AI for Sustainable Subsurface Energy
Leveraging Artificial Intelligence and Machine Learning to pioneer low-carbon technologies, unlock heavy oil resources, and optimize green energy production through advanced subsurface characterization.
AI-Powered Subsurface Intelligence
At Kepler, our mission is to redefine the future of subsurface energy production. We build advanced AI and ML systems that support complex Chemical EOR projects, transforming how heavy oil resources are unlocked. By integrating deep domain expertise with cutting-edge technology, we help our partners develop and understand green technologies, accurately describing the dynamic and static behavior of their reservoirs to achieve production goals with a minimal carbon footprint.
FLAGSHIP PRODUCT
ChemSim Intelligent Simulator
ChemSim is built on four foundational pillars, combining a robust simulation engine with intelligent AI, a seamless user experience, and integrated business analytics.
Simulation Core
Leverages the industry-proven UTCHEM engine on a scalable, cloud-native infrastructure for maximum accuracy and parallel processing power.
AI Co-Pilot
Calibrates models against production history with AI-powered history matching, then employs proxy models for predictive design.
Interactive UX
A fully browser-based, visual platform. Design workflows, visualize data in 3D, and collaborate with your team in a shared workspace.
ESG & Economic Analytics
Automatically calculates project NPV, IRR, and the unit carbon intensity (kg.CO2/bbl) compared to a waterflood baseline.
A Modern Practitioner's Workflow
Project Setup
Visual Data Input
AI-Assisted Design
Full Simulation Run
Results & Analysis
Step 1: Project Setup
Login via a web browser and create a new project. The intuitive dashboard provides a central hub for managing all ongoing and past simulations.
Step 2: Visual Data Input
Upload reservoir models and historical production data for waterflood, polymer, or other chemical injection projects for immediate validation.
Step 3: AI-Assisted Design
The AI Co-Pilot first calibrates the model by automatically matching production history. Once calibrated, the AI assists in designing future EOR strategies.
Step 4: Full Simulation Run
Submit the history-matched model to our GPU-based parallel UTCHEM core in the cloud. This enables predictive simulation of multi-million gridblock models with a single click, capturing the complex responses of highly heterogeneous reservoirs to tracer and chemical floods while you monitor progress in real-time from the dashboard.
Step 5: Results & Analysis
Explore results with interactive dashboards, 3D animations, and integrated economic and carbon intensity analytics to make the best decision.
Our Solutions
We provide specialized, AI-powered solutions for the most critical aspects of Chemical EOR and subsurface analysis.
ESG by Design: Carbon Intensity Calculation
ChemSim provides transparent, automated carbon intensity calculations for every EOR scenario. By comparing your design against a waterflood baseline, our platform empowers you to make environmentally responsible decisions, meet ESG targets, and quantify the benefits of green EOR technologies.
For example, in a recent study for a heavy oil project in Canada (API of 14, viscosity 300+ cP), our optimized polymer flood design demonstrated a significant reduction in emissions compared to the baseline.
AI-Powered Tracer Design & Analysis
Our AI agents and advanced ML models streamline the entire tracer workflow. From optimizing the design of single and multi-well tests to rapidly interpreting return curves, ChemSim delivers a clearer picture of your reservoir's dynamic behavior, helping you map flow paths and identify unswept oil zones with greater speed and confidence.
Tracer Data Input
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AI Analysis Engine
(ML Interpretation & Modeling)
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Flow Path & Saturation Insights
Our Leadership
Kepler was founded by industry veterans with a shared vision: to merge rigorous reservoir engineering with the power of artificial intelligence. We are dedicated to helping our clients unlock the full potential of their assets while promoting sustainable, low-carbon energy production.
Mehdi Ezzatabadipour, Ph.D.
Founder & CEO
My journey began in the world of physics and computational sciences, where I earned a Ph.D. in 2019 focused on designing machine learning algorithms and simulation methods to study complex systems and networks. That early work gave me a deep appreciation for how mathematical models and data-driven approaches can bring clarity to even the most intricate challenges.
Over time, my interests moved toward real-world impact. I transitioned into industry, taking on roles as an R&D scientist, AI specialist, and data expert. I spent years developing AI engines and advanced analytics for companies across the oil and gas sector, from fast-moving startups to some of the largest energy corporations based in Houston, TX. These experiences gave me a front-row seat to the evolving landscape of energy, data, and decision-making.
The rapid emergence of technologies like large language models and agent-based AI marked a turning point. It became clear that there was a window of opportunity to build something meaningful at this intersection of AI and energy. That’s how Kepler came to life. I co-founded the company with my close colleague, Dr. Hamid Lashgari, to bring together physics-based simulation, AI innovation, and domain expertise under one roof. Our goal is to help oil and gas companies make faster, smarter, and more transparent decisions backed by science and powered by technology.
Hamid Lashgari, Ph.D.
Co-Founder
My career has been dedicated to the numerical simulation and optimization of subsurface energy production. I earned my Ph.D. in Computational & Petroleum Engineering from the University of Texas at Austin in 2014, where my research focused on developing thermal and chemical reservoir simulators for heavy oil—a direct precursor to the engine that powers ChemSim.
My passion lies in leveraging highly numerical simulation to forecast production, optimize recovery, and advance low-carbon and clean energy technologies. By integrating AI and ML models with rigorous physics-based simulation, we can achieve a more accurate and efficient understanding of reservoir behavior. This approach is critical for maximizing asset value while minimizing environmental impact.
I joined Mehdi to co-found Kepler because I believe that by embedding this deep domain expertise into an AI-powered platform, we can empower a new generation of engineers to solve critical energy challenges with unprecedented accuracy and speed. We are building the tools to make data-driven, sustainable energy production a reality.
