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  • Methane Emissions Management for Global Energy Company

The client

The client is a US-based onshore oil and gas business, known for their environmentally conscious production. They currently operate in the Permian, Eagle Ford and Haynesville basins with a focus on safely producing high-value oil and gas while reducing emissions.

The client, through its parent company, has a charter to reach net zero by 2050. One of these goals is to undertake advanced methane measurement and push towards a 50% reduction in methane intensity of operations. The client required a solution to monitor and manage methane detections in their operations.

Need for change

Methane is a potent greenhouse gas, with a global warming potential approximately 30 times greater than carbon dioxide over a 100-year period, according to the UN Intergovernmental Panel on Climate Change (IPCC). At COP26, countries worldwide committed to the Global Methane Pledge (GMP), aiming to reduce methane emissions by 30% by 2030. The oil and gas sector is responsible for about one-quarter of all methane emissions from human activities. A Bloomberg report highlights that approximately 80% of methane emissions from this sector arise from upstream production, including methane fugitive emissions and activities such as venting and flaring. 

North America faces stringent regulations and steep fines regarding methane, and publicly traded oil and gas companies are also under increasing pressure from investors to reduce methane emissions, or “demethanize” their operations. Companies here are leading the investment and adoption of advanced methane detection systems, particularly in key oil-producing regions such as the Permian, Anadarko, and Appalachian basins in the U.S.

As an upstream oil and gas company, the client dedicates significant resources to monitoring and reducing methane emissions to meet regulatory targets and industry standards. A specialized team within their HSE and carbon portfolio is focused on managing methane emissions to support the company’s net-zero and OMGP 2.0 goals, while ensuring alignment with EPA LDAR and the Super Emitter Program requirements. This called for a robust methane data management system enabled the timely capture, accurate tracking, and effective reporting of emissions.

Need for change

Challenges

  • Data from methane detections from multiple external and internal sources were difficult to transmit, maintain and streamline. 
  • Delay in visibility of methane detections due to lag of data transfer. 
  • Lack of holistic visualization of data from all sources.
  • The lack of a data architecture for the drone program resulted in several pain points across the business and a significant loss of value from the ~$400,000 program in 2024. This included data errors, resulting in a significant amount of time spent by the field teams conducting follow-up inspections on the wrong locations.
  • There were delays in the work management flow getting triggered as there wasn’t a seamless process of data transmission between the third-party methane captures and in-field inspections, as the field teams received data several weeks after the locations had been flown.
  • Lack of integration of the work management flow in the dashboard, leading to data gaps. 
  • Fines due to slow response to leak detection and mitigation.

LTIMindtree’s solution

LTIMindtree worked with the HSE and carbon team to build the end-to-end architecture and workflow for a methane emissions management system. This included the automation of data transfer from multiple sources of data for methane such as drones, sensors, manned and unmanned aircrafts and public satellite detections. The solution’s robust data pipeline and data transfer and consolidation logic ensured correct attribution of detections to specific equipment and facilities. Security considerations through virus scans for external data, and masking policies to adhere to regulations around midstream data visibility were rigorously maintained. The legacy system data transfers took place through the SFTP path or via API transfers. We introduced a Snowflake-to-Snowflake transfer for tabular data with a vendor for speed of transfer and heightened data security measures.

LTIMindtrees solution

Phase 1: Automating data capture and visualization

The first phase of the methane program involved automating the capture of methane data from multiple sources, including airplanes, drones, sensors, IoT devices, and external/public agencies. This created a centralized repository for methane emissions data. As part of this ongoing effort, new data sources were integrated in collaboration with the business. Data capture was accomplished through methods such as API pulls, Snowflake-to-Snowflake transfers, and S3 bucket integration for non-tabular data like images and geospatial files. Additionally, visualization dashboards were created to present methane emissions data, automating its inclusion in mandatory GHG reporting at the organizational level.

Phase 1: Data integration architecture with methane detection systems

Phase 1: Data integration architecture with methane detection systems

Phase 2: Work management flow for methane emissions

The methane data then seamlessly engaged with our work management ecosystem built on Enterprise Asset Management (EAM) Solutions and SAP. This was achieved via an automated workflow for on-field inspections via field operations teams in midstream and upstream, as well as subsequent work order generation when needed. Both types of data then flowed into the methane dashboard, an analytical dashboard that illustrated data across the lifecycle of a methane detection. This dashboard offered multiple levels of drill-downs and analytical views. Work management workflows were integrated with the methane dashboard, allowing for real-time updates on detection statuses and remediation progress.

Phase 2: Work management flow for methane detections

Phase 2: Work management flow for methane detections

Tech Stack

  • Backend technologies: Snowflake-to-Snowflake share, SQL, API integration, S3/SFTP, python, PowerAutomate, SharePoint
  • Front-end technologies: PBI
  • Cloud resources: AWS (Lambda, S3/SFTP, terraform, SQS, SNS, IAM Roles)
  • DevOps: CI/CD pipelines, Azure DevOps

Benefits

 

Robust data architecture with approved flows that could be easily integrated with new data sources for timely and accurate receipt of data.

Reduced manual efforts by the HSE and carbon team, and the field operations team to receive, process, analyze and share data. This led to savings of over 400 hours every year.

Reduced manual efforts spent by field operations in end-to-end work management flow for methane detections. This led to savings of 320+ hours every year.

Total man hours reduced across teams was 700+ hours per year.

Reduced man hours led to $140,000 savings per year.

Fine avoidance for super emitter program ($196,560 fee avoidance per super emitter event) due to closing the gap between leak detection and remediation.

Conclusion

The comprehensive methane detection and management system developed by LTIMindtree has significantly enhanced the client’s ability to monitor, report, and address methane emissions. By automating data capture and integrating it with efficient work management flows, the client has streamlined operations, reduced manual effort, and avoided costly fines. These improvements have not only contributed to substantial annual savings but have also propelled the company closer to its net-zero goal, highlighting their ongoing commitment to sustainable and responsible energy production. This solution serves as a model for other organizations aiming to mitigate methane emissions and achieve greater environmental stewardship in the oil and gas industry.

Testimonials

 

- Senior Low Carbon Engineer

The set-up of the methane workflows was a great team effort. It took time and patience to put all the pieces of the puzzle together and I think it has turned out to be a great solution. Thank you for all your help. Since we are now a well-oiled machine, I am hoping future enhancements would be far easier, since we have a good foundation to work on.

- Product Owner, Data Platforms Squad

The squad did a great job in setting up the Snowflake-to-Snowflake data share. This is a best practice that we would encourage across the organization, and this would serve as a great case study. I appreciate the management the complicated conditions around data security involved in this project.

Ready to take control of your methane emissions management with the latest technology?

Contact eugene.comms@ltimindtree.com to know more.

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