Enabling Sustainable Production: A Carbon Footprint Calculation Framework for Remote ESP Operations

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:A novel architecture combining edge computing and sustainability analytics for real-time emissions quantification.

🏢実務担当者:Corporate sustainability teams can adopt this framework for Scope 1 reporting and operational efficiency in remote oilfields.

🏛政策担当者:Regulators may consider this as a model for mandating real-time emissions monitoring in the oil and gas sector.

This paper presents an integrated digital solution leveraging Industrial Internet of Things (IIoT) and edge computing to enable real-time carbon footprint calculation during remote Electric Submersible Pump (ESP) operations in mature oilfields. The objective is to quantify carbon dioxide (CO₂) emission reductions, particularly those associated with reduced truck usage in ESP surveillance workflows and demonstrate the feasibility of aligning oilfield production activities with global sustainability goals, including Scope 1 reporting under the Greenhouse Gas (GHG) Protocol (GHG Protocol, n.d.). The methodology combines domain expertise in ESP workflows with the deployment of an IIoT platform incorporating edge gateways, sensors, and cloud-based visualization tools. Real-time operational data such as ESP frequency adjustments, downhole pressures, and temperatures are captured, processed, and analyzed. These data inputs feed autonomous algorithms running at the edge to classify and timestamp remote operations. This information pertaining to remote operations is utilized on the cloud to calculate saved diesel fuel from avoided trips and convert it into CO2-equivalent emissions using Intergovernmental Panel on Climate Change (IPCC) (IPCC, n.d.) and GHG Protocol standards. The implementation was conducted across 250 wells in Latin America with various Variable Speed Drive (VSD) vendors, enabling harmonized control, remote optimization, and daily emissions reporting at a granular level. The solution resulted in a sixfold increase in reported monthly CO2-equivalent savings, from 0.41 to 2.45 tons, after IIoT adoption. Operators were able to remotely execute core ESP control operations such as ramping frequencies, shut-ins, and startups, thereby minimizing field visits. In addition to lowering emissions, the system delivered value through enhanced production visibility, faster decision-making, and increased equipment uptime. The use of time-bounded logic prevented overestimation of carbon savings. Reports generated daily include timestamped command logs and calculated emissions per well. This deployment not only reduced operational risk but also laid a scalable foundation for achieving Environmental, Social and Governance (ESG) and Sustainable Development Goals (SDGs) climate objectives in remote oilfields. This paper introduces a novel architecture that merges edge computing with sustainability-driven analytics, enabling digital measurement of Scope 1 emissions tied to specific remote ESP commands. By differentiating remote actions from physical interventions, the system quantifies emissions reductions in real-time, filling a key gap in existing GHG accounting practices. This methodology can be extended to other artificial lift systems and upstream workflows to further enhance environmental accountability in oilfield operations.

• semanticscholar https://doi.org/10.2118/232528-msfirst seen 2026-05-23 05:54:41 · last seen 2026-06-16 05:13:12