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 LTM for an innovative, AI-powered solution to estimate real-time production rates.
LTM 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:
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.
Implemented automated pipelines for continuous training, validation, and updating of ML models to ensure sustained accuracy and adaptability.
Enabled high-frequency rate generation with integrated accuracy statistics to support operational decision-making and performance tracking.
Provided a reliable substitute for traditional production evaluation techniques, enhancing visibility into well performance across diverse asset types.
Enabled faster decision-making and operational responsiveness by eliminating delays from daily allocation cycles.
Improved stakeholder trust and auditability by making model logic and outputs easy to interpret and validate.
Provided a scalable and cost-effective substitute for MFPM-based assessments, especially for third-party or non-instrumented wells.
LTM’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.
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