Phase-based design of CO2 capture, transport, and storage infrastructure via SimCCS3.0.

The design of optimal infrastructure is essential for the deployment of commercial and large-scale carbon capture and storage (CCS) technology. During the design process, it is important to consider CO2 capture and storage locations and CO2 transportation pipelines to minimize the total project cost. SimCCS, first introduced in 2009, is an integrated open-source tool to optimize CCS infrastructure. The core CCS infrastructure design problem in SimCCS is structured as a mixed-integer linear programming problem by selecting the optimal pipeline routes, searching CO2 source capture and storage locations, and determining the corresponding CO2 amounts to meet desired capture targets. Multiple important and practical features have been developed to the latest version of SimCCS, SimCCS3.0. One of these features is phase-based modeling which enables users to dynamically design the CCS infrastructure. We demonstrate the phased-based modeling capability using two CCS infrastructure optimization case studies. The results from these case studies reveal that the phase-based modeling capability in SimCCS is particularly useful to optimize the dynamic deployment of CCS projects.

Improving deep learning performance for predicting large-scale geological [Formula: see text] sequestration modeling through feature coarsening.

Physics-based reservoir simulation for fluid flow in porous media is a numerical simulation method to predict the temporal-spatial patterns of state variables (e.g. pressure p) in porous media, and usually requires prohibitively high computational expense due to its non-linearity and the large number of degrees of freedom (DoF). This work describes a deep learning (DL) workflow to predict the pressure evolution as fluid flows in large-scale 3-dimensional(3D) heterogeneous porous media. In particular, we develop an efficient feature coarsening technique to extract the most representative information and perform the training and prediction of DL at the coarse scale, and further recover the resolution at the fine scale by spatial interpolation. We validate the DL approach to predict pressure field against physics-based simulation data for a field-scale 3D geologic [Formula: see text] sequestration reservoir model. We evaluate the impact of feature coarsening on DL performance, and observe that the feature coarsening not only decreases the training time by [Formula: see text] and reduces the memory consumption by [Formula: see text], but also maintains temporal error [Formula: see text] on average. Besides, the DL workflow provides predictive efficiency with 1406 times speedup compared to physics-based numerical simulation. The key findings from this research significantly improve the training and prediction efficiency of deep learning model to deal with large-scale heterogeneous reservoir models, and thus it can also be further applied to accelerate workflows of history matching and reservoir optimization for close-loop reservoir management.

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.

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.

A system model for geologic sequestration of carbon dioxide.

In this paperwe describe CO2-PENS, a comprehensive system-level computational model for performance assessment of geologic sequestration of CO2. CO2-PENS is designed to perform probabilistic simulations of CO2 capture, transport, and injection in different geologic reservoirs. Additionally, the long-term fate of CO2 injected in geologic formations, including possible migration out of the target reservoir, is simulated. The simulations sample from probability distributions for each uncertain parameter, leading to estimates of global uncertainty that accumulate through coupling of processes as the simulation time advances. Each underlying process in the system-level model is built as a module that can be modified as the simulation tool evolves toward more complex problems. This approach is essential in coupling processes that are governed by different sets of equations operating at different time-scales. We first explain the basic formulation of the system level model, briefly discuss the suite of process-level modules that are linked to the system level, and finally give an in-depth example that describes the system level coupling between an injection module and an economic module. The example shows how physics-based calculations of the number of wells required to inject a given amount of CO2 and estimates of plume size can impact long-term sequestration costs.

Development of a hybrid process and system model for the assessment of wellbore leakage at a geologic CO2 sequestration site.

Sequestration of CO2 in geologic reservoirs is one of the promising technologies currently being explored to mitigate anthropogenic CO2 emissions. Large-scale deployment of geologic sequestration will require seals with a cumulative area amounting to hundreds of square kilometers per year and will require a large number of sequestration sites. We are developing a system-level model, CO2-PENS, that will predict the overall performance of sequestration systems while taking into account various processes associated with different parts of a sequestration operation, from the power plant to sequestration reservoirs to the accessible environment. The adaptability of CO2-PENS promotes application to a wide variety of sites, and its level of complexity can be increased as detailed site information becomes available. The model CO2-PENS utilizes a science-based-prediction approach by integrating information from process-level laboratory experiments, field experiments/observations, and process-level numerical modeling. The use of coupled process models in the system model of CO2-PENS provides insights into the emergent behavior of aggregate processes that could not be obtained by using individual process models. We illustrate the utility of the concept by incorporating geologic and wellbore data into a synthetic, depleted oil reservoir. In this sequestration scenario, we assess the fate of CO2 via wellbore release and resulting impacts of CO2 to a shallow aquifer and release to the atmosphere.