Informs Annual Meeting Phoenix 2018

INFORMS Phoenix – 2018

SA49

n SA48 North Bldg 229B Integrated Multi-scale Multi-sector Modeling of Energy Systems Sponsored: Energy, Natural Res & the Environment/Energy Sponsored Session Chair: Gokul C. Iyer, Pacific Northwest National Laboratory, 5825 University Research Court, College Park, MD, 20740, United States 1 - Introduction to Integrated Multi-model Frameworks to Improve Decision-making Capabilities Across Spatial and Temporal Scales in the Energy Systems Gokul Iyer, Pacific Northwest National Laboratory, 5825 University Research Court, Suite 3500, College Park, MD, 20740, United States This talk will give an overview of modeling efforts to integrate models with different degrees of temporal, spatial, and sectoral detail to provide improved decision-making capabilities about energy systems and infrastructure planning within the context of the broader energy-water-land-climate nexus. 2 - Structural Uncertainty Across Disparate Energy-economy Modeling Frameworks: Reeds and GCAM-USA Consistency Under Alternative Earth Futures Stuart Cohen, National Renewable Energy Laboratory, 15013 Denver West Parkway, RSF 300, Golden, CO, 80401, United States, Gokul Iyer, Maxwell Brown Robust decision support assimilates information from multiple tools across the relevant scale and scope. However, these tools often differ in structure and function, yielding inconsistent results that confound the decision process. Previous work sought a robust electric sector expansion model framework by harmonizing GCAM-USA, a multi-sector human-Earth system model, with the ReEDS electricity sector model. I’ll describe progress achieving consistent electricity and energy-economy solutions under many socioeconomic, technology, and market futures. I’ll examine structural uncertainties affecting solution consistency and consider the broader implications for decision support. 3 - The Future of Natural Gas Infrastructure Development in the United States Felipe A. Feijoo, Pontificia Universidad Católica de Valparaiso, Av. Brasil 2241, Office 6-8, Valparaiso, Chile, Gokul Iyer, Charalampos Avraam, Sauleh Ahmad Siddiqui, Leon Clarke, Matthew T. Binsted, Sriram Sankaranarayanan, Sriram Sankaranarayanan, Pralit Patel, Marshall Wise, Nathalia Prates, Evelyn Torres-Alfaro This study couple a global human-Earth system model with state-level detail in the U.S. (GCAM-USA) with a natural gas infrastructure investment model (NANGAM) to examine inter-state natural gas pipeline infrastructure development in the U.S. We show that existing pipeline infrastructure is insufficient to satisfy the increasing demand. The geographic distribution of investments within the U.S. is heterogeneous and depends on the capacity of existing infrastructure as well as the magnitude of increase in demand. Our results also illustrate the risks of under-utilization of pipeline capacity 4 - Interconnection Cost Implications of Future Power Generation Capacity Expansion in the United States Chris Vernon, Pacific Northwest National Laboratory, WA, United States, Gokel Iyer, Ian P. Kraucunas, Nathalie Voisin, Mohamad Hejazi, Matthew T. Binsted, Matthew O’Connell, Pratik L. Patel Long-term electric power sector planning is a complex process that is rooted in system dynamics to assess the timing and magnitude of investments in infrastructure within the context of potential vulnerabilities. Assessing these dynamics requires a multi-sector, multi-scale modeling approach that captures socioeconomics, electricity supply and demand, and the impediments and distribution of the power generation sources. This research evaluates the impact of achieving future capacity expansion plans to the resulting technology-specific interconnection costs by pairing an integrated human-Earth system model (GCAM-USA) with the Capacity Expansion Regional Feasibility model (CERF).

n SA49 North Bldg 230

Joint Session ENRE/Practice Curated:Advanced Analytics in Oil & Gas Production and Exploration Sponsored: Energy, Natural Res & the Environment/Natural Resources Petrochemicals Sponsored Session Chair: Damian Burch, ExxonMobil Upstream Research Company, Houston, TX, United States 1 - Bayesian Modeling and Decision Making for a Well System Rujian Chen, Massachusetts Institute of Technology We are interested in modeling a large off-shore oil well system with complex multi-phase fluid flows. Previous optimization-based learning methods failed to capture the high uncertainty in the system arising from noisy and missing measurements. In this work, we adopt a Bayesian framework to infer system parameters as well as characterize their uncertainty. We use a Gaussian process to model the flow simulation and develop an approximate model with a tractable inference method. We use synthetic to data show the fidelity of the approximate model and use Bayesian model validation techniques to show the predictive accuracy of the model. Finally, we develop new experiment design approach which brings time and cost savings compared to previously used empirical methods. 2 - The Effect of Technology on Importance of Geologic Parameters for Shale Well Productivity: Cross-Play Analysis Svetlana Ikonnikova, University of Texas at Austin, Austin, TX, United States, Katie Smye, Scott Hamlin, Robin Dommisse, Frank Male This study of Haynesville, Fayetteville, and Marcellus shale plays explores the role of geologic parameters versus completion technology? Applied machine learning methods (random forest and model-based recursive partitioning) reveal the set of variables, which explain individual well productivity. The analysis focuses on exceptionally good wells in geologically mediocre, e.g. ductile, areas to understand whether “poor rock quality can be compensated by completions and be economical. We find that good “outliers exist, with productivity being statistically explained by technology-related changes, such as optimized completions (and field experience) making “poor zones economic. 3 - Transdimensional Full Waveform Inversion Using a Hamiltonian Formulation Mrinal K. Sen, University of Texas at Austin Abstract not available. 4 - Data-driven Methods for Well Connectivity Damian Burch, PhD, ExxonMobil Upstream Research Company, 22 S. Peaceful Canyon Circle, The Woodlands, TX, 77381, United States, Akash Mittal, Tripti Kumari Making the best development and production decisions in the oil & gas industry requires a detailed understanding of subsurface flow paths. Most critically, we need to understand if and how pressure from injection wells might affect nearby producer wells. Unfortunately, this information usually cannot be directly imaged, so indirect methods are required to infer subsurface flow paths from sparse surface measurements. In this talk, we will discuss methods that combine simple physical models with data-analytic algorithms for detecting and quantifying well connectivity. 5 - Real-time Solution of a Pursuit-Evasion Game for Ice Management Matthew W. Harris, ExxonMobil Upstream Research, Magnolia, TX, United States Ice management systems provide a rational basis for risk-based decisions, and they involve estimating the probability of an ice impact and associated time. A differential pursuit-evasion game perspective provides conservative estimates for miss distance and time. Such problems are generally difficult to solve since the direct methods of optimal control do not apply. However, the state space can be partitioned to identify closed-form solutions or a reduced set of algebraic equations. It so happens that the degenerate solution types admit closed-form solutions while regular solution types do not. Examples of each are shown.

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