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  1. LTIMindtree is now LTM | It’s time to Outcreate
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Zero Guesswork

AI-Driven Calibration for Optimized Well Production

Overview

As the oil and industry faces growing operational complexity and pressure to optimize production, legacy methods for calibration of complex production networks are no longer adequate. The client needed an innovative solution to automate the calibration of complex production network models. They partnered with LTM to transform their manual calibration workflows into a streamlined, automated and autonomous operation.

                     LTM’s Solution

We equipped the client with an automated anomaly detection solution for unconventional wells by developing a heuristic Z-score model, powered by Azure AI/ML and Seeq. The solution was deployed to monitor over 1,000 gas-lift wells in the Permian Basin, enabling proactive well management and operational efficiency:

LTM deployed the PSwarm algorithm integrated with Petroleum Experts’ Production Optimization Suite to automate calibration of complex production networks.

A solution designed to consistently reduce the gap between actual and expected production increments by leveraging advanced optimization techniques and real-time feedback loops.

Developed an intelligent algorithm capable of navigating complex network, multi-constraint target functions to identify and implement optimal solutions efficiently.

Cluster management and distributed computing capabilities enabled parallel processing of multiple scenarios, accelerating network calibration and allowing for more frequent updates.

An automated and continuous integration layer that connected real-time systems with other systems of record (SORs), ensuring timely and accurate collection of field and well data.

                       Tech Stack and Architecture

Benefits

1800+ hours per year saved per field by eliminating manual calibration efforts.

Real-time rate prediction reduces unplanned downtime and production losses by allowing teams to address issues before they escalate, ensuring more consistent and optimized well performance.

Improved model reliability improved model reliability with minimal human intervention ensures more accurate and consistent production forecasts, reduces operational errors, and frees up engineers to focus on higher-value tasks.

Cluster-based parallel processing helped with faster and more frequent calibration through cluster-based parallel processing enables quicker optimization cycles, leading to more timely production adjustments and maximized well output.

Reduced model-to-field error percentage helped in lowering the model-to-field error percentage leads to more accurate optimization and better decision-making, resulting in improved and more consistent well production.

                       Conclusion

LTM’s AI-powered calibration solution, built on the PSwarm algorithm and integrated with PETEX, helped the client automate one of the most time-consuming and complex tasks in subsurface operations. It enabled real-time rate predictions, improved model accuracy, reduced model-to-field errors, and saved over 1,800 hours per field annually. The solution’s scalable, data-driven architecture accelerated decision-making across joint venture-operated wells and drove better production outcomes. This case underscores how AI and intelligent automation can modernize core workflows in oil and gas, setting a new benchmark for operational excellence in the energy industry.

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