Pablo Barragan

Portfolio

Oil & Gas Dashboard

Objective

This dashboard centralizes and automates the processing of oil, gas, and water production data, replacing manual Excel-based monthly reporting with daily updates. It provides a consolidated view of key production and injection metrics, reducing manual workload.

Scenario

Previously, the operational area managed production and injection reports manually using Excel, with data downloads and report generation occurring only on a monthly basis. This process was time-consuming and offered limited visibility into daily performance.

Workflow

  • Data Extraction: Daily production and injection data is automatically downloaded from source systems using Python scripting and loaded into the Microsoft Fabric Data Lake.
  • Data Transformation & Warehousing: Data within the Fabric Data Lake is transformed and cleaned using Python and then loaded into a structured Data Warehouse within Fabric.
  • Modeling & Reporting: The clean and structured data in the Fabric Data Warehouse is connected to Power BI, where key production and injection metrics and trends are visualized in a dynamic dashboard.

Key Metrics & Visuals

  • Oil prod / day (m³): Daily volume of oil produced.
  • Gas prod / day (Mm³): Daily volume of gas produced.
  • Water prod / day (m³): Daily volume of water produced.
  • Water inj. / day (m³): Daily volume of water injected.
  • Active wells: Count of wells with any production during the selected period.

Technologies Used

  • Data Extraction: Python scripting.
  • Data Lake: Microsoft Fabric Data Lake.
  • ETL & Data Warehousing: Microsoft Fabric.
  • Reporting & Modeling: Power BI.

Business Impact

  • Enhanced Timeliness: Transitioning from monthly to daily reports provides near real-time visibility into key production metrics (oil, gas, and water), enabling quicker identification of trends and potential issues.
  • Improved Responsiveness: Daily data allows for more agile decision-making.
  • Increased Efficiency: Automating the data download and transformation processes with Python and Fabric significantly reduces the manual effort previously required.
  • Data-Driven Optimization: With readily available daily data on production and injection, teams can more effectively analyze the impact of operational changes and optimize production strategies.