nextreservoir.com  ·  Subsurface AI

The work engine
for the individual
subsurface engineer.

Ask it a question and it doesn't just answer. It runs the calculation, writes the code, builds the plot, and cites every source down to the page. All in the browser. No install.

01 · CITED

Every claim has a page.

It reads your PDFs first, then answers. Click the citation and land on the line it came from.

No source, no answer. Unsourced claims are removed.
What is Krg at residual oil for P-12A?
Measured Krg at Sor = 0.18 is 0.274 SCAL 2018 · p.14
SCAL 2018 · page 14

The workspace

This is what the work looks like,
end to end, in one screen.

/ Nordhav West GOR Study / HM QC · Session 4
sessionprojectadmin lib
KK
Compute Krg at Sor for P-12A from the SCAL dataset and compare to the 2018 SCAL report. Flag the discrepancy.
At residual oil saturation (Sor = 0.18), the measured Krg for P-12A is 0.274 SCAL_Nordhav_West_2021 · p.14, which is 18 % below the 0.333 in the 2018 SCAL report SCAL_Nordhav_West_2018 · p.9. Carrying the older value forward would overstate gas mobility and the GOR response.

The mismatch exceeds the recommended QC tolerance of ±10 % SPE-123456 · p.8. Recommend using the 2021 endpoint from here on.
3 citations verified · all chunks in retrieved set · 0 flagged
Python · executedsandbox · 0.14 s · 4 MB
from pyscal import GasOil go = GasOil(swirr=0.12, sgcr=0.04, sorg=0.18) go.add_corey_gas(ng=1.8, krgend=0.274) # SCAL_Nordhav_West_2021 p.14 Krg_Sor = go.table["krg"].iloc[-1] print(f"Krg @ Sor = {Krg_Sor:.4f}") print(f"2018 report = 0.3330") print(f"Discrepancy = {(0.333-Krg_Sor)/0.333*100:.1f} %")
Krg @ Sor = 0.2740  ·  2018 report = 0.3330  ·  Discrepancy = 17.7 %
Krg vs Sg, P-12A vs 2018 SCAL report
1.00.50.0Sg →Sor−17.7 %SCAL measured2018 SCAL report
Context
SCAL_Nordhav_West_2021
SCAL_Nordhav_West_2018
HM_run42_output.csv
Nordhav_P12A.las
4 active · 12 chunks retrieved

Guided demo

See it in two minutes.

Follow one project from a new area to a checked method, a cited answer and a page in your notes.

See it work →

Core capabilities

It doesn't answer questions.
It completes the work.

Every interaction produces something you can defend: a cited source, inspectable code, a reproducible plot, or a draft deliverable. Not a chatbot output.

Citation-first

Every claim traces to a source

Inline citation pills show the document, page, and knowledge tier for every factual statement. A structural validation layer runs on every answer. Hallucinated citations are stripped automatically before you see them.

Inspectable code

The AI never does arithmetic in its head

All calculations are written and executed as Python. The code is always visible and downloadable. You run the same script in your own environment and get the same number. That is the reliability guarantee.

Work engine

QC packs, plots, and report drafts

Upload your history-match output, SCAL tables, or well logs and get a structured QC pack with flagged anomalies, standard cross-plots in house style, and a draft technical note, ready to defend in peer review.

Zero install

Open a browser. That's it.

No software to install, no IT approval, no VPN. Calculations, code and corpus search all run in the cloud.

Private by design

Your data is yours

What you upload stays in your private workspace and is never pooled into a shared corpus. EU-resident inference. GDPR-compliant architecture. Row-level security enforces tenant isolation at the database level, not by convention.

Citation pills

The trust mechanism,
auditable by design.

In a domain where a wrong number is expensive, auditability is not a nice-to-have. It is the product. Every citation tells you exactly where the claim came from.

The shape factor for isotropic dual-porosity spacing L reduces to σ = 12/L², so halving the fracture spacing quadruples the matrix-fracture transfer rate. Kazemi et al. · SPEJ 1976 · p.4 · Admin library At L = 2 m this gives σ = 3.0 m⁻², for the spacing your field study reports. Nordhav_West_fracture_study · p.12 · Project docs

Source document & page

Kazemi et al. · SPEJ 1976 · p.4

Knowledge tier

Admin library Project docs Your Boxed library Session upload

Validation guarantee

Every cited chunk verified to be in the retrieved set. Hallucinated citations are stripped and flagged, automatically, on every answer.

How it works

Four steps from
question to deliverable.

The pipeline is visible at every stage. You can inspect what was retrieved, what code was run, and what the source actually says.

01

Ask or upload

Type a question, paste a dataset, or upload a PDF, LAS file or SCAL table. The system detects the format and renders a default view immediately.

02

Retrieve & rank

Hybrid search across all four knowledge tiers, semantic and keyword combined, retrieves the best matching chunks, re-ranked by a cross-encoder reranker for accuracy.

03

Compute & cite

The AI writes Python, runs it in a sandboxed environment, and grounds every claim in the retrieved sources. Citations are validated structurally before you see them.

04

Deliver

A cited answer, a Plotly chart in house style, downloadable code, or a QC pack draft. Ready to defend, share, or compile into a report.

See it work →

Toolboxes

Engineering toolboxes,
inside the conversation.

Ask in plain words and the matching toolbox opens in the answer: sliders, a plot in house style, and the Python behind every number.

  • DCA
  • Nodal
  • PVT
  • Oil MBAL
  • Gas MBAL
  • Gas IPR
  • Critical Lift
  • SCAL
  • Decision Tree
  • Volumetrics

More toolboxes are added continuously.

See it work →

Paper-to-Python

Point at a paper.
Get runnable Python.

Choose a paper in your library and Next Reservoir turns its equations into Python that runs in the sandbox. The result is checked against the paper's own worked example, so you see whether the method reproduces it before you rely on it.

See it work →

Knowledge library

Your knowledge and the field's knowledge,
in one ranked hierarchy.

Every answer searches across four tiers simultaneously. Citations show which tier a source belongs to. Your data always outranks the shared corpus.

④

Session, your current upload

Files you drag in during this working session. Highest retrieval priority. Expires after 60 days; metadata preserved for citation integrity.

③

Project, your shared library

Documents shared across all sessions in a project. Your field data, reports, and references stay searchable throughout the engagement.

②

Boxed Library, your personal corpus

Portable. Belongs to your account, not an employer. Papers, methods, references you've curated over a career. Follows you across every project.

①

Admin Library, the shared foundation

Curated technical corpus: SPE papers, SCAL & PVT literature, open data. Human-vetted before it goes live. Available to all subscribers.

“A general AI tool can summarise a paper. It cannot tell you that your Krg endpoint is 18 % below last year's SCAL report, and show you the code that proves it.”
Built for the subsurface · Nothing horizontal comes close

House style

Plots that look like your work,
not the AI's defaults.

Every generated plot uses the Next Reservoir fluid colour system, the conventions your reviewers and clients already expect. User-overridable per project.

Oil#2B6C2B
Gas#BE3520
Water#1657A3
CO₂#6B3FA0
H₂#B8860A

Line conventions: solid = observed · – – – dashed = modelled · · · · dotted = forecast. Baked into every Plotly output.

Get started

The work is waiting.
Start the session.

Next Reservoir is in closed early access for individual engineers and consultants. No install. No team required. One subscription, your entire subsurface workflow.