About the Event

Traditional reservoir simulation methods are often slow and resource-intensive, requiring iterative runs that consume valuable time during critical decision windows. Each scenario can take hours to execute, limiting the number of development strategies that can be evaluated and often leading to sub-optimal choices. Physics Informed Neural Networks (PINNs) offer a transformative solution by drastically accelerating these simulations—enabling engineers to test multiple scenarios rapidly and identify optimal development paths with greater confidence and agility. Unlike traditional AI models that rely purely on data correlations, PINNs embed the governing physics—such as fluid flow and geomechanics—directly into the neural network architecture. This integration dramatically reduces the need for large, high-quality labeled datasets while improving model generalizability and reliability. For oil and gas operators, this means faster time-to-insight, reduced simulation costs, and more robust decision-making under uncertainty. From field development planning to real-time production forecasting, PINNs provide a physics-consistent, high-speed alternative to conventional reservoir modeling—empowering teams to unlock greater asset value with less computational burden.

About the Innovator

Origen.ai delivers a cutting-edge simulation platform built on Physics Informed Neural Networks (PINNs), purpose-designed for upstream oil and gas workflows. While many AI solutions operate as black boxes that require massive data volumes and struggle with physical consistency, Origen.ai bridges the gap between data-driven modeling and the laws of physics. Rather than applying generic AI toolkits across industries, Origen focuses exclusively on solving subsurface challenges like reservoir simulation, pressure forecasting, and production optimization. Its platform delivers high-speed, physics-consistent results—drastically reducing the time and computational cost of scenario analysis. By embedding domain-specific physics directly into its models, Origen.ai enables operators to run faster, more accurate simulations without compromising scientific rigor, offering a unique value proposition tailored to the complexity of oilfield operations.

Join us as Ruben Rodriguez Torrado, Origen AI’s CEO, presents how their PINNs are revolutionizing the simulation space and their upcoming products in the pipeline.

About the Audience Tailored for subsurface engineers, geoscientists, and asset managers, this session explores how next-generation modeling tools—like Physics Informed Neural Networks—can streamline scenario analysis and optimize field development strategies.