Energy Recovery Optimization

AI platform for optimizing extraction performance and upstream investment decisions through recovery benchmarking, subsurface modeling, prospect valuation, partnership targeting, and simulation-assisted process design.

The Problem

RecoverOptix improves recovery performance, prospect valuation, and upstream investment decisions with AI-driven benchmarking, modelling, and optimization

Organizations face these key challenges:

1

Recovery performance is benchmarked inconsistently across portfolios and geographies

2

Subsurface, production, economic, and country-risk data are fragmented across systems

3

Capital allocation decisions take too long and depend heavily on manual expert synthesis

4

Partnership targeting for underperforming NOCs is subjective and difficult to scale

Impact When Solved

Ranks fields, prospects, countries, and partners by recoverable upside and commercial attractivenessReduces time required for subsurface-informed capital allocation decisionsIdentifies recovery-factor gaps for targeted NOC partnership and negotiation strategiesImproves pre-drill screening consistency under capital and emissions constraints

The Shift

Before AI~85% Manual

Human Does

  • Gather subsurface, production, reserve, economic, and country data from separate sources
  • Benchmark field and portfolio recovery performance using manual studies and spreadsheets
  • Screen prospects, countries, and potential partners through expert review and consultant input
  • Run standalone engineering simulations and compare design options manually

Automation

  • No significant AI support in the legacy workflow
  • Limited automation for basic reporting and spreadsheet calculations
  • Minimal cross-asset pattern detection or predictive ranking
  • No continuous opportunity scoring across portfolios and geographies
With AI~75% Automated

Human Does

  • Set investment priorities, commercial assumptions, and decision criteria for screening
  • Review ranked fields, prospects, countries, and partner targets and approve actions
  • Validate high-impact recommendations against strategic, regulatory, and country context

AI Handles

  • Unify technical, production, commercial, and country inputs into a consistent opportunity view
  • Benchmark recovery performance, detect recovery-factor gaps, and rank recoverable upside
  • Predict subsurface, prospect, and portfolio outcomes under multiple commercial and emissions scenarios
  • Score partnership targets and recommend likely matches based on technical and commercial fit

Operating Intelligence

How Energy Recovery Optimization runs once it is live

AI runs the first three steps autonomously.

Humans own every decision.

The system gets smarter each cycle.

Confidence95%
ArchetypeRecommend & Decide
Shape6-step converge
Human gates1
Autonomy
67%AI controls 4 of 6 steps

Who is in control at each step

Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.

Loop shapeconverge

Step 1

Assemble Context

Step 2

Analyze

Step 3

Recommend

Step 4

Human Decision

Step 5

Execute

Step 6

Feedback

AI lead

Autonomous execution

1AI
2AI
3AI
5AI
gate

Human lead

Approval, override, feedback

4Human
6 Loop
AI-led step
Human-controlled step
Feedback loop
TL;DR

AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.

The Loop

6 steps

1 operating angles mapped

Operational Depth

Technologies

Technologies commonly used in Energy Recovery Optimization implementations:

Key Players

Companies actively working on Energy Recovery Optimization solutions:

Real-World Use Cases

AI-guided partnership targeting for NOC recovery improvement

The analysis shows which state-run oil fields have the biggest gap between current performance and what similar fields achieve, helping companies spot where partnerships could unlock more oil.

gap analysis and opportunity rankingdecision-support use case inferred directly from the published deployment and its partnership recommendations.
10.0

Material-flow tracking across circular value chains

AI can act like a smart tracker that follows waste and recycled materials through many companies so everyone knows what moved where.

tracking and orchestrationproposed workflow implied by the report's operational requirements, not described as already deployed.
10.0

Process optimization for plasma gasification using appropriate gasifying agents

Use smart process tuning to choose the best operating conditions and gasifying agents so plasma gasification turns difficult waste into cleaner useful gas more efficiently.

process optimizationearly-stage. the source frames this as active research aimed at improving efficiency, reducing costs, and solving commercialization barriers.
10.0

Prospect valuation for pre-drill commercial screening

Before drilling, the tool estimates how much a prospect could be worth so companies can decide which opportunities deserve money and attention.

predictive valuation and decision supportdeployed commercial analytics capability available now.
10.0

AI benchmarking of recovery limits by field type and operator class

Use AI to compare different kinds of oil fields and operators to see which ones are already close to their maximum recovery and which still have room to improve.

Comparative benchmarking and classificationhigh. the article presents clear benchmark comparisons and operational implications using existing field and operator performance data.
9.5

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