Lawrence D Boston 2006

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Information about Lawrence D Boston 2006

Published on April 14, 2008

Author: Breezy


World Energy Modeling:  World Energy Modeling Dick Lawrence ASPO-USA 2006 Photo: Jim Baldauf Overview - Outline:  Overview - Outline Why energy is fundamental The role of oil – industrial civilization and agriculture Peak Oil: a matter of time Big disagreements on numbers How much is down there Rate of extraction (production per day, or per year) Implications to humanity Industrialization Urbanization Globalization and reverse globalization (“localization”) Transportation Complacency and naïve optimism Why numbers matter World energy modeling project Energy Matters 1:  Energy Matters 1 “Energy is at the core of virtually every problem facing humanity. We cannot afford to get this wrong. We should be skeptical of optimism that the existing energy industry will be able to work this out on its own.” Testimony of witness Dr. Richard E. Smalley to the Senate Committee on Energy and Natural Resources – Full Committee Hearing on sustainable low-emission electricity generation, 27 April, 2004 Dr. Smalley is Director of the Carbon Nanotechnology Laboratory at Rice University Energy Matters 2:  Energy Matters 2 “… the momentous decisions we take in the next few years will determine whether our heirs thank us or curse us for the energy choices we bequeath to them.” Alex Kirby, BBC News Online environment correspondent, 19 April 2004 Everything Depends on Energy:  Everything Depends on Energy Whatever cause motivates you will be a lost cause if we do not have the energy to sustain society, industry, and agriculture in something like its present form … Raising standard of living in developing nations Reducing disease and illiteracy Eliminating hunger and famine in Africa, Asia, South America “Sustainable development” and economic growth Extending democratic institutions around the world Energy? Not a Problem … Right?:  Energy? Not a Problem … Right? Why is it so hard to communicate the Peak Oil message? The Boy Who Cried Wolf … “we’ve heard that before” “They’re always finding more oil!” (true, but …) Reserves/production = “40 years, at present rate of use” Acknowledging Peak Oil is only the first step toward understanding the problem: “OK, I buy your Peak Oil story, but market forces will ensure adequate energy for everyone – when prices go up, people will find alternatives!” (free-market theory) “OK, I buy your Peak Oil story, but we’ve got 40 years for science and technology to come through with something!” (they always did, before) Everyone Agrees “Peak Oil” is Just a Matter of Time:  Everyone Agrees “Peak Oil” is Just a Matter of Time But… huge uncertainty as to WHEN Optimistic or contradictory oil and gas projections lead to complacency and confusion No agreement on consequences of peak oil, whenever it comes Result: Little has been done since oil crisis of 1973 - Contribution of “renewables” still very small Nuclear is practically at a standstill Economic recovery leading to record consumption of oil, gas, and coal => what about global warming? Huge Disagreement!:  Huge Disagreement! 1920 1940 1960 1980 2000 2020 2040 OPEC USA ROW-Russia World 0 100 80 60 40 20 120 URR According to ASPO URR According to USGS, IEA, … URR = ~2 Trillion Bbl 1+ Trillion Remaining URR = 3 Trillion Bbl 2+ Trillion Remaining Energy Future According to ASPO Energy Future According to USGS How can something this important have so much uncertainty? (+/-100%!) URR = Ultimate Recoverable Resource 1) No Agreement on Crude Oil URR:  1) No Agreement on Crude Oil URR Unreliable, missing, or unbelievable reserve numbers Variable estimates for “reserve growth” applied at global level How much “Yet to Be Discovered” exists? Disagreement on impact of new technology Uncertainty re. relation of price to exploration, discovery, and extraction rates Wildly-varying estimates for future production rates and URR for tar sands, Venezuela heavy, shale oil 2) More Reasons for Confusion and Complacency:  2) More Reasons for Confusion and Complacency For every Fossil-Fuel alarmist, there is another expert saying “No problem” Opinions run the gamut: Utopian scenarios of fusion-enabled hydrogen-fueled industry, transport, and agriculture Magical reversion to 18th-century way of life Cataclysmic scenarios of war, starvation, death Without science or numbers behind them, all opinions are equally valid Oil Demand Exceeding Expectations:  Oil Demand Exceeding Expectations Population – world population projected to continue rising past 2050 or later (9-10 B) Industrial development – China’s oil imports rise 30+% in 2003, surpasses Japan; India and rest of SE Asia also exploding demand Middle-class aspirations in China, India Although losing industry, N American demand continues to rise – transport, electricity, gas Rest of world coming out of recession – EIA repeatedly raising estimates of demand Agriculture:  Agriculture Agriculture was once very labor-intensive in human and animal power; what will substitute for fossil fuels now that we number 6.4 billion (heading for 9 to 10 billion) ? US horse population peaked in 1915 at 25 million; 20% of all arable US land was used to feed horses. US humans numbered 100 million. In 2015, expect 300M people. We have never seen a plan for feeding 9 billion people without fossil fuel! The Big Picture – Oil Production over History:  The Big Picture – Oil Production over History M bbl/day ? What the World Needs Now:  What the World Needs Now Better data on reserves, production capability (Simmons, Bakhtiari, Campbell et al) – improve forecasts of future oil, gas production A good world energy model (us, here) – improve our ability to: Accompany ASPO’s Peak Oil message with energy modeling to show its implications Understand consequences of decisions made Analyze relative feasibility and net energy of future energy scenarios Complex Systems Modeling History:  Complex Systems Modeling History Big Studies Limits to Growth (Meadows et al 1972) Beyond Oil (Gever, Kaufmann, Skole, Vorosmarty 1986, 1991) Techniques, Papers, Books HT Odum, Cutler Cleveland, Charles Hall … Modeling Software and Systems Must handle hundreds of variables, complex feedback relationships Limits to Growth – IBM mainframe, MIT Beyond Oil – Fortran on a Prime supermini Now: Stella – dynamic systems modeling software, used by Richard Duncan and others Linking Multiple Models:  Linking Multiple Models There are many different “energy models”, most dealing with one aspect of energy – typically focusing on supply only, or demand only, or one particular type of energy Imagine a modeling structure that could tie several models together: output of one model (for example) can be input to another; No model known tackles the problem in a holistic, worldwide and integrated way; we want to understand the implications of an integrated supply and demand model, for all types of energy What’s an Energy Model Good For?:  Raise awareness of fossil-fuel depletion and its consequences for global policy makers, decision makers Analyze and quantify the consequences of fossil-fuel depletion on agriculture, industry, commerce and homes Show the impact of different rates of adoption for various mixes of renewable/fossil/nuclear sources Make world energy information program-accessible, in one database, in standardized format With standardized and rigorous ERoEI methodology, show what mix of conventional and renewable energies has the best outcome – point the way, avoid dead ends What’s an Energy Model Good For? What’s an Energy Model Good For? (detail):  What’s an Energy Model Good For? (detail) Impact of ERoEI on URR (example) Impact of ERoEI on NET ENERGY (example) Quantify impact of delaying investment in alternatives (example) Critical analysis of alternative energy sources The Hydrogen Economy Biofuels like ethanol Tar sands and other unconventional oil Shale oil Nuclear power Impact of ERoEI on URR:  Impact of ERoEI on URR Time Energy Invested to get New Energy Energy Return on Energy Investment Easy Hard Understanding Net Energy:  Understanding Net Energy All energy sources require up-front investment in energy, as well as $ capital and human effort, to yield a return on that investment NET ENERGY: Energy Returned on Energy Invested = ERoEI Some investments are better than others Some investments are energy losers! How can we know which ones to invest in? The Energy Cost of Energy:  The Energy Cost of Energy Annual contribution from alternate energy source 100 75 Cumulative new energy from investment - NET ENERGY Analysis The Energy Cost of Energy:  The Energy Cost of Energy Cumulative new energy from investment 100 75 60 - NET ENERGY Analysis Consequence of Delayed Investment:  Consequence of Delayed Investment From Beyond Oil, fig. 7-1 Comparative Analysis of Future Energy Resources:  Comparative Analysis of Future Energy Resources Any realistic future contains a mix of energy sources; what’s the best mix? Which are the energy losers and dead ends? Where should we invest our energy and $ capital for the best return (net energy), best long-term sustainable future, and best outcome for the environment? Good data and a high-quality model will point the way Model Structure and Organization:  Model Structure and Organization Database of best-known world energy information World energy information is program-accessible Use of spreadsheets as database inputs, outputs, and program-to-program intermediary Can link multiple programs for energy supply, energy demand, mixes of renewable energy Peripheral programs may input data on oil & gas production, demand, population, … Model output shown as graphs, tables – future total energy and per-capita net energy by type, by region, by year, as function of scenario Model Structure (example):  Model Structure (example) Model Core Dynamic System Simulation (Stella or similar software) UN Population Model Spreadsheet- database Future Scenario Spreadsheet- database Oil & Gas Supply Model Spreadsheet- database Energy Demand Model ASPO Oil-Gas Model, OPEC Model, Wocap Input data from IEA, BP,USGS, O&G Journal Model Attributes:  Model Attributes Transparency Model inputs are databases / spreadsheets Database inputs & outputs directly human-viewable as spreadsheets, or easily converted to spreadsheets Inner workings of model and underlying assumptions are visible and easy to understand Accessibility Any reasonably-equipped PC can run the models and view results Modeling software is reasonably priced, or shareware, or free Model Attributes (detail):  Model Attributes (detail) Linkable One model can feed into another via standardized database (e.g. spreadsheet) Standardized Database Format “Oil Export from Nigeria in 1997” goes to predetermined (Row 97) location in “Africa Production” spreadsheet Every program has SW modules that tell it where to get input data, and where to put output data Database Structure Has Extensible Detail Level Data in any cell of a spreadsheet can be sourced from another page of the spreadsheet, or another spreadsheet – down to oil-field level of detail, if wanted Example of Per-Country Energy Spreadsheet:  Example of Per-Country Energy Spreadsheet Country X By-Country Database Development:  By-Country Database Development View adjacent countries as 3D space with energy flows across the boundaries; Track all energy flows – all types of fossil-fuel imports, exports; electricity; include storage, gas-to-liquid, refinery gain; Show consumption of all types (net energy of typeX in = consumption + storage); checking mechanism: S(all imports) = S(all exports) for each energy type, elec generation & consumption Tracking Energy Flow Between Countries:  Tracking Energy Flow Between Countries “Country” is natural boundary for a spreadsheet – IEA data, BP data, etc. all on per-country basis checking mechanism: S(all imports) = S(all exports) for each energy type, elec gen’n & cons’n Building the World Energy Model:  Building the World Energy Model What’s Needed Work together and agree on goals, scope, and process Identify several strong and committed co-sponsoring organizations Develop preliminary project and funding plan Core team of 4-6 experienced staff for 2-3 years, with solid academic connections & resources Need a strong visionary leader and experienced modeling advisors Identify funding sources to meet staffing requirements Develop model; use worldwide Internet, academic connections for data-entry job developing machine-readable database / spreadsheets; Agree on initial scenarios, start model runs, document results Long-Term Prospect:  Long-Term Prospect Funded team runs scenarios on model, documents results – but, the need does not end there! World’s need to keep model updated, improve it, run new scenarios will continue beyond this decade The People’s Energy Model Outreach program: Train others to develop and run scenarios Continue to refine detail and accuracy in model’s database Set up energy equivalent to Open Software Foundation for long-term continuing development, maintenance, and future scenario running Could be some great PhD dissertations in this! Thanks for Your Interest! Additional resources:  Thanks for Your Interest! Additional resources: Email me:

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