Using Enverus’ integrated production, spacing, cost, and economic analytics, teams can evaluate producing assets and undeveloped inventory in a single, traceable workflow, allowing engineers, A&D teams, and executives to move from raw well data to defensible valuation conclusions faster.
Rather than relying on static basin averages, disconnected tools, or opaque models, Enverus applies context‑controlled benchmarking, decline‑based forecasting, and DSU‑level inventory modeling to support technically sound, review‑ready decisions.
Producing asset and undeveloped inventory analysis can be interrogated, exported, and reproduced for technical review.
Asset valuation workflows are often slowed by fragmented data, inconsistent assumptions, and limited traceability between outputs and underlying wells. Enverus removes these bottlenecks by:
Enverus follows a structured modeling sequence that mirrors how reservoir engineers and asset teams think about value, moving from defining the right well population to evaluating full DSU-level development economics.
At a high level, the workflow will:
Each step remains fully transparent, allowing teams to interrogate assumptions, audit results, and defend conclusions internally and externally.
Faster, reproducible asset valuations grounded in observed well performance, supporting A&D decisions, capital allocation, and long-range development planning with confidence.
This integrated data framework underpins every step of the asset valuation workflow, ensuring context and traceability are preserved from input to output.
Enverus workflows are built on integrated upstream datasets within a single analytical environment, including:
Data undergo standardization and quality checks prior to analysis. Production timing, completeness, and refresh cadence remain visible to users.
Because production, completion, geologic, and commercial data are analyzed together, benchmarking and forecasting incorporate relevant operating context rather than relying on isolated datasets, reducing blind spots in your analysis.
Asset evaluation requires more than high-quality data; it requires the ability to apply internal assumptions, interpretations, and development philosophies within a controlled analytical framework.
Enverus allows users to incorporate proprietary datasets directly into the workflow so that well population definition, benchmarking, forecasting, inventory modeling, and economics reflect how teams actually evaluate assets internally.
Users can integrate inputs such as Areas of Interest (AOIs), internal tiering maps, geologic interpretations, completion designs, and cost assumptions directly into the modeling sequence. These inputs then flow consistently through every downstream step, from defining the well population to evaluating DSU-level development economics.
To support alignment across technical and commercial teams, organizations can establish shared definitions and publish a controlled “golden dataset.” This ensures that subsurface, engineering, A&D, and finance teams are working from the same well lists, filters, and assumptions, reducing reconciliation cycles and improving confidence in final valuation outputs.
All user inputs remain visible, adjustable, and exportable, preserving transparency and enabling results to be reviewed, challenged, and reproduced.
These user-defined inputs propagate through every step of the workflow, ensuring forecasts, inventory, and economics remain internally consistent and technically defensible.
Analysis begins by defining the well set using variables that materially influence performance and cost, such as:
All forecasts, benchmarks, and economic outputs derive directly from this defined population.
The exact well list, filters, and normalization windows remain visible and exportable.
Key contextual variables such as timing, spacing, landing zone, and parent-child sequencing are aligned so performance differences can be interpreted correctly.
Uncontrolled comparisons obscure true performance drivers and lead to misleading conclusions.
Enverus aligns and normalizes contextual variables to support like-for-like evaluation.
The goal is not to homogenize wells, but to control for confounding variables so performance differences are interpretable and you can home in on the true drivers of productivity.
Within the aligned well population, Enverus evaluates engineering and subsurface variables to distinguish design-driven, geologic, and development-order impacts.
Sensitivity testing isolates specific inputs while holding contextual factors constant. This reduces false correlations and clarifies whether observed uplift is design-driven, geologically driven, or development-order driven. Understanding why wells perform differently is critical for forecasting future results and capital decisions.
Well spacing is modeled using a pre-calculated, multi-dimensional framework that quantifies spatial proximity, development timing, vertical relationships, and well status.
Spacing is not limited to a single “nearest neighbor distance.” Instead, it incorporates:
Because spacing metrics are structured:
Users can also apply their own assumptions to the spacing model, influencing parent/child classifications based on co-completion timing window, minimum overlap percentage of neighbor wells, and neighbor search radius.
Spacing is a first-order control on performance and inventory economics, and treating it explicitly prevents overstating recovery and value.
Forecasts are generated using established decline-curve methodologies applied to the defined well population. With Enverus Forecast Studio, you can change assumptions using data conditioning, segmentation, and manually edit Arps DCA parameters. Forecasts grounded in real performance are more defensible than generic basin-wide curves.
Key principles:
Decline parameters for Type Curves reflect the aligned population and normalization period selected by the user.
Type curves are constructed using statistical aggregation techniques applied to the defined well set.
Probability cases (P10, P50, P90) reflect the observed monthly distribution of performance within that population, capturing real variability rather than assumed spreads.
All forecast outputs remain linked to the underlying well list and assumptions.
Your custom forecasts can also be exported directly to Aries & PHDWin to help maintain consistency across engineering and finance workflows.
Undeveloped potential is assessed within drilling spacing units, accounting for spacing density, interference risk, and development sequencing, because asset value is driven by full-development outcomes, not only isolated single-well economics.
Enverus provides structured remaining inventory modeling which can be aligned to Drilling Spacing Units (DSUs) so engineers can evaluate undeveloped potential within a development grid rather than as disconnected single-well cases.
The Enverus Expert Remaining Inventory model provides a pre-generated, play-specific estimate of remaining well locations based on:
The model estimates:
This provides a structured baseline for understanding undeveloped potential in a new area or competitive position without requiring immediate custom modeling.
It is particularly useful for:
For operators with defined spacing philosophies or internal development strategies, Enverus supports customizable future well placement.
Engineers can apply:
Future well locations are generated within the DSU grid according to these assumptions. Because spacing metrics and parent-child logic are integrated into the broader modeling framework, engineers can evaluate:
This prevents overestimating inventory by assuming unconstrained single-well economics.
Inventory is aligned to DSU geometry rather than arbitrary map grids.
This enables evaluation at the unit level:
Because existing and future wells are evaluated together, economic modeling reflects:
This shifts analysis from:
“Is this next well economic?”
to
“What is the value of fully developing this DSU under defined spacing assumptions?”
This allows engineers to test:
All within a consistent, traceable framework.
This supports more defensible valuation and planning:
Capital, operating, and revenue inputs are modeled using driverbased logic aligned to operator behavior and development practices.
Realistic economics require costs and assumptions that reflect how assets are actually developed.
Primary cost drivers include:
Cost inputs are informed by public AFEs, operator capital guidance, SEC filings, and industry cost studies. Inputs are refreshed periodically to reflect evolving development conditions. Where appropriate, modeled costs are scaled to reflect operator-specific capital structures rather than flat regional averages.
Operating and revenue inputs incorporate:
Assumptions are modeled at the play and operator level, aligning economic outputs with realistic operating conditions.
Technical users can:
This allows forecasts and benchmarks to be reviewed, challenged, and reproduced without rebuilding workflows.
Technical discussions can focus on assumptions and drivers rather than data reconciliation.
Many teams use Enverus to establish traceable forecasting and economic inputs, then export outputs into established reserves and economics software.
AI-assisted outputs operate within the same deterministic, traceable framework described above, accelerating analysis without replacing engineering judgment.
Enverus AI deploys energy-native AI agents that securely execute flows across land, trading, operations, finance, and planning, using decades of Enverus intelligence, your proprietary data, and trusted third-party sources.
This is not AI added to software. This is software rebuilt for the AI era.
AI does not replace decline logic, cost modeling, or user-defined well selection. All AI outputs remain linked to the underlying wells, filters, and assumptions.
Engineers retain full control over definitions and final conclusions.
Enverus workflows are built on:
The result is analysis that withstands scrutiny from reservoir engineering, subsurface, finance, and executive stakeholders.
Enverus applies established decline-curve methodologies appropriate to unconventional development. Decline parameters are derived from the defined well population and selected normalization window.
The platform does not impose a single basin-wide curve or fixed parameter set. Forecasts reflect the observed production behavior of the selected wells and can be adjusted by modifying the well population, normalization period, or context filters.
The goal is reproducible decline analysis grounded in observed performance rather than opaque predictive models.
Outliers are not arbitrarily removed. Users retain control over well selection and filtering criteria.
Because type curves are constructed from the defined well population, statistical aggregation reflects the selected dataset. Engineers can:
Probability cases (P10/P50/P90) reflect the actual performance distribution within the selected wells, preserving transparency around variability.
Probability cases are derived from the statistical distribution of performance within the selected well population.
They represent observed variability in decline behavior and cumulative recovery rather than arbitrary uplift or haircut factors applied to a base curve.
Because probability cases are tied to the defined well set, changing the population or context filters directly updates the distribution and resulting cases.
Benchmarking bias is reduced through context control and normalization.
Key controls include:
By reducing confounding variables, the platform supports like-for-like comparisons rather than raw, uncontrolled production overlays.
Engineers can view and adjust all filters used in benchmarking.
Parent-child sequencing can materially affect early-time production and decline behavior.
Users can filter wells based on development order and pad sequencing to evaluate performance within comparable parent or child groups.
Because sequencing remains visible at the well level, engineers can isolate:
This prevents blending fundamentally different production dynamics into a single benchmark.
Normalization windows are user-defined.
Engineers can:
Forecasts derive from observed performance within the selected time window. Immature wells can be included or excluded based on user-defined criteria.
Forecasting and economic outputs are grounded in deterministic, decline-based methodologies and structured cost-driver logic.
AI-assisted features accelerate pattern identification and summarization but do not replace decline calculations, statistical type curve construction, or driver-based cost modeling.
All AI-assisted outputs remain traceable to underlying wells and assumptions.
Well cost inputs are informed by publicly disclosed AFEs, operator capital guidance, SEC filings, and industry cost studies.
Inputs are refreshed periodically to reflect updated disclosures and evolving development conditions.
Operating assumptions (LOE, GP&T, differentials, royalties) are modeled at the play and operator level and updated as new public information becomes available.
Users retain the ability to adjust cost and economic assumptions for scenario testing.
Cost models incorporate primary design and operational drivers such as:
Where applicable, modeled costs are scaled to align with operator-specific capital structures rather than applying flat regional averages.
This allows engineers to compare operator performance within realistic capital frameworks.
Yes. For any forecast, type curve, benchmark, or economic output, users can:
This ensures results can be reproduced internally and defended in technical review meetings.
No. Many teams use Enverus to generate context-controlled forecasting and economic inputs, then export results into established reserves and booking software.
The platform strengthens upstream assumptions while preserving downstream reporting workflows.
Corporate type curves often assume uniform geology, spacing, and development conditions.
Enverus type curves are constructed from user-defined well populations that reflect specific landing zones, vintages, spacing regimes, and development sequencing.
This reduces the risk of overgeneralization and improves defensibility when capital decisions are reviewed.
Yes. Because well selection, context filters, decline logic, and economic inputs are integrated within a single environment, engineers can adjust:
And regenerate outputs without reconstructing the analysis from scratch.
Distances between a subject well and near neighbors are measured every 50 feet along the subject lateral.
At each sampling point, horizontal (2D), vertical, and 3D distances are calculated.
Statistical properties (P25, P50, P75, min, max, mean, standard deviation) are published in the 4D Spacing table for each well pairing.
This provides a distributional view of proximity rather than a single-point estimate.
By default, neighbors are evaluated within a 2,640 ft (½-mile) 3D cylindrical radius.
A 2-mile variant dataset is also available for broader spacing analysis.
This ensures spacing metrics reflect meaningful reservoir interaction distances rather than arbitrary map proximity.
Boundedness metrics classify wells as:
Vertical boundedness is evaluated separately.
This prevents standalone wells from being grouped with interference-prone wells in benchmarking or type curve construction.
Spacing metrics are used as contextual filters during well population selection.
Engineers can construct decline forecasts within:
This reduces interference-driven distortion in decline parameter estimation.
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