCH®

EIR notes that utilities stand to gain significantly from the volume of transmission buildout this reform will drive. EIR specifically identifies AEP, Oncor and NextEra Energy (NEE) as well-positioned to capitalize — all three are concentrated in PJM and ERCOT, where EIR forecasts the highest load growth. Source: ”Setting Terms | FERC Rewires Large-Load Interconnection,” EIR, June 22, 2026.

Why this matters for financial services

Large-load developments will increasingly require meaningful financial commitments earlier in the project lifecycle. Depending on how regional rules evolve, exposure could include:

  • Study deposits and scoping fees
  • Network upgrade contributions and construction cost-sharing obligations
  • Letters of credit and parent company guarantees
  • Minimum payment requirements and obligations tied to requested capacity
  • Costs associated with a delayed or reduced load ramp

These requirements will affect project leverage, liquidity, contingency reserves and the amount of sponsor equity required before operations begin. Transmission costs are becoming a material line item in the project capital structure.

Figure 3: Financial commitments now arise at every stage of the project lifecycle, not just at construction.
Figure 3: Financial commitments now arise at every stage of the project lifecycle, not just at construction.

3.  Greater Support for Co-location and Behind-the-Meter Generation

FERC is directing regional markets to develop clearer treatment of co-located projects — arrangements that pair a large electricity user with generation located on or near the same site. Under the new order, these projects receive an explicit regulatory tailwind: they are positioned to become the preferred pathway for connection requests.

ENVERUS INTELLIGENCE RESEARCH®

EIR views co-location as the emerging blueprint for large-load development. According to EIR, co-located projects — already a major and growing trend — now receive regulatory validation, paving the way for projects that pair load with supply to become the desired model. EIR tracks co-located data center campuses across all major grid regions and expects this structure to increasingly define how hyperscalers and industrial users access power. Source: “Setting Terms | FERC Rewires Large-Load Interconnection,” EIR, June 22, 2026.

Figure 4: Co-located Data Center Projects by Grid Region. Source: EIR, “Setting Terms | FERC Rewires Large-Load Interconnection,” June 22, 2026.

That said, co-location changes the nature of the risk it doesn’t eliminate it. The entire power solution still requires rigorous diligence.

Why this matters for financial services

For gas-fired behind-the-meter generation, diligence should cover pipeline proximity, available transportation capacity, fuel price exposure, equipment availability, air permitting, operating reliability, backup power and grid import/export restrictions.

For renewable and storage combinations, the analysis should address intermittency, storage duration, grid backup requirements and contractual alignment with the facility’s actual operating profile.

Co-location also opens new investment opportunities across generation, storage, microgrids, gas infrastructure and related energy services.

Figure 5: Traditional grid connection vs. co-located/BTM generation — risk profile and regulatory treatment.
Figure 5: Traditional grid connection vs. co-located/BTM generation — risk profile and regulatory treatment.

4.  New Service Options for Flexible Large Loads

FERC is asking grid operators to consider new transmission-service options for large loads that can reduce or interrupt consumption when the grid is constrained. Projects with genuine operational flexibility may be able to connect faster, or at lower cost, because they don’t require the grid to serve their full maximum demand under every condition.

This is particularly relevant for projects supported by on-site generation, battery storage, staged load growth, demand-management technology, or curtailable operations.

Why this matters for financial services

Load flexibility can be a real source of project value — but only when it is technically credible and commercially structured. Before underwriting that value, investors should ask:

  • How often can the project be curtailed and for how long?
  • Who controls the curtailment decision — the utility, the operator, or an automated system?
  • Does the project have backup supply that maintains operations during interruptions?
  • Are tenant or customer contracts compatible with service interruptions?
  • What happens to revenue during a curtailment event?

The core financial question:

Can the project reduce its grid demand without undermining the revenue and operating assumptions that support the investment thesis?

5.  Better Coordination Between Large Loads and Nearby Generation

The final reform area addresses how new generating facilities should be studied when they are intended to serve electrically proximate or co-located large loads. Historically, load development, generation interconnection and transmission planning have moved through separate processes on different timelines — a separation that is increasingly untenable when a large-load project depends on new generation arriving at roughly the same time.

Why this matters for financial services

Investors can no longer evaluate the load and its proposed power supply as independent projects. The two must be underwritten together. Key questions include:

  • Does the generation project have its own credible interconnection path?
  • Are transmission upgrades required for both the load and the generation?
  • Can the generation legally and physically serve the load as proposed?
  • How do delays in one project affect the economics of the other?
  • Who bears the risk if the load and generation timelines don’t align?

This is especially important for projects promising accelerated access to power through a generation asset that hasn’t yet been constructed or interconnected. As EIR puts it, a proposed power source is only valuable when it can actually be delivered on the timeline embedded in the financial model.

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About Enverus Intelligence® | Research, Inc. (EIR)

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.

New forecast separates organic electricity demand from structural load growth

New forecast separates organic electricity demand from structural load growth

CALGARY, Alberta (July 22, 2026) — Enverus Intelligence® Research (EIR), a subsidiary of Enverus, has released a new report that separates underlying electricity demand from structural drivers such as data centers, electric vehicles, and electrification.

EIR’s model defines organic load as the baseline trajectory of electricity consumption shaped by weather, population, economic activity and energy efficiency. By removing structural drivers from historical load before estimating each zone’s underlying trend, the model is designed to prevent rapidly developing sources of demand from overstating the organic baseline. Those structural drivers are forecast independently and layered back into EIR’s total load outlook.

The 80-zone analysis forecasts organic load rising from 490 GW in 2025 to 521 GW by 2036 before efficiency gains, representing a 0.55% compound annual growth rate. After efficiency is included, EIR expects approximately 10 GW of net organic load growth by 2036, or a 0.2% compound annual growth rate. The report uses PJM DOM as an example of how separating data center demand from historical load can produce a clearer view of the zone’s underlying trajectory.

“Separating organic demand from data centers, electric vehicles and other structural drivers gives market participants a clearer baseline for evaluating electricity growth. This approach helps distinguish demand associated with population, economic activity and weather from newer sources of load that may develop at different rates and in different regions,” said Thomas Mulvihill, report author and associate at EIR.

Key takeaways:

  • EIR’s 80-zone model isolates organic load from data centers, electric vehicles, and electrification.
  • Each zone’s organic forecast is based on historical relationships among hourly load, weather, population and GDP, with energy efficiency modeled as an offset.
  • Organic load is forecast to increase from 490 GW in 2025 to 521 GW by 2036 before efficiency gains.
  • After efficiency is included, EIR expects approximately 10 GW of net organic load growth by 2036, equivalent to a 0.2% compound annual growth rate.
  • The report’s PJM DOM example shows how removing data center growth from historical load can prevent a structural driver from inflating the organic baseline.

FIGURE 1 | Zone-level Organic Growth, 2026-36

US map of organic load growth by region, 2026–2036, in percent
Source | Enverus Intelligence® Research

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.

Off the grid, on the gas

Off the grid, on the gas

CALGARY, Alberta (July 21, 2026) — Enverus Intelligence® Research (EIR), a subsidiary of Enverus, has released a new report examining how hyperscaler capital spending could translate into new data center capacity and greater use of dedicated natural gas generation.

EIR forecasts approximately 62 GW of new Lower 48 data center capacity additions through 2030, supported by roughly $5 trillion in cumulative hyperscaler capital expenditures from 2026 through 2030. The buildout is expected to be concentrated in PJM, ERCOT, MISO and WECC, which together account for more than 80% of forecast capacity additions through 2030.

Grid interconnection congestion and long development timelines are also pushing a growing share of capacity toward behind-the-meter generation. EIR estimates that behind-the-meter projects will account for roughly 40% of installed capacity and imply approximately 1.3 Bcf/d of incremental natural gas demand by 2030, with about half concentrated in ERCOT and PJM.

Near-term project visibility remains comparatively strong, with approximately 96% of forecast 2028 additions tied to projects under construction or in late-stage development. That share falls to 26% by 2030, reflecting greater uncertainty around unannounced, uncontracted and outer-year projects.

“Data center growth is increasingly reliant on behind-the-meter infrastructure. Our forecast indicates that grid interconnection constraints will shape not only where capacity is built, but also how it is powered, with behind-the-meter natural gas generation emerging as a primary path for projects that cannot wait for traditional grid access,” said Carson Kearl, report author and associate at EIR.

Key takeaways:

  • EIR estimates approximately 62 GW of new Lower 48 data center capacity additions through 2030.
  • PJM, ERCOT, MISO and WECC are expected to absorb more than 80% of new capacity through 2030.
  • Behind-the-meter projects are forecast to account for roughly 40% of installed capacity.
  • EIR forecasts approximately 1.3 Bcf/d of incremental natural gas demand from behind-the-meter projects by 2030.
  • High-confidence project coverage declines from approximately 96% of forecast additions in 2028 to 26% in 2030.
Data center capacity growth by ISO, PJM to ISONE, 2025–2035 in GW

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.

Enverus Media Advisory - Trump vs. Harris: A tale of two energy policies

What’s shaping mineral markets in the second half of 2026

The first half of 2026 brought commodity price volatility, geopolitical disruptions, infrastructure constraints, and demand patterns that don’t match the forecasts written just a few years ago. Our Mid-Year Minerals Outlook webinar highlights what’s happening and what it means heading into the second half of the year.

If you missed it, the replay is available now. Here’s a preview of what the session covered.

What’s driving commodity prices right now

Energy markets respond to events, not just supply and demand fundamentals. The webinar opened by grounding the discussion in that reality: conflicts, infrastructure failures, weather events, and policy surprises all redirect long-term trajectories. Understanding the forces behind current pricing is the starting point for making sound decisions about your portfolio.

Energy demand is growing, just not where you might expect

Global primary energy demand continues to increase, but the growth rate has slowed and is not uniform across geographies. Where that growth is concentrated, and what it means for different commodity types, came through clearly in the session. It’s a more nuanced picture than the headline numbers suggest.

For mineral owners, the geographic distribution of demand growth has real implications. Markets where consumption is expanding fastest are also the markets driving LNG trade flows, pipeline investment decisions, and ultimately the price signals that operators respond to when setting their drilling budgets.

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Natural gas vs. oil: a different outlook for each

The webinar draws a clear distinction between where natural gas demand is headed versus oil. The dynamics affecting each are different enough that your exposure to one versus the other carries different implications for long-term cash flow. Renewables growth, energy transition timelines, and transportation trends are all part of the picture, and they don’t point in the same direction for both commodities.

If your acreage is weighted toward natural gas, or you’re evaluating an acquisition with significant gas production, the session offers a grounded view of where the market is heading and what to make of the current price environment. The same applies if you’re oil-weighted and thinking through how long that exposure stays constructive.

What export infrastructure has to do with your royalties

Production in the ground is only worth what you can actually sell. Getting gas or oil from the wellhead to a buyer requires pipelines, processing facilities, and for natural gas crossing borders, export terminals that take years to permit and build. When that infrastructure isn’t there, or isn’t keeping pace with production growth, royalty owners feel it in their checks.

The webinar covers where those bottlenecks are right now, how they developed, and what the near-term outlook looks like for resolving them. If you own acreage in a basin where prices have been running below what you expected, or you’re evaluating a deal and trying to understand why one area prices lower than another, this section gives you the context to read those signals more clearly.

What this means if you’re managing or evaluating minerals

Mineral and non-op interests don’t exist in isolation. Commodity prices are shaped by infrastructure capacity, geopolitical events, demand patterns across global markets, and technology adoption curves that are still playing out. The session closes with a practical lens for thinking about your acreage and royalties heading into the second half of the year.

Three questions the webinar answers:

  • How does the commodity mix in your royalties or acreage align with where demand is actually heading?
  • For natural gas interests, is your acreage close enough to the infrastructure needed to capture full market value?
  • How are the operators on your acreage responding to current price conditions, and what does that mean for near-term development activity?

Watch the full replay for the complete analysis.

Enverus Press Release - No pain, no gain: Short-term headwinds for natural gas could bring beneficial long-term tailwinds

How Horseshoe Wells are Reshaping Eagle Ford Development Plans

Some Eagle Ford acreage once considered uneconomic is back on development plans. In the recent Enverus Intelligence® Research webinar, Basin Insights: Eagle Ford Long Laterals, horseshoe well data showed why operators are taking a fresh look at these areas. 

Historically, irregular lease boundaries, split ownership, and easements made certain acreage difficult to develop. Traditional two-well pads require space for straight laterals and vertical sections, leaving many tracts untouched. Horseshoe well designs change that equation by allowing operators to develop acreage that previously would have been left behind. 

What Horseshoe Wells Solve in Eagle Ford

A horseshoe well drills out, turns, and comes back, covering roughly the same lateral footage as two straight wells but with a single vertical section instead of two. That’s the entire idea. It sounds simple because it is. What makes it worth writing about is where operators are choosing to drill them: acreage blocks with irregular shapes, split ownership, or lease geometry that made a standard two-well layout impossible to permit cleanly. 

Crescent Energy has drilled more horseshoe wells in the Eagle Ford than any other operator, and roughly 60% of its horseshoe results are in hand. The rest are sitting in permit or drilled-but-uncompleted status, which means the current data set is a partial picture of a technique that’s still filling in. ConocoPhillips has also moved horseshoe designs into active development programs, so this isn’t a single-operator experiment anymore. It’s becoming a standard tool for a specific acreage problem. 

That timing matters for how you read the results so far. Enough horseshoe wells are producing to draw real conclusions about cost and productivity, but the picture isn’t complete. Operators evaluating their own acreage right now are working with a data set that’s still growing, which means the case for or against a given parcel can change as more of Crescent’s results and other operators’ programs come online. 

Horseshoe Well Economics: Why the 15% Cost Savings Matters

Avoiding a second vertical section cuts drilling costs by around 15% compared to two independent short laterals. That’s the number operators cite most often in the Eagle Ford. Chord Energy has reported closer to 30% savings on horseshoe wells in the Bakken, where longer average laterals mean the fixed cost of a second vertical section represents a bigger share of total well cost. 

The gap between those two numbers is worth exploring. It tells you the savings scale with how much lateral footage you’re already committing to, not just the fact of skipping a vertical section. In a basin like the Eagle Ford, where laterals run shorter than the Bakken on average, 15% is the more realistic planning number. Still, on a play where breakevens have been climbing as Tier 1 inventory thins out, that 15% cost reduction on a location that otherwise wouldn’t get drilled at all is a meaningful swing in a development plan’s economics. 

What The Karnes County Data Shows

ConocoPhillips ran a stacked horseshoe program in Karnes County, drilling across two Lower Eagle Ford intervals and an Upper Eagle Ford interval on the same pad. Early initial production rates came in ahead of the subplay average. That result matters for a specific reason: it shows horseshoe geometry works across multiple stacked intervals, not just as a single-zone solution for oddly shaped acreage. 

That distinction changes how a team should evaluate the technique. A horseshoe well isn’t only a land-driven decision about fitting a lateral into an awkward parcel. It’s also a completions decision about which intervals a given block can support, since the Karnes County results suggest stacked horseshoe development doesn’t sacrifice per-well productivity to gain the cost advantage. 

Average horseshoe lateral length in the Eagle Ford runs around 10,000 feet. That’s long enough to justify the well design economically while staying within what current drilling equipment handles reliably on a single run. 

Horseshoe Well Spacing Limits and Acreage Constraints

Horseshoe wells aren’t free of constraints, and the constraint that matters most is interwell spacing. Most current activity clusters around 1,300 feet between the outbound and return legs, which gives operators a cushion against wellbore collision risk and pressure interference between the two legs. Tighter spacing is possible within the Eagle Ford core, where the tightest horseshoes drilled so far have come in around 800 feet. 

That spacing requirement is also what limits which acreage blocks qualify. A horseshoe well needs enough width to turn within, so this isn’t a fix for every stranded parcel. It’s a fix for parcels with awkward shape but adequate width, which is a narrower category than “anything that wasn’t drillable before.” A block that’s simply too narrow still doesn’t work, no matter how much the shape otherwise fits the technique. 

This is where a land team’s early screening matters more than drilling engineering does. Getting the spacing wrong on paper means either walking away from a location that would have worked or committing engineering time to a parcel that was never going to clear the technical floor. 

What This Means For Development Planning

Horseshoe wells are reshaping which parcels make it into a development plan, as operators revisit acreage they’d previously ruled out. Locations that got written off because a two-well pad wouldn’t fit the lease geometry are back on the table, provided the parcel has the width a horseshoe design needs. For operators sitting on Eagle Ford positions with irregular boundaries, that’s a direct change to how much of the acreage position counts as real inventory, not a paper adjustment to a type curve

Finding The Right Candidates to Drill

The hard part is finding those locations. Most teams don’t have a clean way to overlay lease geometry against the spacing thresholds a horseshoe design requires, so the screening ends up happening well by well, usually after someone already suspects a parcel might qualify. Locations that would clear the technical floor can sit unevaluated simply because nobody flagged them first. 

Enverus PRISM® lets you pull lease geometry, existing horseshoe results, and spacing data into one view, so you can flag which of your undeveloped locations qualify for the design before committing engineering time to a full evaluation. That turns a parcel-by-parcel search into a filtered list your land and engineering teams can work from together. 

The data set on horseshoe wells is still growing. Crescent alone has 40% of its results still to come in, and Karnes County is one case study among a technique that’s spreading to more operators and more counties. Watch the Eagle Ford webinar for the full breakdown of horseshoe economics, spacing data, and the extended laterals story shaping the rest of the basin’s development plans. 


About Enverus Intelligence® | Research, Inc. (EIR)

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.

data-center-demand

Charging Ahead | ChargePoint Plugs into The Southeast

ChargePoint and Florida-based Optimus Energy Solutions are expanding their partnership to deploy more than 200 public fast-charging ports across the Southeast, targeting quick-service restaurants and retail centers. ChargePoint will serve as the exclusive provider of hardware, software and services, while Optimus will own and operate the sites.

The buildout comes as Enverus Intelligence® Research (EIR) projects a widening regional divide in EV adoption, with much of the Southeast lagging behind states with zero-emission vehicle mandates. Florida is a notable exception, ranking seventh nationally in projected 2035 EV penetration (Figure 1), driven by its large vehicle base, higher-income households and strong adoption in major metropolitan areas rather than policy. EIR recently cut its 2035 U.S. EV penetration forecast to 8.1% from 20%, citing slower consumer uptake and the September 2025 expiration of federal EV purchase credits under the One Big Beautiful Bill Act. The outlook reinforces the view that adoption in non-mandate states will hinge more on infrastructure and structural demand than on policy. 

This blog offers just a glimpse of the powerful analysis Energy Transition Research delivers on the trending themes. Don’t miss the full picture.

Research Highlights:

Electric cars predate gas-powered vehicles by decades and once outsold them in the U.S. around 1900 before Ford’s Model T made gasoline cars affordable enough to dominate.

Top Three Takeaways:

1: Why is ChargePoint expanding charging infrastructure in the Southeast despite slower EV adoption forecasts?

The expansion reflects the growing importance of charging availability in driving EV adoption. As federal incentives fade and consumer uptake remains slower than expected, accessible public charging infrastructure will play a larger role in supporting EV growth, particularly in regions that are not relying on policy mandates.

2: What makes Florida stand out in the Southeast EV market?

Florida is projected to rank seventh in the nation for EV penetration by 2035, making it a regional leader despite lacking a zero-emission vehicle mandate. Its large vehicle population, higher-income households and strong adoption in major metro areas are expected to drive EV growth through market demand rather than regulation.

3: What does this partnership signal about the future of EV adoption in non-mandate states?

The ChargePoint and Optimus partnership highlights a shift toward infrastructure-led growth. With EIR lowering its U.S. EV penetration forecast, success in states without EV mandates is likely to depend more on the availability of convenient charging networks and underlying consumer demand than on government incentives or policies.

About Enverus Intelligence® | Research, Inc. (EIR)

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.

Enverus press release: Bolstering the Bakken’s twilight years

The Non-Operated Joint Venture Management Is Now a Strategic Function

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​

CFO
Brigham Exploration

Andrew Armpriester​, General Manager North America Non-Operated Joint Venture and Royalty​, Chevron​

General Manager North America Non-Operated Joint Venture and Royalty
Chevron

Scott Rice, Riverbend

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:

  1. 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.  

  1. 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.  

  1. 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.  
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The variable most likely to kill your deal late: network upgrade costs

In our previous two posts, we covered why M&A has become a preferred path to market for many developers and how to build a defensible shortlist using market screening and asset-level economics. This post covers the final step: validating that the assets that survive your screen will actually hold up economically when the deepest risk variable gets stress-tested.

Network upgrade costs are the single biggest determinant of project success in renewable development. They are also the variable most likely to surface late, after significant capital has been committed, time has been spent, and a process is already underway. 

The reason is structural. Cluster study results arrive well into the development process. By the time costs are assigned, most teams have already formed a view on what a project is worth. If the number comes back materially different from what was assumed, it is rarely a pleasant surprise.

This post covers how to quantify that exposure early, how to understand what kind of project you are dealing with before you price it, and how to close the loop from a confirmed site into a preliminary design that validates your CapEx assumptions. 

Why network upgrade costs are so hard to underwrite

The biggest risk is often not the initial upgrade cost. It is how much that cost can change as the queue evolves. 

When a project enters a cluster study, its upgrade costs are not determined in isolation. They are allocated based on the composition of every other project in that study group. As projects withdraw, which they do at high rates, the allocation shifts. A project that looks expensive early can become attractive as the queue thins. A project that looks cheap can become a liability as peers drop out and a larger share of remaining upgrades falls to you. 

The numbers bear this out. At phase one, withdrawal rates typically run between 40 and 50 percent. At phase two, rates typically run between 25 and 35 percent. Each transition reshapes the cost picture for every project still in the cluster. 

The practical consequence is that a single data point from a cluster study is not enough. What you need to know is not just what the network upgrade cost is today. It is which direction it is likely to move, and by how much, as the queue evolves. 

Bottom line: A network upgrade cost is not a fixed number. It is a moving target that changes as projects enter and leave the cluster. Understanding how that number is likely to evolve is often more important than the initial study result itself.

The three project archetypes

Not all projects behave the same way as the queue changes. After running sensitivity analysis across clusters and scenarios, three patterns emerge consistently. Understanding which type of project you are looking at changes the diligence conversation entirely. 

1. The project that gets better.  

This is the most commonly mispriced type. At first glance, it carries a high dollar-per-megawatt cost and a significant number of network upgrades. Red flags on paper. But when you look more closely, many of those constraints are only marginally overloaded, at 101 or 102 percent of capacity. That matters because marginal overloads tend to disappear as competing projects withdraw and system loading eases. In practice, as the queue thins, the constraint count drops from 17 to 7 or 8, and total network upgrade costs decline substantially. A project that looks prohibitive based on one data point may be a legitimate acquisition target once the withdrawal trajectory is modeled. 

M&A Blog Graph2

2. The project that gets worse.  

This is the one that matters most for due diligence. Project J3436 is a clean example from a recent cluster analysis: at first glance, five constraints, $96,000 per megawatt for a 200 MW solar facility. For a project of that size, that number looks attractive. But under every other scenario modeled, costs more than doubled. The reason: the upgrades driving J3436’s cost are not marginal. They are significant overloads that will not go away as peers withdraw. What changes is the allocation percentage. In phase one, this project is responsible for a small share of those upgrade costs because it is one of many triggering them. As the queue thins and competing projects drop out, the same upgrades remain but J3436’s share of the bill grows materially. A project priced at $96,000 per megawatt becoming a $200,000-plus per megawatt project as it progresses is not a recoverable situation. 

M&A Blog Graph2

3. The stable project. 

A 200 MW battery storage facility: 10 constraints, $17 million in total network upgrade costs, $88,000 per megawatt. Similar headline numbers to J3436, but the behavior under sensitivity analysis is fundamentally different. Costs hold across scenarios. Volatility is low. This project has a genuinely low economic risk profile from a network upgrade perspective, and the sensitivity analysis confirms it rather than undermining it. 

M&A Blog Graph3

Bottom line: The headline upgrade cost is often less important than how that cost behaves under different scenarios. A project that appears expensive may improve materially as the queue evolves, while a project that appears inexpensive can become uneconomic as costs are reallocated.

How Enverus Interconnect® quantifies this in days, not months

The analysis above used to require a consulting engagement, a detailed queue model built from scratch, and several weeks of back-and-forth with the ISO. Interconnect® runs the same analysis in two days. 

A single cluster study result tells you what network upgrade costs look like today. It does not tell you how those costs are likely to change as the queue evolves

That distinction is what makes scenario analysis valuable. 

How sensitive is this project to queue withdrawals?

What the platform produces is a full-scale interconnection model for every project in a cluster: constraint identification, cost allocation, and project-level network upgrade costs across standard and custom withdrawal scenarios. You can see where your project ranks within its cluster, by fuel type and by size, and drill into every individual constraint driving the cost.

How likely are those withdrawals to occur?

The sensitivity framework goes beyond standard withdrawal rate scenarios. Interconnect paired with data from Enverus PRISM® (including long-term LMP forecasts, land feasibility scores, and permitting data), provides the ability to build custom sensitivities that reflect the specific characteristics of the projects competing with yours. A project with weak land feasibility signals in PRISM, for example, can be modeled as a likely early withdrawal. The result is a sensitivity analysis calibrated to your cluster, not a generic one built on average assumptions.

How reliable are the results?

Interconnect is powered by SUGAR™, which the ISOs themselves use to run cluster studies. That is not a coincidence. It is what makes the accuracy benchmark meaningful: recent validations against published MISO and SPP cycle results show a 98 to 99 percent match on network upgrade costs between Interconnect outputs and ISO-published outcomes.

Bottom line: The objective is not simply to estimate network upgrade costs. It is to understand how sensitive those costs are to changes in the surrounding queue and how that sensitivity affects project value. 

Closing the loop: from confirmed site to preliminary design

Network upgrade cost sensitivity tells you whether a site is viable. It does not tell you whether the design you are assuming captures the value you are underwriting. 

Most developers enter M&A with preliminary design assumptions that were built at a different stage of the project, under different site conditions, with different equipment pricing. Those assumptions feed directly into the CapEx estimate in the economic model. If they are off, the model is off, and you find out after you have entered exclusivity. 

RatedPower closes that loop. Starting from the PRISM land parcel, with buildable area already defined across 25 configurable constraints, RatedPower produces a preliminary design in minutes: equipment selection, layout configuration, and 0 to 30 percent electrical design documentation, including drawings, bill of quantities, and energy yield results. 

The output is not a finished engineering package. It is the right level of detail for the diligence stage: enough to validate your CapEx assumptions, identify site-specific constraints that a desktop screen would miss, and anchor your economic model in a realistic design before you finalize valuation. 

Bottom line: A site is not fully underwritten until the design assumptions behind the valuation have been tested. Preliminary design helps confirm whether the economics you are modeling can realistically be built. 

The full workflow in sequence

Put together, the three posts in this series describe a single connected workflow: 

Start with a market hypothesis, not an asset. Use PRISM to screen the nationwide opportunity set by load growth, forward LMP, generation composition, and substation headroom, and narrow to a shortlist of candidates worth evaluating. Run asset-level economics on those candidates using CEMS data, nodal pricing, and pre-built DCF models, without a manual data build or a banking engagement. Then, before committing to a process, use Interconnect to model network upgrade cost exposure across withdrawal scenarios and understand what kind of project you are looking at. Once the site clears, use RatedPower to produce a preliminary design that validates your CapEx assumptions before you finalize valuation. 

What used to require three vendors, multiple consulting engagements, and six or more weeks of elapsed time runs in a single workflow. That is not a minor operational improvement.  

The developers who move fastest are not necessarily taking more risk. Increasingly, they are reducing uncertainty earlier in the process.  
 
In a market where timing has become a competitive advantage, the ability to move from hypothesis to conviction in days rather than months changes which opportunities you can actually compete for. 

Enverus brings together the data, economics, and design tools renewables developers need to move from market screen to investment conviction, without the manual builds, fragmented vendors, or months of elapsed time. Explore the Enverus platform for renewable developers to see the full workflow. 

Want to see how Interconnect models network upgrade cost exposure for a project in your current pipeline?

Enverus Intelligence® Research Press Release - Surge in clean energy demand intensifies market competition

Gas vs. Solar vs. Storage: How to Compare Apples to Apples in Power Investment

The power sector is experiencing a fundamental shift. AI-driven data center buildouts, electrification mandates and grid reliability concerns are driving demand for new generation capacity at a pace the industry hasn’t seen in decades. And for investors, developers and operators trying to allocate capital across that landscape, the question is no longer just which assets to pursue—it’s how you actually compare them.

A combined cycle gas plant in Texas. A utility-scale solar farm in New Mexico. A four-hour battery storage project in California. Each carries a different risk profile, a different revenue structure and a fundamentally different operational logic. Yet capital allocation decisions require putting all three on the same scale. That’s the challenge  and it’s one that the power investment world hasn’t solved cleanly. 

The Problem: Power Asset Classes Were Never Built to Be Compared

Three Asset Types at a Glance

Natural GasSolar PVBattery Storage
Revenue DriverEnergy & capacity markets; spark spreadPPA off-take; capacity factorEnergy arbitrage; ancillary services; capacity payments
Dispatch TypeOften misjudges arrival and duration of cold snaps or warm-upsIntermittent (daylight hours only)Flexible (charges & discharges on price signal)
Capex ProfileHigher upfront; stable over timeDeclining rapidly ($/MW)Tied to battery duration ($/MWh)
Key Risk FactorFuel price & carbon policy exposureCurtailment risk; PPA counterparty qualityRevenue stack volatility; market rule changes
Underwriting ComplexityModerate – heat rate, utilization assumptionsModerate – irradiance, degradation curveHigh – duration, stacked revenue sources

Source: Enverus Power & Renewables | For illustrative purposes

Natural gas, solar and storage differ in almost every financial dimension that matters to an investor. 

Natural gas earns revenue through energy and capacity markets, with dispatch economics tied to fuel costs, heat rates and spark spreads. Its returns are sensitive to gas prices, carbon policy and utilization assumptions. A gas plant that looks excellent at $3.50/MMBtu looks materially different at $5.00. Capex is typically higher upfront, but the asset can generate revenue around the clock — making it a baseload or peaking play depending on configuration. 

Solar PV operates on a completely different financial structure. Revenue is a function of irradiance, capacity factors and contracted off-take — typically a power purchase agreement (PPA) at a fixed or indexed price. Capex has fallen dramatically and continues to decline, but solar is intermittent by nature. It generates during daylight hours, which creates both curtailment risk and basis risk depending on how congested the local market gets around noon. Evaluating a solar deal requires understanding the shape of generation, the quality of the PPA counterparty and the long-run degradation curve of the panels. 

Battery storage is the most structurally complex of the three. It doesn’t generate power — it arbitrages it. Revenue comes from a combination of energy arbitrage, ancillary services (frequency regulation, spinning reserves) and increasingly, capacity payments. Duration matters enormously: a two-hour battery and a six-hour battery are nearly different asset classes from an investment standpoint. And the revenue stack is more volatile and less contracted than a typical PPA, meaning underwriting storage requires a fundamentally different set of assumptions about market structure. 

The result: comparing a gas plant to a solar farm to a battery project using a single spreadsheet model means rebuilding your assumptions from scratch every time or worse, forcing incompatible assets into the same template and hoping the outputs are meaningful. 

Most investment teams do something in between. They run separate models. They apply different discount rates subjectively. They benchmark against other deals they’ve seen rather than operating data. And they take weeks to get to a first-pass view on any single opportunity. 

What Happens When You’re Evaluating at Scale

The problem compounds quickly when you’re not evaluating one asset and you’re evaluating a pipeline of them. 

Consider a fund or developer that’s actively screening 50 opportunities across gas, solar, wind and storage in each quarter. Each asset type requires its own modeling conventions. Each team member has built their model slightly differently. PPA pricing assumptions vary. Capex benchmarks are pulled from memory or stale comps. The result is a portfolio of financial models that are internally consistent but externally incomparable. You can’t rank a solar project against a gas plant against a storage deal in any reliable way because the underlying assumptions were
never standardized. 

This is a structural problem with how power assets have historically been underwritten: one deal at a time, by specialists in each technology, using bespoke models. That approach made sense when gas dominated new capacity additions and the competitive set was narrow. It doesn’t work in a market where all three technology types are competing for the same capital simultaneously. 

“You can’t rank a solar project against a gas plant against a storage deal in any reliable way when the underlying assumptions were never standardized.”

The teams that are winning in this environment are building systems, a standardized financial framework that lets them evaluate gas, solar and storage on a consistent basis, rank them against real operating benchmarks and stress-test economics quickly across scenarios.

What “Apples to Apples” Actually Requires 

A true cross-asset comparison framework for power investment needs to do several things consistently: 

  • Standardized financial outputs. 
    IRR, NPV, and payback period needs to be calculated using the same methodology across all asset types — same treatment of depreciation, tax equity, debt structure and 
    terminal value. 

  • Consistent capex benchmarking. 
    Actual installed costs per MW – not developer proformas. Reflecting what gas, solar and storage really cost to build today, by region. 

  • Technology-appropriate revenue modeling. 
    Gas runs on spark spread. Solar runs on PPA structure. Storage runs on a stacked revenue model. Each handled correctly — without rebuilding the logic. 

  • Real-world operating benchmarks. 
    Returns benchmarked against how similar projects actually performed, not what was promised at financial close. 

  • Scenario flexibility. 
    Power prices are up 20%. Capex over budget by 15%. Any framework that requires rebuilding from scratch to answer those questions is too slow. 

The Stakes Are High — And Getting Higher

new power capacity needed by 2030
to meet AI data center demand

rise in PPA prices across
key U.S. markets since 2022

year-over-year growth in
U.S. battery storage capacity
under construction (1Q25-1Q26)

The urgency here isn’t abstract. The AI-driven surge in power demand is repricing generation assets across all technology types simultaneously. Data center developers are signing long-term offtake agreements at prices that would have seemed unrealistic three years ago. Transmission constraints are creating regional basis differentials that significantly affect asset economics. The Inflation Reduction Act continues to reshape the economics of solar, wind and storage through the tax credit landscape. 

In this environment, investment decisions made on the basis of stale benchmarks, inconsistent models, or incomplete scenario analysis carry real downside risk. A gas plant underwritten against 2021 capex numbers may no longer pencil the way the original model suggested. A storage project benchmarked only against developer projections, without reference to how comparable projects have actually performed, may carry execution risk that isn’t showing up
in the IRR. 

The teams that are moving fastest and with the most conviction are the ones who’ve solved the apples-to-apples problem and who can screen hundreds of assets across all three technology types in days rather than weeks, benchmark against real operating performance and stress-test key assumptions without rebuilding from scratch. 

The Solution: Power Plant Portfolio Economics

That’s exactly the problem Enverus built Power Plant Portfolio Economics to solve. 

Power Plant Portfolio Economics delivers standardized, investment-grade financial analytics — IRR, NPV, payback period — across thousands of generation and storage assets, calculated using consistent methodology regardless of technology type. Gas, solar, wind and battery storage are all evaluated on the same financial framework, so your team can compare a combined-cycle plant in the Gulf Coast against a solar-plus-storage project in the desert Southwest without rebuilding a single model. 

The platform draws on actual operating and completed project data, not developer projections so your benchmarks reflect how assets actually perform in real market conditions. And scenario testing is built in: adjust capex, PPA pricing, or operating cost assumptions on the fly to run upside, downside and base-case views instantly. 

For investment teams operating at scale, funds screening large pipelines, developers prioritizing where to allocate development capital, operators benchmarking their portfolio against the market — it’s the difference between evaluating ten deals a month and evaluating a hundred. 

The power sector is moving faster than it ever has. The investment teams that will win are the ones who can see the whole picture, compare assets on equal footing and move from screening to decision in minutes, not weeks. 

Want to see how Power Plant Portfolio Economics works across your asset pipeline? Request a demo at Enverus.com 

Enverus Press Release - No pain, no gain: Short-term headwinds for natural gas could bring beneficial long-term tailwinds

A Faster Review Workflow for Oil and Gas Production Forecasting

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

Forecast review workspace showing selected wells and decline-parameter distributions.
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

Well-level production charts comparing historical production with automated decline curve forecasts.
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 Engineering 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.

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