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Business Context & Goals

  • Upstream companies face constant challenges (e.g. equipment failure, well underperformance, and other well servicing requirements) to maintain the hydrocarbon production from wells as per plan. When “liquid loading” occurs in wells in a Producing field, the operator needs to find an optimum schedule for performing a workover service(e.g. swabbing) that optimizes the revenue loss and workover cost against the potential production gains subsequent to the workover operation.

Business Challenges

  • Limited ability to apply the knowledge from historical workovers and arrive at optimal workover plan. Limited ability to react fast and communicate with service providers for changes to workover schedule.

Process-Innovations

  • Analyze historical data on previous workovers using statistical techniques and use this knowledge to predict behavior of planned workovers. Arrive at the optimal workover plan for servicing multiple wells in the field and communicate with Service Providers.

Contribution of HANA/BW

  • Analyze historical data on previous workovers using statistical techniques and use this knowledge to predict behavior of planned workovers. Arrive at the optimal workover plan for servicing multiple wells in the field and communicate with Service Providers.

Value Drivers/KPIs

  • Reduce Workover Operations Cost as % of Revenue.