Method to determine the locations of tsunami vertical evacuation shelters

The 2004 Indian Ocean tsunami and the 2011 Great Tohoku Japan earthquake and tsunami focused a great deal of the world??s attention on the effect of tsunamis on buildings and infrastructure. When a tsunami impacts structures in a coastal community, the structures are often not strong enough to withstand the forces and may collapse. Therefore, to maximize the survival probability, people evacuate to higher ground or move outside the inundation zone. However, this is not always possible because of short warning times for near-field tsunamis. Thus, sheltering-in-place or ??sheltering-near-place?? using vertical evacuation should be considered as an alternative approach to lateral evacuation from a tsunami inundation zone. This paper presents the method and results of a study to develop and demonstrate a methodology that applied genetic optimization to determine optimal tsunami shelter locations with the goal of reducing evacuation time, thereby maximizing the probability of survival for the population in a coastal community. The City of Cannon Beach, Oregon, USA, was used as an illustrative example. Several cases were investigated ranging from a single shelter to multiple shelters with locations of high elevation already in place near the city. The method can provide decision-support for the determination of locations for tsunami vertical evacuation shelters. The optimum location of the shelter(s), which was found to vary depending on the number of shelters considered, can reduce the evacuation time significantly, thereby reducing the number of fatalities and increasing the safety of a community.     

Water–Rock Interactions: An Investigation of the Relationships Between Mineralogy and Groundwater Composition and Flow in a Subtropical Basalt Aquifer

A holistic study of the composition of the basalt groundwaters of the Atherton Tablelands region in Queensland, Australia was undertaken to elucidate possible mechanisms for the evolution of these very low salinity, silica- and bicarbonate-rich groundwaters. It is proposed that aluminosilicate mineral weathering is the major contributing process to the overall composition of the basalt groundwaters. The groundwaters approach equilibrium with respect to the primary minerals with increasing pH and are mostly in equilibrium with the major secondary minerals (kaolinite and smectite), and other secondary phases such as goethite, hematite, and gibbsite, which are common accessory minerals in the Atherton basalts. The mineralogy of the basalt rocks, which has been examined using X-ray diffraction and whole rock geochemistry methods, supports the proposed model for the hydrogeochemical evolution of these groundwaters: precipitation + CO₂ (atmospheric + soil) + pyroxene + feldspars + olivine yields H₄SiO₄, HCO₃ ⁻, Mg²⁺, Na⁺, Ca²⁺ + kaolinite and smectite clays + amorphous or crystalline silica + accessory minerals (hematite, goethite, gibbsite, carbonates, zeolites, and pyrite). The variations in the mineralogical content of these basalts also provide insights into the controls on groundwater storage and movement in this aquifer system. The fresh and weathered vesicular basalts are considered to be important in terms of zones of groundwater occurrence, while the fractures in the massive basalt are important pathways for groundwater movement.     

Economic justification of tsunami research: A specific example based on reduction of false alarms in Hawaii

The most significant potential improvement to the Tsunami Warning System, at least as it affects Hawaii, and one of the more important practical justifications of tsunami research, is the reduction in false alarms. There are both immediate and deferred costs of tsunami false alarms. The immediate costs are the costs of responding to tsunami warnings, false or not. The deferred costs are the tsunami casualties resulting from failures to respond to subsequent warnings, insofar as these are attributable to the loss of public confidence in the warning system due to the false alarms. It is estimated that the Hawaiian response to a tsunami false alarm costs about $777,000, and that with present warning system policy the average annual costs of such responses is $264,000. Assigning values to human life and injury, it is estimated that the deferred costs of false alarms average about $42,000 per year, bringing the total annual costs to $306,000. An 80% reduction of these costs would justify an annual research expenditure of $307,000 per year for the next ten years.     

Acceleration of thermodynamic computations in fluid flow applications

Reservoir simulators model the highly nonlinear partial differential equations that represent flows in heterogeneous porous media. The system is made up of conservation equations for each thermodynamic species, flash equilibrium equations and some constraints. With advances in Field Development Planning (FDP) strategies, clients need to model highly complex Improved Oil Recovery processes such as gas re-injection and CO2 injection, which requires multi-component simulation models. The operating range of these simulation models is usually around the mixture critical point and this can be very difficult to simulate due to phase mislabeling and poor nonlinear convergence. We present a Machine Learning (ML) based approach that significantly accelerates such simulation models. One of the most important physical parameters required in order to simulate complex fluids in the subsurface is the critical temperature (T_crit). There are advanced iterative methods to compute the critical point such as the algorithm proposed by Heidemann and Khalil (AIChE J 26,769–799, 1980) but, because these methods are too expensive, they are usually replaced by cheaper and less accurate methods such as the Li-correlation (Reid and Sherwood 1966). In this work we use a ML workflow that is based on two interacting fully connected neural networks, one a classifier and the other a regressor, that are used to replace physical algorithms for single phase labelling and improve the convergence of the simulator. We generate real time compositional training data using a linear mixing rule between the injected and the in-situ fluid compositions that can exhibit temporal evolution. In many complicated scenarios, a physical critical temperature does not exist and the iterative sequence fails to converge. We train the classifier to identify, a-priori, if a sequence of iterations will diverge. The regressor is then trained to predict an accurate value of T_crit. A framework is developed inside the simulator based on TensorFlow that aids real time machine learning applications. The training data is generated within the simulator at the beginning of the simulation run and the ML models are trained on this data while the simulator is running. All the run-times presented in this paper include the time taken to generate the training data and train the models. Applying this ML workflow to real field gas re-injection cases suffering from severe convergence issues has resulted in a 10-fold reduction of the nonlinear iterations in the examples shown in this paper, with the overall run time reduced 2- to 10-fold, thus making complex FDP workflows several times faster. Such models are usually run many times in history matching and optimization workflows, which results in compounded computational savings. The workflow also results in more accurate prediction of the oil in place due to better single phase labelling.