BackgroundA leading energy company observed a persistent decline in netbacks for a portion of its assets. Despite recognizing the downward trend, the root cause remained unclear due to the sheer volume of operational and financial data. With vast amounts of information to analyze, the company faced significant challenges in pinpointing the specific factor driving the decline. | ![]() |
The ChallengeThe company had accumulated over 7,500 individual statements spanning two years and multiple processors. Manually reviewing this volume of data to identify anomalies or discrepancies was simply not feasible. The complexity of midstream revenue calculations—ranging from variable processing fees and gathering charges to evolving contract terms—further compounded the challenge, making accurate and timely analysis nearly impossible using traditional methods. |
SolutionTo tackle the scale and complexity of the data, the company engaged AEGIS and deployed its Revenue Capture solution to streamline the normalization and analysis of revenue data. Within just two hours of receiving more than 7,500 POP statements, AEGIS delivered the following: Statement Ingestion & NormalizationAI-driven ingestion and normalization of POP statement data from multiple midstream processors resulted in a structured, consistent dataset—ready for rapid analysis. Revenue Analytics and InsightsThe processed data was seamlessly integrated into the AEGIS platform and API, centralizing all revenue-related information into a single, accessible interface. Trending TimelinesAutomated generation of operational trending reports and revenue/yield timelines provided a clear, comprehensive view of financial performance across assets and time. |
Results & ImpactBy leveraging the AEGIS Revenue Capture solution, the company transformed more than 7,500 disorganized statements into clear, actionable intelligence—empowering faster, more accurate revenue analysis and decision-making. Enhanced Operational EfficiencyAutomated data processing drastically reduced manual effort, minimized the risk of errors, and freed up internal resources. Greater Data AccuracyStructured datasets provided a reliable foundation for deep analysis, supporting confident and defensible decisions. Proactive Revenue ManagementWith real-time insights at their fingertips, the team was able to detect and resolve revenue-impacting issues quickly. Rapid Detection of a Contractual Adjustment:The structured dataset enabled immediate identification of two critical issues: Contract Modification UncoveredA previously overlooked contract adjustment had introduced a new gathering fee, directly affecting netbacks. Decreasing NGL YieldsA shift in gas composition revealed a drop in GPM, contributing to a noticeable month-over-month revenue decline. |
|
|
|