Client

The client was an oil and gas super major with operations worldwide across the entire energy value chain–upstream, midstream and downstream. 

Market Dynamics Driving Change

The energy industry is undergoing a rapid digital transformation. With increasing pressure to optimize production, reduce downtime, and ensure compliance, oil and gas companies are turning to AI and ML to manage vast data volumes and complex operations. This sets the stage for digital transformation initiatives that leverage AI-ML in the energy sector to address core challenges and drive operational efficiency, enhance decision-making, and future-proof production operations workflows.

             Business Challenges

The client’s legacy production and data management processes were constrained by several inefficiencies that limited operational efficiency and hindered timely decision-making. Despite the availability of large volumes of production and reservoir data, the absence of integrated tools, automation, and real-time insights created significant bottlenecks across engineering, analytics, and compliance workflows. These challenges collectively impacted productivity, visibility, and overall business agility, as listed below:

Lack of real-time visibility into well performance, especially in JV-operated wells

Limited access to daily allocation data made it difficult to monitor and optimize production effectively. Difficulty in well rate estimation for wells lacking multiphase meters.

Anomaly detection in unconventional wells required intensive manual surveillance

Engineers had to manually scan data to identify underperforming wells, increasing operational overhead.

Difficulty in managing and optimizing unconventional assets

Difficulties due to a high well count, relatively low production rates, rapid decline in production during the first three years of the well (circa 70%), minimal to nil subsurface sensors, and high volume of maintenance activities.

Estimating reservoir pressure is difficult due to rapid declines and limited data

Traditional methods lacked precision, making it hard to forecast inflow performance and plan interventions.

Manual calibration of production networks is time-consuming and error-prone

Engineers had to frequently intervene to adjust models, leading to delays and reduced productivity.

                   Our Solutions

LTM implemented four solutions that leveraged AI and ML to surmount the client’s challenges. Solutions enabled production, process and reservoir engineering teams to take swift decisions, instead of spending time in data wrangling and performing manual analysis with minimal to no governance and/or audit trails. These digital solutions empowered existing teams to manage additional oil wells and assets.