2018年-普华永道全球_Predictive_Analytics_10页_580kb

Enhanced Profit Recovery through Predictive Analytics

Core Content

Predictive analytics has become increasingly popular in the energy sector due to advancements in cloud computing, IoT, sensor technology, and downhole communication. These technologies enable the integration and conversion of vast, disparate data sources—such as seismic data, core floods, production history, and well logs—into actionable insights that support better decision-making.

The application of predictive analytics is particularly relevant in upstream and oil field services, where the complexity of subsurface environments and data sources necessitate advanced analytical approaches. It offers a range of benefits, including improved operational efficiency, reduced maintenance costs, and increased capital and inventory efficiency.

Main Points and Key Applications

1. Assessing Opportunities

• The feasibility and value of predictive analytics should be evaluated by examining the data infrastructure and identifying key pain points across maintenance, supply chain, commercial, and operations.
• A holistic view of the business is essential to identify where predictive analytics can be operationalized for maximum impact.

2. Use Cases for Predictive Analytics

Threshold Refinement - A Quick Win Opportunity

• Assets have operational thresholds for parameters like temperature, pressure, vibration, and circulating hours.
• These thresholds can be refined using a data-driven approach, particularly through survival analysis, which estimates the likelihood of failure at different threshold levels.
• Increasing thresholds can reduce maintenance events and costs, while still maintaining asset reliability.
• Quick win benefits: More than 5% savings on total maintenance costs.

Predictive Modeling - An Advanced Capability

• Predictive models estimate the remaining life of assets and identify components at high risk of failure.
• These models help in targeted repairs, reducing unnecessary maintenance frequency and costs.
• Advanced benefits: Increased operational efficiency, reduced maintenance and repair costs, improved capital efficiency, and reduced inventory.

3. Application in Maintenance

• Predictive maintenance programs use models to determine when assets need inspection and to route them to appropriate maintenance facilities.
• The model also aids in troubleshooting by narrowing down potential problem areas.
• Expected benefit: 15%–20% increase in maintenance velocity.

4. Application in Operations

• Re-run optimization: Predictive models allow operators to better assess asset health, enabling more re-runs and reducing reliance on backups.
  • Expected benefit: Up to 10% savings in maintenance cost.
• Deployment strategy optimization: Assets can be deployed strategically based on their remaining life, reducing the need for backups.
  • Expected benefit: Up to 30% reduction in asset fleet size.

5. Application in Material Management

• Predictive analytics improves spare parts forecasting by predicting short-term material consumption.
• This leads to a more accurate demand signal and reduced safety stock inventory by up to 15%.

Key Considerations

1. Data Infrastructure and Governance

• A mature data infrastructure and governance are essential for successful implementation.
• Components include:
  • A coherent data strategy with data infrastructure improvement initiatives.
  • A central data repository with common data views.
  • A cloud platform for advanced analytics.
  • Staff with clear responsibilities for data ownership and validation.
• Data collection should shift from "what we can collect" to "what we need to collect".
• Challenge: Poor data strategy and governance can delay the development of predictive capabilities, with data preparation taking up to 6 weeks.

2. Asset Selection and Prioritization

• Predictive analytics is not equally applicable to all assets.
• Assets are segmented based on inspection rates and incident rates:
  • Q1: Ideal state (low inspection, low incident) – minimal need for predictive analytics.
  • Q2: Failure prediction modeling opportunity (low inspection, high incident) – high value from predictive modeling.
  • Q3: Failure prediction modeling opportunity (high inspection, high incident) – moderate value.
  • Q4: Threshold refinement opportunity (high inspection, low incident) – high value from threshold refinement.

3. Organizational Readiness

• Organizations must transition from reactive to proactive maintenance methodologies before implementing predictive analytics.
• Reactive maintenance involves unplanned repairs and firefighting.
• Proactive maintenance includes basic care, preventive, and predictive approaches, with the latter being the most advanced.
• Expected benefit: A shift to proactive maintenance enables more efficient and cost-effective operations.

Conclusion

Predictive analytics is a powerful tool for enhancing operational efficiency and profitability in the energy sector. It should be selectively applied to support key asset classes and address specific business requirements. With a well-planned and executed program, organizations can achieve greater than 20% EBITDA increases, lower breakeven points, and greater competitiveness and agility in the market.