Power Allocation Optimization
AI platform for upstream extraction process optimization that screens field portfolios, values drilling prospects, and unifies subsurface and commercial data to improve capital allocation decisions across geographies, water depths, and market conditions.
The Problem
“Optimize upstream capital allocation across field portfolios, drilling prospects, and subsurface scenarios”
Organizations face these key challenges:
Field portfolio screening is fragmented across regions and asset classes
Deepwater and mature segments may attract capital despite limited remaining recovery upside
Prospect valuation is slow and heavily dependent on manual analyst work
Subsurface and commercial teams often work in disconnected systems with inconsistent assumptions
Impact When Solved
The Shift
Human Does
- •Assemble field, prospect, subsurface, cost, and market inputs from separate sources
- •Review portfolio segments by geography, basin, and water depth using spreadsheets and dashboards
- •Estimate prospect economics and compare drilling opportunities under current assumptions
- •Reconcile subsurface and commercial views to set rankings and capital priorities
Automation
- •No AI-driven screening or valuation is used
- •No continuous ranking of assets across changing market conditions is performed
- •No automated integration of subsurface and commercial signals is available
Human Does
- •Set portfolio objectives, risk tolerance, emissions priorities, and capital constraints
- •Review AI-ranked fields and prospects and decide which opportunities advance
- •Challenge assumptions, investigate exceptions, and resolve conflicts between technical and commercial views
AI Handles
- •Screen field portfolios across geographies and water depths to identify remaining recovery upside and over-invested segments
- •Estimate prospect value, breakeven, emissions intensity, and capital efficiency under multiple market scenarios
- •Combine subsurface and commercial inputs into a unified ranking and comparative diagnostics workflow
- •Monitor changes in prices, costs, fiscal terms, and asset performance and refresh opportunity rankings
Operating Intelligence
How Power Allocation Optimization runs once it is live
AI runs the first three steps autonomously.
Humans own every decision.
The system gets smarter each cycle.
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.
Step 1
Assemble Context
Step 2
Analyze
Step 3
Recommend
Step 4
Human Decision
Step 5
Execute
Step 6
Feedback
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.
The Loop
6 steps
Assemble Context
Combine the relevant records, signals, and constraints.
Analyze
Evaluate options, risk, and likely outcomes.
Recommend
Present a ranked recommendation with supporting rationale.
Human Decision
A human accepts, edits, or rejects the recommendation.
Authority gates · 1
The system must not approve final capital allocation decisions without review and sign-off from portfolio planners or investment decision makers.[S1][S2]
Why this step is human
The decision carries real-world consequences that require professional judgment and accountability.
Execute
Carry out the approved action in the operating workflow.
Feedback
Outcome data improves future recommendations.
1 operating angles mapped
Operational Depth
Technologies
Technologies commonly used in Power Allocation Optimization implementations:
Key Players
Companies actively working on Power Allocation Optimization solutions:
Real-World Use Cases
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