Agenda
Monday, May 23
In this session, industry technical executives will share current states and challenges of progressing advanced analytics, cultural changes, and cross-discipline applications, giving their views on current battle lines and the next steps to address. Presentations will be from a range of companies followed by a panel discussion with the presenters.
Panelist 1: Kevin Chambers, Chevron
Presentation 1: Subsurface Data Analytics: Applications and Opportunities
Panelist 2: Trygve Randen, Schlumberger
Presentation 2: Agile Field Development Planning Powered With Data-Driven Analytics
Panelist 3: Med Kamal, 2023 SPE President
Presentation 3: Data Analytics: Necessary and Sufficient?
This session will present recent advances applying MLS algorithms in reservoir characterization and modeling. Presentations will focus on areas of data integration including fibre well data, micro-seismic, petrophysics, core, and field diagnostics such as pressure fall-off to develop detailed static models of rock, fluid, and mechanical properties.
Presenter 1: Charles Kang, ResFrac
Presentation 1: Optimizing Shale Economics with an Integrated Hydraulic Fracturing and Reservoir Simulator and a Bayesian Automated History Matching and Optimization Algorithm
Presenter 2: Mehrdad Zamirian, West Virginia University
Presentation 2: Prediction and Management of Frac-Hit using AI and Machine Learning
Presenter 3: Adam Halpert, Chevron
Presentation 3: Deep Learning for Seismic Interpretation Workflows
The session explores real world applications and methods in predicting well performance. Can data driven analytics go beyond automated history matching and pattern recognition? Case studies include the integration of production, geology and completions with discussions on successes and failures.
Presenter 1: Shahab Mohaghegh, West Virginia University
Presentation 1: Modeling Physics with Artificial Intelligence Versus Combining AI with Traditional Engineering Approach (Hybrid models); Reality Versus Fiction
Presenter 2: Sathish Sankaran, Xecta Digital Labs
Presentation 2: Tracking Well Performance Using Hybrid Reservoir Models – Applications in Conventional and Unconventional Reservoirs
Presenter 3: Birol Dindoruk, University of Houston
Presentation 3: Prediction of Transport Properties and Physics Based Conceptual Elements For Flow in Porous Media Using Hybrid Approaches
Without question, the analytics solutions are increasingly pervasive and sophisticated, but the data, uncertainties and fundamentals of the petroleum industry make the application of analytics distinct in the petroleum industry. This session will begin a presentation followed by an open discussion of utilizing AI/ML in the petroleum industry.
Presenter 1: Mariano Gurfinkel, Marathon
Presentation 1: Better Decisions Through Machine- Learning Based Predictions and Forecasts of Fracture Stimulated Horizontal Wells: Are We There Yet?
Presenter 2: John Hudson, Shell
Presentation 2: Analytics Supporting Clarification of Next Learning Opportunities
Presenter 3: Michael Pyrcz, University of Texas at Austin
Presentation 3: Data Analytics and Machine Learning, The Subsurface is Unique
Tuesday, May 24
In this session, industry technical leaders will discuss the importance of carbon reduction, CO2 sequestration and energy transition and discuss how applications of Artificial Intelligence and Machine Learning to this challenges. Technologies and cases studied will reflect the current state, challenges, and opportunities to deploying digital solutions as enablers. On open floor dialog is intended to allow sharing of the current state of applications in different organizations.
Presenter 1: Iraj Ershaghi, University of Southern California
Presentation 1: De-Risking Subsurface Carbon Dioxide Storage Operations
Presenter 2: Grant Bromhal, U.S. Department of Energy
Presentation 2: Applying Science-informed Machine Learning to for Carbon Storage
Presenter 3: Shahab Mohaghegh, West Virginia University
Presentation 3: CCS-Analytics; AI-based Carbon Capture and Storage
In this session, industry experts share their experiences in analyzing filed data with AI and ML to undestand complex patterns and build predictive models which are used to optimize drilling, completion and field development operations.
Presenter 1: Cesar Velasquez, S&P Global Commodity Insights
Presentation 1: Leverage Machine Learning Models to Identify Production Performance Drivers and Completion Strategy
Presenter 2: Siddharth Misra, Texas A&M University
Presentation 2: Machine-Learning Assisted Monitoring of Subsurface Flow and Fracture Processes
Presenter 3: Ted Cross, Novi Labs
Presentation 3: How Does the Impact of Completions Change Over the Life of a Well? A Comparison Across the Major US Unconventional Plays Using Machine Learning
In this session, the discussions will be on managing the human factor issues related to reducing demand on the human using automation and also the expectation of increased performance with the advent of digital information technologists.
Presenter 1: Jim Crompton, Consultant
Presentation 1: In a World Where AI is a Co-worker What is the Role and Challenges of the Human?
Presenter 2: Pallav Sarma, Tachyus
Presentation 2: Accelerating Adoption of Data Science and Analytics in the Oil and Gas Industry
Presenter 3: Naj Meshkati, University of Sounthern California
Presentation 3: Lessons Learned and Advances in Human Factors and Safety Culture in Process Safety Management (PSM) in Petroleum Industry: From Up- to Downstream
Presenter 1: Chet Ozgen, Nitec LLC
Presentation 1: Beyond Data Analytics; Fast Multi-objective Decision Making at Higher Dimensions
Presenter 2: Sarath Ketineni, Chevron
Presentation 2: Real-world Applications of Data Science in Reservoir Engineering and Lessons Learned from SPE Datathons