No More Blind Spots
AI-Driven, Real-Time Well Rate Estimation with ML.NET

Overview

In the oil and gas industry, accurately estimating well rates is critical for
optimizing production and managing performance, but it remains a persistent challenge across many
subsurface operations. The client faced challenges in well rate estimation for wells
lacking multiphase meters. They partnered with LTIMindtree for an innovative, AI-powered solution to estimate real-time production rates.

LTIMindtree’s Solution

LTIMindtree developed a well rate estimation solution using ML.NET to provide real-time estimates for wells lacking multiphase flow meters. Key aspects of the solution included:

ML-Based Rate Estimation Engine

Deployed machine learning models that generated real-time well rate estimates for wells lacking Multiphase Flow Meter (MFPM) instrumentation or those operated by third parties.

Automated Model Lifecycle Management

Implemented automated pipelines for continuous training, validation, and updating of ML models to ensure sustained accuracy and adaptability.

High-Frequency Output and Accuracy Monitoring

Enabled high-frequency rate generation with integrated accuracy statistics to support operational decision-making and performance tracking.

Alternative Production Evaluation Methodology

Provided a reliable substitute for traditional production evaluation techniques, enhancing visibility into well performance across diverse asset types.

Business Challenges

Despite a clear vision for modernization, the client’s data environment remained fragmented and operationally rigid. The absence of a centralized Surface Data Hub meant that vital operational data, ranging from equipment and tag information to work orders, was scattered across disconnected systems. This fragmentation weakened collaboration, slowed decision-making, and limited the effectiveness of analytics-driven initiatives.

Key pain points included:

Disparate Systems

Critical equipment, tag, and work order data were locked within different systems, making it hard to get a single, accurate view of operations.

Poor Data Quality

Inconsistent naming conventions, missing attributes, and duplicating records undermined data confidence.

High Integration Costs

Teams spent significant time and resources manually connecting systems and reconciling data, increasing project costs and slowing delivery.

Security and Compliance Risks

With no centralized governance, enforcing data access policies or tracking audit trails became complex and risky.

Limited Scalability

Existing infrastructure struggled to support real-time analytics or scale predictive models, restricting growth and agility.

Governance and Empowerment Deficiency

The absence of structured governance kept data teams reactive rather than empowered, reducing innovation potential.

Tech Stack and Architecture

Benefits

Real-Time Visibility

Enabled faster decision-making and operational responsiveness by eliminating delays from daily allocation cycles.

Transparent Analytics

Improved stakeholder trust and auditability by making model logic and outputs easy to interpret and validate.

Alternative Evaluation Method

Provided a scalable and cost-effective substitute for MFPM-based assessments, especially for third-party or non-instrumented wells.

Conclusion

LTIMindtree’s well rate estimation solution, built using ML.NET, gave the client a smart, reliable way to monitor production in wells without multiphase flow meters—many of which were operated by third parties. By automating the full lifecycle of the machine learning models and enabling high-frequency, accurate rate estimates, the solution offered real-time visibility and analytics that traditional methods couldn’t provide. This solution highlights illustrates how energy companies can use AI and machine learning to unlock greater agility, precision, and scalability in production monitoring across complex upstream portfolios.

No More Blind Spots

Overview

LTIMindtree’s Solution