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Well Cost Optimiser

About the Customer

Companies in the Oil and Gas market segment

Solution in Brief - AI driven cost analytics to improve capital efficiency, enable efficient risk management and help proactive interventions in well planning and budgeting.

Business Challenges

  • High cost variance between authorisation for expenditure (AFE) and actual well cost, on an average +/- 25% (Budget vs Expendture)
  • Limited ability to validate AFE using historical well data, and to perform data driven estimation for a new planned well
  • High lead times to derive cost estimates from well collected data

Solution Description

Consolidate, cleanse data and create descriptive analytics

  • Discover and integrate available data of wells from various sources
  • Perform exploratory analysis and data quality assessment to create descriptive analytics

Develop Predictive Analytics

  • Conduct feature selection and feature engineering to condense the list of useful model inputs
  • Create well cost models using various structures and algorithms; assess model performance by comparing actual vs. predicted results
  • Select the best-performing model; interpret model parameters to derive actionable insights
  • Derive analytics-driven historical data, cost trends and patterns to support cost planning for existing/new wells and predict new well's cost variance.

Value Delivered

  • 8-10% savings on total well spend
  • Higher AFE accuracy, up to 30% reduction in cost variance
  • Data science-led predicted cost variance allows for more accurate AFEs
  • Single platform for data entry and consolidation

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