Extraction Recovery Optimization

AI platform for optimizing oil and gas extraction decisions across prospect valuation, completions, hydraulic fracturing, remote well monitoring, partnership targeting, and upstream data readiness.

The Problem

Fragmented upstream decisions reduce recovery, slow operations, and hide partnership value

Organizations face these key challenges:

1

Prospect valuation is slow, inconsistent, and difficult to compare across basins and portfolios

2

Frac crews lack immediate insight into fracture hits and offset-well communication during treatment

3

Completion design decisions often miss nonlinear production tradeoffs and interference risks

4

Remote well monitoring is fragmented across vendors, sensors, and manual review processes

Impact When Solved

Prioritize pre-drill prospects with faster and more consistent economic screeningDetect fracture communication and offset-well response during live hydraulic fracturing operationsOptimize completion design using interpretable parameter ranking and physics-informed diagnosticsEnable remote monitoring and anomaly-aware control for intelligent completions

The Shift

Before AI~85% Manual

Human Does

  • Screen prospects, fields, and partnership targets manually across siloed subsurface, production, and economic data
  • Review frac, completion, and remote well signals from fragmented dashboards and expert interpretation during operations
  • Compare completion options, type curves, and post-job studies to choose designs and operating ranges
  • Clean and assemble well, cost, production, and report data before analysis or internal knowledge use

Automation

  • No consistent AI support in the legacy workflow
  • Limited rule-based alerts from existing monitoring tools
  • Occasional static analytics outputs after manual data preparation
With AI~75% Automated

Human Does

  • Approve prospect rankings, partnership priorities, and capital allocation decisions
  • Decide frac and completion actions based on AI alerts, field context, and operating constraints
  • Review exceptions, low-confidence recommendations, and anomalous well behavior requiring intervention

AI Handles

  • Score prospects, producing fields, and partnership opportunities using combined geologic, operational, and economic signals
  • Rank completion parameters, estimate tradeoffs, and surface physics-informed design recommendations
  • Monitor frac, pressure, and downhole telemetry in near real time to detect fracture communication and anomalies
  • Standardize upstream data and generate searchable field intelligence for analyst and copilot workflows

Operating Intelligence

How Extraction Recovery Optimization runs once it is live

AI runs the first three steps autonomously.

Humans own every decision.

The system gets smarter each cycle.

Confidence88%
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 Extraction Recovery Optimization implementations:

Key Players

Companies actively working on Extraction Recovery Optimization solutions:

Real-World Use Cases

AI-ready upstream data preparation for customer LLM and machine learning workflows

Wood Mackenzie is cleaning and expanding oilfield data so customers can feed it into their own AI tools more easily and get better answers.

data enrichment and retrieval preparationactively being implemented; the source describes expanded api attributes and internal ai interface usage to improve readiness for customer ai ingestion.
10.0

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

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

Interpretable completion parameter ranking and tradeoff analysis

The workflow ranks which completion knobs matter most and shows how changing one knob affects production, so engineers can see where extra spending helps and where it stops paying off.

feature importance analysis and what-if response estimationpractical and explainable workflow built on established ml interpretation methods; suitable for engineering studies and optimization reviews.
10.0

Real-time SWPM decision support during hydraulic fracturing

During a frac job, software reads pressure changes in real time from nearby sealed wells so crews can make better on-the-fly pumping and stage decisions.

real-time event detection and operational decision supportdeployed capability referenced as already developed, building on the automated swpm platform.
10.0
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