Schedule |19-20 Nov 2019 | San Antonio, Texas
Tuesday, November 19
There is a growing trend toward the use of statistical modeling and data analytics for oil and gas (and related subsurface domain) applications. This session will focus on the state of the art applications of converting data into information—particularly the actionable kind that lead to better decisions.
Presentation 1: Reservoir Flow Simulation, Data Analytics and the Gantner Hype Curve
Presenter: Larry Lake, University of Texas
Presentation 2: Robust Machine Learning for E&P Problems – Are We There Yet?
Presenter: Jared Schuetter, Battelle
Presentation 3: Changing Subsurface Engineering through Science-Informed Machine Learning
Presenter: Grant Bromhal, U.S. Deparment of Energy
Embedding physics into data driven models and /or extracing physical insights from data are becoming an integral part of machine learning approches in the Oil and Gas industry. Such approaches aim to allow better extraction of signals from noisy data, generate models with long term predcitive capacity, enable models to be more interpretable and generalizable, and help in discovery of unexposed reservoir dynamics. This session explores new approaches to physics-based and data-driven models and combinations thereof that effectively embeds physical constraints, conservation laws and constitutive relations with data. The discussion will be showcasing several key challenges aimed at finding the best compromise between accuracy, efficiency and interpretability of a new generation of models.
Presentation 1: Deep-learning-based Surrogate Models for History Matching and Production Optimization
Presenter: Lou Durlofsky, Stanford University
Presentation 2: Deep Learning of Subsurface Flow via Theory-guided Neural Network.
Presenter: Haibin Chang, Peking University
Presentation 3: Fast, Deep Learning Based, Reservoir Simulation
Presenter: Jean-Marie Laigle, Belmont
This session presents various practical applications where machine-learning and physics-based modeling have been combined to offer an innovative technical solution to an existing technical challenge. Machine learning provides a fast and flexible framework to solve applied technical problems but physics-based modeling has the added advantage to contain additional information about the nature of the problem being solved. The presentations will demonstrate how combining the approach can offer superior insights that are timely and actionable for field development, reservoir management or operation.
Presentation 1: Hybrid Modeling: Challenges and Opportunities in the Subsurface
Presenter: Shahram Farhadi , Beyond Limits
Presentation 2: Augmented AI in Reservoir Management Decision-Making
Presenter: Hamed Darabi, QRI
Presentation 3: Physics Embedded Machine Learning for Modeling and Optimization of Oil and Gas Assets
Presenter: Pallav Sarma, Tachyus
This session focuses on new and emerging data types such as DTS/DAS, time-lapse seismic, novel tracers and others that go beyond traditional approaches for reservoir characterization, flow profiling, and production management. The technology implementation, data processing, analysis, interpretation and challenges of these new approaches are the focus of this session.
Presentation 1: Integrating DNA Diagnostic with Traditional Reservoir Workflows
Presenter: Hasan Shojaei, Biota
Presentation 2: Use of Surface Drilling Data to Inform Real-Time Geosteering/Lithology Identification
Presenter: Deepak Devegowda
Presentation 3: Measuring Inflow Distribution and Identifying Location of Water Influx Utilizing Intelligent Tracers Embedded in Completion Components
Presenter: Brock Williams
Wednesday, November 20
Developing unconventional plays requires analysis of production data and understanding reservoir characteristics to forecast well performance and make better decisions about acreage selection, targeting, completions design, well spacing and operational strategy. This session covers state-of-the-art techniques and workflows for modeling unconventional reservoirs using a blend of physics-based and data-driven methods to derive meaningful insights. Discussions will include applications of these methods to operational fields along with their strengths and weaknesses and illustrate the importance of interpretability to support business decisions.
Presentation 1: AI-Based Field Development and Optimization in Unconventional Reservoirs
Presenter: Hector Klie, Deepcast.ai
Presentation 2: Unconventional Data-Driven Well Performance Analysis
Presenter: Diego Molinari, Anadarko
Presentation 3: A Physics-Based Data-Driven Model for History Matching, Prediction and Characterization of Shale and Tight Reservoirs
Presenter: Yanbin Zhang , Chevron
Understanding reservoir dynamics is paramount in order to successfully predict production behavior and optimize field development which has traditionally relied on integrated simulation modelling. With recent advances in data analytics and artificial intelligence, data-driven models have become more widely-used in analysis, predictive modeling, and optimization. This session will explore different ways, data driven and physics based models can be brought together for an enhanced understanding on factors that impacts dynamic reservoir behavior.
Presentation 1: Physics Informed Deep Learning and its Application to Flow and Transport in Porous Media
Presenter: Cedric Fraces, Stanford University
Presentation 2: A Physical, Flow Regimes-Based Decline Curve for Unconventional Reservoirs
Presenter: Vincent Artus, Kappa
Presentation 3: Applications of the Diffusive Time of Flight to a Data Driven Approach to Decline Curve Analysis
Presenter: Mike King, Texas A&M University
Topics covered in this session encompasses the advances in developing predictive, data-driven, and portable static/dynamic reservoir models. The focus is on taking advantage of cutting-edge highly-scalable machine learning algorithms to rapidly generate or contribute to the construction of high-quality geologically-consistent reservoir models/model ensembles for reservoir forecasting. Envisioned applications include both exploration/appraisal/early-production-stage reservoirs and brownfield/late-production-stage reservoirs.
Presentation 1: Getting the Best from Both Worlds: Combining Science with Data-Science
Presenter: Joe Lynch, Rock Flow Dynamics
Presentation 2: Machine Learning for Reservoir Characterisation Under Uncertainty: Explore, Describe, Update.
Presenter: Vasily Demyanov, Heriot-Watt University
Presentation 3: Uncertainty Centric Reservoir Management to Continuously Improve our Subsurface Understanding.
Presenter: Jon Saetrom, Resoptima
This panel discussion will address the promissing opportunities presented by combining the data driven models with physics-based models in reservoir management and field development optimization, and ideas of what the mid and long term future will hold.
Nathan Meehan, Gaffney, Cline & Associates / 2016 SPE President
Michael Davidson, Southwestern Energy
Eldad Haber, University of British Columbia
Graziella Caputo, IBM
Peter Wang, Anaconda