UnRiskIT® is extending a commercially proven probabilistic decision engine into geothermal EGS, CCUS, and hydrogen storage applications — and actively pursuing federal funding and research partnerships to accelerate deployment.
DOE's primary geothermal cost modeling tool — GETEM, now integrated into NREL's System Advisor Model (SAM) — provides solid deterministic cost estimation. It is not designed to propagate uncertainty through to economic outcomes or to support probabilistic decision-making under uncertainty.
As DOE accelerates geothermal and subsurface energy deployment, capital efficiency and decision quality are emerging as limiting factors in project scalability. The opportunity is to layer probabilistic economic decision support on top of the existing technical estimation frameworks rather than rebuild them.
UnRiskIT® is that decision layer. Commercially validated over 20+ years with major operators. Now being extended into the geothermal, CCUS, and hydrogen storage domains where DOE is investing billions.
GETEM and SAM characterize technical and cost potential. UnRiskIT® extends those inputs by propagating uncertainty through cost, schedule, and economics — producing decision-relevant probabilistic outputs that complement, rather than replace, DOE's existing standards.
The Office of Geothermal's stated priority of reducing drilling costs and risks in EGS development maps directly to UnRiskIT®'s probabilistic well construction decision framework.
FECM has funded digital decision tools for CO₂ transport, storage integrity, and subsurface uncertainty. UnRiskIT® extends this into probabilistic economic decision support.
The same probabilistic architecture applies across geothermal, CCUS, and hydrogen storage — making UnRiskIT® a cross-cutting tool relevant to multiple DOE program offices.
DOE's existing toolset characterizes resource and cost potential well. UnRiskIT® adds the probabilistic decision support that connects those inputs to risked cost, schedule, and economic outcomes — the integrated capability that current standards and industry stacks do not provide together in one framework.
| Capability | Static / Deterministic Models | Industry Tools | UnRiskIT® |
|---|---|---|---|
| Probabilistic outputs | ✗ Deterministic only | PARTIAL | ✓ Native Monte Carlo |
| Integrated cost + risk + schedule | ✗ No | FRAGMENTED | ✓ Unified framework |
| Decision-centric outputs (NPV, drill/defer) | ✗ Distributions only | ✗ Reporting only | ✓ Built in |
| Adaptive / real-time during operations | ✗ No | ✗ No | ✓ Dynamic recalculation |
| Commercial operator validation | N/A (technical estimation) | ✓ Enterprise | ✓ Major operators, 20+ years |
| Applicable to EGS, CCUS, H₂ storage | DOMAIN-SPECIFIC | ✗ Domain-specific | ✓ Cross-domain architecture |
Unlike typical SBIR proposals, this work builds on a commercially deployed system — extending a proven architecture into geothermal EGS applications. This is domain extension, not greenfield R&D. Technical risk is low; commercialization path is clear.
Extend probabilistic engine for EGS-specific inputs: temperature gradient, resource uncertainty, stimulation risk, and dry-hole probability distributions
Calibrate against 3–5 EGS/hydrothermal well datasets from FORGE and other Office of Geothermal public records; document input uncertainty distributions
Quantify difference between deterministic planning outputs and UnRiskIT® probabilistic decision outputs; measure P90–P10 spread reduction vs. baseline
Replay historical drilling go/no-go decisions using probabilistic outputs; demonstrate measurable improvement in economic decision quality under real-world conditions
Define deployment architecture for active Office of Geothermal field test operators; identify 3 commercialization targets; prepare Phase II proposal
This work will demonstrate not only improved uncertainty characterization, but measurable improvement in economic decision quality under real-world conditions — shifting capital allocation decisions from static, assumption-driven planning to continuously updated, economically optimized decision-making.
Expected topic release October 2026 (EERE participates in the October cycle). Aligns with the Office of Geothermal's stated priority of reducing drilling costs and risks in EGS development. The probabilistic decision-support extension is the specific positioning.
FECM has funded digital decision tools for CO₂ transport and storage integrity across multiple SBIR cycles. UnRiskIT® extends this into probabilistic economic decision support for CCUS and H₂ storage.
$171.5M NOFO for next-generation geothermal field tests remains open for recurring rounds (~12 months). UnRiskIT® as decision-support layer for a field test project team — teaming target, not prime.
We welcome discussions with national laboratories, geothermal developers, CCUS project teams, and operators who can provide validation data or letters of support — and who would benefit from Phase II deployment.
NREL maintains GETEM, now part of the System Advisor Model (SAM). A CRADA or letter of collaboration from NREL — ideally via the SAM team — would validate UnRiskIT® as a complementary probabilistic decision layer on top of SAM/GETEM and open the STTR award pathway with a larger ceiling than SBIR.
Subsurface uncertainty expertise strengthens the FECM lane. NETL is the technical home for the CCUS / H₂ storage applications. Lawrence Berkeley National Lab's geothermal and CCS research programs offer direct technical validation and co-investigator opportunities.
Real project data from active EGS programs enables Phase I calibration and creates a direct Phase II deployment pathway. Letters of support from Office of Geothermal field test teams strengthen the market pull argument.
Existing commercial deployment history demonstrates operator acceptance and reduces perceived execution risk relative to typical SBIR efforts. A letter confirming prior deployment is our single strongest credibility argument to DOE reviewers.
Whether you're a national laboratory, a geothermal developer, or a CCUS project team — we'd welcome the conversation about collaboration, teaming, or letters of support.