Live & Online Business Conference - 27 & 28 May 2025 Houston, TX
Following the learnings from the main symposium day, this workshop addresses the critical challenge of gathering and integrating data from various sources into a unified dataset that can be effectively used for predictive maintenance. In the digital transformation era, accurately predicting equipment failures and maintenance needs is pivotal for operational efficiency and cost reduction. However, the diversity and disparity of data sources pose significant hurdles. This workshop will allow practitioners to share their strategies, techniques and tools necessary to overcome these obstacles, ensuring a robust foundation for reliable predictive models and maintenance strategies.
Learning Objectives and Takeaways:
✓ Understand the importance of data quality and consistency for predictive maintenance
✓ Hear best practices for data gathering across diverse sources
✓ Gain insights into effective data integration techniques to create cohesive datasets
✓ Discuss strategies for storing and visualizing data to facilitate analysis and decision-making
✓ Interactive discussions on overcoming common challenges in data collection and integration
Importance of the Workshop: The complexity of integrating data from disparate sources is a significant barrier to deploying effective predictive maintenance strategies. By focusing on real-world examples and interactive problem-solving, attendees will hear how other operators have navigated data diversity challenges, ensuring the accuracy and reliability of predictive maintenance models.
Interactive Solution Sharing Topics:
· Overcoming data silos and ensuring data quality.
· Integrating vibration and infrared data for asset condition monitoring.
· Case studies on successful data integration and predictive maintenance applications.
9:00 - 9:15
Introduction and Workshop Overview
9:15 - 10:00
Session 1: Challenges of Data Diversity in Predictive Maintenance
10:00 - 10:45
Session 2: Techniques for Effective Data Gathering
10:45 - 11:00
Refreshment Break
11:00 - 1145
Session 3: Strategies for Integrating Disparate Data Sources
11:45 - 12:15
Session 4: Data Storage and Visualization for Decision Making
12:15 - 12:30
Wrap-Up and Q&A,
12:30 Lunch
Purpose of the Workshop: This half-day workshop is dedicated to discussing the robustness and reliability of AI-based numerical reservoir simulation models and refining the history matching process. Participants will explore integrating machine learning and numerical simulations in reservoir engineering. The focus will be on ensuring data accuracy, understanding how to maintain model reliability over time, and improving history-matching processes.
Learning Objectives and Takeaways:
✓ Establishing the reliability of AI-based simulation models over time
✓ Advanced techniques for accurate history matching and model validation
✓ Strategies to integrate new data and adapt to evolving reservoir conditions
✓ Translating AI insights into concrete, actionable strategies for production optimization
✓ Enhancing decision-making in production optimization, well placement, and intervention strategies
Workshop Importance: AI and ML have revolutionized reservoir simulation and forecasting, but the challenge lies in the practical application of these technologies. This workshop will enable participants to share their experiences in utilizing AI effectively for enhanced decision-making in oil production. Interactive discussions will facilitate a stronger understanding of how AI can support the prediction of future production and influence operational strategies.
09:00 am - 09:30 am: Introduction to AI in Reservoir Simulation
Understanding AI's role in mimicking real reservoir conditions.
09:30 am - 10:00 am: Robustness of AI-Based Models Over Time
Techniques to ensure long-term reliability of simulation models.
10:00 am - 10:30 am: History Matching: Accuracy and Efficiency
Exploring new methodologies to improve history matching processes.
10:30 am - 10:45 am: Refreshment Break
10:45 am - 11:15 am: Data Integration for Predictive Modeling
How to incorporate evolving data into existing models.
11:15 am - 11:45 am: Interactive Problem-Solving: Real-World Challenges
Group discussions on specific operational problems and AI application.
11:45 am - 12:15 pm: Actionable Strategies from AI Insights
Translating model predictions into strategic decisions for production optimization.
12:15 pm - 12:30 pm: Q&A and Closing Remarks
Open discussion to clarify any outstanding questions and conclude the workshop.
12:30 pm Lunch
Purpose of the Workshop:
This workshop addresses the importance of high-quality data as the foundation for reliable reservoir simulation and production forecasting. Attendees will share their experiences with state-of-the-art data preparation, cleaning, visualization, and anomaly detection methodologies crucial for empowering machine learning applications in the oil and gas industry.
Learning Objectives and Takeaways:
✓ Solutions for cleaning and pre-processing reservoir simulation data
✓ Learn techniques for effective data visualization to uncover key data characteristics.
✓ Identify and manage anomalies to maintain data integrity.
✓ Discover strategies to handle collinearity and improve machine learning model performance.
Importance for Practitioners:
For professionals dealing with vast datasets, prioritizing data cleaning tasks and obtaining clean data from the outset are essential skills. This workshop will provide practical solutions to common problems, such as handling missing values and standardizing data, to ensure consistency across datasets. Interactive sessions will facilitate the exchange of best practices and collaborative problem-solving.
TIMETABLE
1:00 pm - 1:10 pm: Introduction and Objectives
Outline the workshop's goals and structure, emphasizing interactive learning.
1:10 pm - 1:40 pm: Best Practices For Cleaning & Pre-Processing Reservoir Simulation Data
Explore methodologies for collecting high-quality data and prioritizing cleaning tasks.
1:40 pm - 2:10 pm: Data Visualization: Techniques and Tools
Hands-on session on using visualization tools to understand and prepare data for ML models.
2:10 pm - 2:40 pm: Anomaly Detection and Collinearity Removal
Practical exercises on identifying data anomalies and removing collinearity to refine ML models.
2:40 pm - 3:10 pm: Use Of AI algorithms That Standardize Units, Interpolate Missing Values & Harmonize Data From Multiple Sources
3:10 pm - 3:40 pm: Refreshment Break
Networking and informal discussions on data challenges and AI applications.
3:40 pm - 4:10 pm: Interactive Case Studies: Normalisation Of Data
4:10 pm - 4:40 pm: Collaborative Problem-Solving On Noise Filtering & Uncertainty Qualification
4:40 pm - 5:00 pm: Expert Panel: Tailoring The Dataset To The Objectives Of The Simulation & Production Forecasting
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