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AI Portfolio Optimization in Oil & Gas to Yield $1B Growth

Background:

Project Geminae redefines how businesses view and solve their portfolio optimization problems with a current focus on oil and gas production. The project involves the development of a new tool utilising machine learning to predict oil, gas, and water production based on historical data.

Before:

  • Model performance was inadequate, failing to meet customer expectations and contractual obligations.
  • The existing model was unable to make accurate predictions for oil, gas, and water production.
  • Lack of model interpretability hindered user trust and understanding.
  • The system lacked a user-friendly visualization tool for explaining model predictions.

After:

  • Significantly improved model accuracy: predictions now closely match actual production values for oil, water, and gas.
  • The improved model demonstrated the potential to increase earnings by over a billion dollars over a 10-year period.
  • Successfully implemented a visualization tool for explaining model predictions, increasing user understanding and trust.
  • The new model is interpretable and traceable, allowing users to understand the logic behind predictions.
  • Enhancements in data quality and preprocessing contribute to the model’s improved accuracy.
  • Integrated accurate predictions for oil, water, and gas production into a single, comprehensive model.
  • Increased customer satisfaction due to improved reliability and value of predictive services.

Process:

  • Comprehensive review and enhancement of the model’s architecture, algorithms, and performance, as well as a thorough analysis of the underlying data.
  • Development of new features to increase model interpretability and user-friendliness, including a visualization tool for explaining model predictions.
  • Enhancement of the machine learning model to improve accuracy and reliability in predicting oil, water, and gas production.
  • Code optimization for better efficiency and maintainability.
  • Provision of expert suggestions for further model improvements and implementation of best practices in energy sector predictive modeling.
  • Documentation and knowledge transfer to ensure the client’s team could maintain and further develop the enhanced model.
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The improved model demonstrated the potential to increase earnings by over a billion dollars over a 10-year period.

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