Custom AI for Complex Global Energy Operations

At its core, AI only does two things: it makes results more accurate, or it makes them faster. Our AI solutions do not replace engineering judgment—they help provide empirical validation. Our models are trained on real field data, built with physics in mind, and designed to give engineers quantitative confidence in their decisions.

Foundational IP

Every subsurface AI solution begins with a common foundation—the mathematical models, domain abstractions, and engineering know-how that enable prediction, optimization, and decision support. At AlphaX, this foundational IP is a reusable layer of intelligence that accelerates new workflows, reduces reinvention, and compounds in value across projects. It transforms bespoke challenges into solvable problems by providing the substrate every subsurface application relies on.

"AI comes of age" — CERAWeek (2019)

Production Forecasting

Fast, predictable, and repeatable AI-based EUR forecasts via spatial, temporal, and induced-phenomena understanding.

The industry's first open-source-based AI that delivers repeatable, reproducible forecasts and performance evaluation tools for reservoir and production engineers. Workflows include quick economics, competitor analysis, type curves, and P-value distributions.

Uses Virtual Well™ to predict production for wells with short or no history.

Visit AlphaX Sky

Implementation Process

Deploying subsurface AI is not one-size-fits-all. We integrate your domain expertise with our foundational IP through a phased approach—data and domain alignment, model adaptation, validation with SMEs, and deployment with feedback loops—so the output is a working decision system, not merely a model artifact.

De-risk your AI implementation

Can you solve for your value proposition with the data you have?

Our Customer Data Acceptance Test (CDAT) is a proprietary set of workflows and questions constructed by our data scientists that allows data engineers to quickly decipher the value of your data stores.

CDAT proactively identifies and solves data management issues like missing values or outliers, anonymizes sensitive data and performs transformations on existing data enabling it be used in real-time or offline machine learning systems.

CDAT process visualization showing data validation workflow

How We Deliver

Four phases, fast iterations, measurable outcomes.

Problem Scope and Definition

Your experts and our team define, review, and discuss the Use Case for the AI Software under consideration.

Data Assessment & Validation

Our team performs an extensive data engineering process (CDAT) to determine if the customer's dataset is viable for the specific AI Software.

Production Development

Our team completes the integration of the dataset with the AI software and works with you to test.

Deployment in Production

Your experts and our team work to deploy the application either in your existing environment or using our cloud system and software harness.

Cloud Deployment

Delivered on secure, dedicated cloud environments (AWS, Azure, or private). Deployments support high-volume processing, automated updates, enterprise integration (PrivateLink/VPN), and role-based access—combining speed with control for production-grade operations.