Transforming Petroleum Distribution with AI
Multi-agent RL + Generative AI for real-time optimization. A three-layer platform that unifies scenario generation, logistics coordination and financial risk management across your entire distribution network.
0.0%
Cost Reduction
delivered cost per barrel
+0.0%
Service Reliability
on-time fulfillment rate
-0.0%
Risk Reduction (VaR)
vs. static hedged baseline
0%
5-Year ROI
net of platform cost
The Platform
A three-layer architecture, working as one
Each layer produces what the next consumes: scenarios feed the logistics agents, and the realized risk distribution drives hedging decisions — a closed loop of AI decisions.
Layer 1 · Generative Scenario Engine
Generative Scenario Engine
Learns the joint distribution of prices, demand and geopolitics, then samples thousands of realistic futures. Every logistics and hedging decision is stress-tested against the full ensemble.
- Probabilistic price & demand paths
- Regime-switching shocks
- 100-path ensembles in ms
Layer 2 · MADRL Logistics Coordinator
MADRL Logistics Coordinator
A fleet of RL agents — one per terminal, carrier and corridor — negotiate dispatch, routing and inventory policy in real time, balancing service reliability against operating cost.
- Decentralized policy learning
- Real-time re-routing
- Service reliability +23.8%
Layer 3 · Financial Risk Module
Financial Risk Module
Translates scenario distributions into optimal hedge positions under Value-at-Risk and Conditional VaR constraints, continuously rebalancing across futures, options and swaps.
- Minimum-variance hedge ratios
- VaR / CVaR constraints
- −31.2% risk reduction
Technology
Built on a modern ML & data stack
Every component is containerized, streamed and observable — from diffusion training to live dashboards.
PyTorch
ModelingTime-series diffusion backbone
Ray RLlib
ModelingMulti-agent RL training
Stable-Baselines3
ModelingPPO / SAC baselines
XGBoost
ModelingDemand forecasting
FastAPI
BackendInference API
Kafka
InfrastructureReal-time event streaming
ClickHouse
InfrastructureTime-series analytics
Docker / K8s
InfrastructureOrchestration
TimescaleDB
DataOperational store
Next.js 14
FrontendThis platform
Recharts
FrontendScenario visualization
Framer Motion
FrontendInteraction design
Research
Peer-reviewed foundation
The platform is backed by a Q1 journal paper on AI-driven digital transformation in petroleum product distribution — covering the generative scenario engine, the MADRL coordinator and the dynamic hedging module end-to-end.
A Closed-Loop Framework Integrating Diffusion Models with Multi-Agent Reinforcement Learning for Risk-Aware Petroleum Distribution Networks
The distribution of petroleum products is a multi-layered optimization with a multitude of scales, involving gigantic decisions in the terminal, the fleet and the finances, all over a backdrop of high prices uncertainty, high demand uncertainty and geopolitical factors. In this paper, an artificial intelligence (AI) system of three layers is presented. Layer 1 is a generative scenario engine based on time-series diffusion models, providing realistic price and demand paths and is regime-aware. The optimization of service reliability and inventory cost/asset utilization is done in Layer 2 with a multi-agent deep reinforcement learning (MADRL) architecture. The scenario distribution is added to Layer 3 and a financial risk layer is added for dynamic hedging in the scenario space with the constraint of VaR (Value-at-Risk) and CVaR (Conditional Value-at-Risk). The platform achieves a 17.3% cost savings on delivered cost for a simulated US network of 10 terminals, 20 different network carriers and 5 pipelines, a 23.8% improvement in service reliability, and a 31.2% savings in value-at-risk when compared to a static baseline, to achieve a five-year ROI of 1332% against the static baseline. In this talk we discuss the quality measures for the scenarios, the measures of convergence of logistics and the measures of effectiveness of the hedges and we discuss the deployment aspects which makes it difficult to converge the logistics and which aligns with ISO 31000:2018 risk-management guideline and ISO 20815:2018 production-assurance practice.