We explored cationic, nonionic and zwitterionic surfactants to identify candidates that have the potential to satisfy all the key requirements for CO2 foams in EOR. We have examined the formation, texture, rheology and stability of CO2 foams as a function of the surfactant structure and formulation variables including temperature, pressure, water/CO2 ratio, surfactant concentration, salinity and concentration of oil. Furthermore, the partitioning of surfactants between oil and water as well as CO2 and water was examined in conjunction with adsorption measurements on limestone by the Hirasaki lab to develop strategies to optimize the transport of surfactants in reservoirs
The US Department of Transportation (US-DOT) estimates that over half of all congestion events are caused by highway incidents rather than by rush-hour traffic in big cities. Real-time incident detection on freeways is an important part of any modern traffic control center operation because it offers an opportunity to maximize road system performance. An effective incident detection and management operation cannot prevent incidents, however, it can diminish the impacts of non-recurring congestion problems. The main purpose of real-time incident detection is to reduce delay and the number of secondary accidents, and to improve safety and travel information during unusual traffic conditions. The purpose of this project is to evaluate two recently developed automatic incident detection algorithms. The majority of automatic incident detection algorithms are focused on identifying traffic incident patterns but may not adequately investigate possible similarities in patterns observed under incident-free conditions. When traffic demand exceeds road capacity, the traffic speed decreases significantly and the traffic enters a highly unstable regime often referred to as “stop-and-go” conditions. The most challenging part of real-time incident detection is recognition of traffic pattern changes when incidents happen during stop-and-go conditions. This work describes a case study evaluation of two recently evolved incident detection methods using data from the Dallas, TX traffic control center.
This study presents an evaluation of the potential impacts on TxDOT revenues of substituting liquefied natural gas (LNG), compressed natural gas (CNG), and liquefied petroleum gas (LPG) for traditional diesel or gasoline vehicle fuels in Texas. Time series analyses are conducted for LNG, CNG, and LPG to estimate a model to forecast diesel and gasoline consumption for years 2012 to 2025. Taking into account the federal and state fuel taxes, the revenue generated from traditional fuel consumption is compared to three alternative fuel substitution scenarios. Overall, if the Federal and State excise tax rates remain at current levels the analysis suggests that substitution of LNG and LPG for traditional fuels will generate more revenue for the forecast years. However, substitution of CNG for gasoline consumption will reduce revenue if the Federal and State excise tax rates remain the same for the forecast years.
Because of the serious societal, environmental, economic, and public health problems associated with motorized transportation, there is increased interest in encouraging non-motorized modes of travel. The current study contributes toward this objective in two ways. First, it evaluates the operational impacts of bicycling adjacent to on-street parking. Second, it identifies the importance of attributes influencing bicyclists’ route choice preferences. The importance of evaluating both operations and individual preferences at the same time is the interrelationship between the two; poorly designed roadways may encourage cyclists to leave designated bicycle routes. Operationally, this study examines field data that was collected in Austin, Houston, and San Antonio and resulted in over 6,400 observations of motorists and/or cyclists adjacent to on-street parking. From the data, multivariate regression models were developed to predict the motorist’s and cyclist’s position on the roadway and the probability of motor vehicle encroachment. The models indicate that on-street parking has a significant impact on motorist and cyclist position; a bike lane combined with a buffer space is the only way to completely remove cyclists from the door zone, and operationally, a bicycle lane is more effective than a wide outside lane. As a result of the study, the Texas Guide for Planned and Retrofit Bike Facilities was updated to include on-street parking. In evaluating route choice, the study specifically examines a comprehensive set of attributes that influence bicycle route choice, including: (1) bicyclists’ characteristics, (2) on-street parking, (3) bicycle facility type and amenities, (4) roadway physical characteristics, (5) roadway functional characteristics, and (6) roadway operational characteristics. The data used in the analysis is drawn from a web-based stated preference survey of Texas bicyclists. The results of the study emphasize the importance of a comprehensive evaluation of both route-related attributes and bicyclists’ demographics in bicycle route choice decisions. The empirical models indicate that travel time is the most important attribute for commuters in choosing their routes. These factors also impact bicyclists’ route choice: traffic volume; speed limit; on-street parking characteristics; bicycle route continuity; number of stop signs, red lights, and cross streets; and roadway terrain.