Richard S. Middleton

Greening Coal: Breakthroughs and Challenges in Carbon Capture and Storage

Like it or not, coal is here to stay, for the next few decades at least. Continued use of coal in this age of growing greenhouse gas controls will require removing carbon dioxide from the coal waste stream. We already remove toxicants such as sulfur dioxide and mercury, and the removal of CO₂ is the next step in reducing the environmental impacts of using coal as an energy source (i.e., greening coal). This paper outlines some of the complexities encountered in capturing CO₂ from coal, transporting it large distances through pipelines, and storing it safely underground.

CO2 Accounting and Risk Analysis for CO2 Sequestration at Enhanced Oil Recovery Sites.

Using CO2 in enhanced oil recovery (CO2-EOR) is a promising technology for emissions management because CO2-EOR can dramatically reduce sequestration costs in the absence of emissions policies that include incentives for carbon capture and storage. This study develops a multiscale statistical framework to perform CO2 accounting and risk analysis in an EOR environment at the Farnsworth Unit (FWU), Texas. A set of geostatistical-based Monte Carlo simulations of CO2-oil/gas-water flow and transport in the Morrow formation are conducted for global sensitivity and statistical analysis of the major risk metrics: CO2/water injection/production rates, cumulative net CO2 storage, cumulative oil/gas productions, and CO2 breakthrough time. The median and confidence intervals are estimated for quantifying uncertainty ranges of the risk metrics. A response-surface-based economic model has been derived to calculate the CO2-EOR profitability for the FWU site with a current oil price, which suggests that approximately 31% of the 1000 realizations can be profitable. If government carbon-tax credits are available, or the oil price goes up or CO2 capture and operating expenses reduce, more realizations would be profitable. The results from this study provide valuable insights for understanding CO2 storage potential and the corresponding environmental and economic risks of commercial-scale CO2-sequestration in depleted reservoirs.

The cross-scale science of CO2 capture and storage: from pore scale to regional scale

We describe state-of-the-art science and technology related to modeling of CO2 capture and storage (CCS) at four different process scales: pore, reservoir, site, and region scale. We present novel research at each scale to demonstrate why each scale is important for a comprehensive understanding of CCS. Further, we illustrate research linking adjacent process scales, such that critical information is passed from one process scale to the next adjacent scale. We demonstrate this cross-scale approach using real world CO2 capture and storage data, including a scenario managing CO2 emissions from a large U.S. electric utility. At the pore scale, we present a new method for incorporating pore-scale surface tension effects into a relative permeability model of CO2-brine multiphase flow at the reservoir scale. We benchmark a reduced complexity model for site-scale analysis against a rigorous physics-based reservoir simulator, and include new system level considerations including local site-scale pipeline routing analysis (i.e., reservoir to site scale). We also include costs associated with brine production and treatment at the site scale, a significant issue often overlooked in CCS studies. All models that comprise our total system include parameter uncertainty which leads to results that have ranges of probability. Results suggest that research at one scale is able to inform models at adjacent process scales, and that these scale connections can inform policy makers and utility managers of overall system behavior including the impacts of uncertainty.

Pre-site characterization risk analysis for commercial-scale carbon sequestration.

This study develops a probability framework to evaluate subsurface risks associated with commercial-scale carbon sequestration in the Kevin Dome, Montana. Limited knowledge of the spatial distribution of physical attributes of the storage reservoir and the confining rocks in the area requires using regional data to estimate project risks during the pre-site characterization analysis. A set of integrated Monte Carlo simulations are used to assess four risk proxies: the CO2 injectivity, area of review (AoR), migration rate into confining rocks, and a monitoring strategy prior to detailed site characterization. Results show a reasonable likelihood of reaching the project goal of injecting 1 Mt in 4 years with a single injection well (>58%), increasing to >70% if the project is allowed to run for 5 years. The mean radius of the AoR, based on a 0.1 MPa pressure change, is around 4.8 km. No leakage of CO2 through the confining units is seen in any simulations. The computed CO2 detection probability suggests that the monitoring wells should be located at less than 1.2 km away from the injection well so that CO2 is likely to be detected within the time frame of the project. The scientific results of this study will be used to inform the detailed site characterization process and to provide more insight for understanding operational and technical risks before injecting CO2.

Generating candidate networks for optimization: The CO2 capture and storage optimization problem

We develop a new framework for spatially optimizing infrastructure for CO2 capture and storage (CCS). CCS is a complex and challenging problem: domestically deploying CCS at a meaningful scale will require linking hundreds of coal-fired power plants with CO2 sequestration reservoirs through a dedicated and extensive (many tens-of-thousands of miles) CO2 pipeline network. We introduce a unique method for generating a candidate network from scratch, from which the optimization model selects the optimal set of arcs to form the pipeline network. This new generation method can be applied to any network optimization problem including transmission line, roads, and telecommunication applications. We demonstrate the model and candidate network methodology using a real example of capturing CO2 from coal-fired power plants in the US Midwest and storing the CO2 in depleted oil and gas fields. Results illustrate the critical need to balance CCS investments with generating a candidate network of arcs.

Mesoscale Carbon Sequestration Site Screening and CCS Infrastructure Analysis

We explore carbon capture and sequestration (CCS) at the meso-scale, a level of study between regional carbon accounting and highly detailed reservoir models for individual sites. We develop an approach to CO(2) sequestration site screening for industries or energy development policies that involves identification of appropriate sequestration basin, analysis of geologic formations, definition of surface sites, design of infrastructure, and analysis of CO(2) transport and storage costs. Our case study involves carbon management for potential oil shale development in the Piceance-Uinta Basin, CO and UT. This study uses new capabilities of the CO(2)-PENS model for site screening, including reservoir capacity, injectivity, and cost calculations for simple reservoirs at multiple sites. We couple this with a model of optimized source-sink-network infrastructure (SimCCS) to design pipeline networks and minimize CCS cost for a given industry or region. The CLEAR(uff) dynamical assessment model calculates the CO(2) source term for various oil production levels. Nine sites in a 13,300 km(2) area have the capacity to store 6.5 GtCO(2), corresponding to shale-oil production of 1.3 Mbbl/day for 50 years (about 1/4 of U.S. crude oil production). Our results highlight the complex, nonlinear relationship between the spatial deployment of CCS infrastructure and the oil-shale production rate.

Optimal Spatial Deployment of CO2 Capture and Storage Given a Price on Carbon

Carbon dioxide capture and storage (CCS) links together technologies that separate carbon dioxide (CO2) from fixed point source emissions and transport it by pipeline to geologic reservoirs into which it is injected underground for long-term containment. Previously, models have been developed to minimize the cost of a CCS infrastructure network that captures a given amount of CO2. The CCS process can be costly, however, and large-scale implementation by industry will require government regulations and economic incentives. The incentives can price CO2 emissions through a tax or a cap-and-trade system. This paper extends the earlier mixed-integer linear programming model to endogenously determine the optimal quantity of CO2 to capture and optimize the various components of a CCS infrastructure network, given the price per tonne to emit CO2 into the atmosphere. The spatial decision support system first generates a candidate pipeline network and then minimizes the total cost of capturing, transporting, storing, or emitting CO2. To illustrate how the new model based on CO2 prices works, it is applied to a case study of CO2 sources, reservoirs, and candidate pipeline links and diameters in California.

Using infrastructure optimization to reduce greenhouse gas emissions from oil sands extraction and processing.

The Alberta oil sands are a significant source of oil production and greenhouse gas emissions, and their importance will grow as the region is poised for decades of growth. We present an integrated framework that simultaneously considers economic and engineering decisions for the capture, transport, and storage of oil sands CO(2) emissions. The model optimizes CO(2) management infrastructure at a variety of carbon prices for the oil sands industry. Our study reveals several key findings. We find that the oil sands industry lends itself well to development of CO(2) trunk lines due to geographic coincidence of sources and sinks. This reduces the relative importance of transport costs compared to nonintegrated transport systems. Also, the amount of managed oil sands CO(2) emissions, and therefore the CCS infrastructure, is very sensitive to the carbon price; significant capture and storage occurs only above 110

Predicting Chronic Climate-Driven Disturbances and Their Mitigation

/tonne CO(2) in our simulations. Deployment of infrastructure is also sensitive to CO(2) capture decisions and technology, particularly the fraction of capturable CO(2) from oil sands upgrading and steam generation facilities. The framework will help stakeholders and policy makers understand how CCS infrastructure, including an extensive pipeline system, can be safely and cost-effectively deployed.

Global Sensitivity of Simulated Water Balance Indicators Under Future Climate Change in the Colorado Basin

Society increasingly demands the stable provision of ecosystem resources to support our population. Resource risks from climate-driven disturbances, including drought, heat, insect outbreaks, and wildfire, are growing as a chronic state of disequilibrium results from increasing temperatures and a greater frequency of extreme events. This confluence of increased demand and risk may soon reach critical thresholds. We explain here why extreme chronic disequilibrium of ecosystem function is likely to increase dramatically across the globe, creating no-analog conditions that challenge adaptation. We also present novel mechanistic theory that combines models for disturbance mortality and metabolic scaling to link size-dependent plant mortality to changes in ecosystem stocks and fluxes. Efforts must anticipate and model chronic ecosystem disequilibrium to properly prepare for resilience planning.