Abstract not Available.
Abstract One of the most important problems in reservoir management and EOR applications is to describe interwell heterogeneity for optimum well spacing and process design. The conventional approaches to build reservoir descriptions use interpolation schemes based on the geometric distance of the samples. In geostatistics, on the other hand, the interpolated values depend on geometric orientation and correlation. Geostatistics has been used in the mining industry for the past forty years and in hydrogeology for the past twenty years. However, its application has been limited in reservoir engineering because of skepticism due to a lack of needed data and the absence of comparisons with the conventional techniques. Conventional and geostatistical reservoir characterization techniques are compared in this paper. Field data from about 50 wells are used in this evaluation. Interwell properties are computed using conventional and geostatistical techniques, cross-validated, and compared. Histograms show there are no geographically distinct populations requiring separate modeling. Experimental variograms identify the scales of variability of the reservoir properties and the existence of preferential directions of maximum and minimum variability. Variogram models are fitted to the experimental correlations. The results of this work indicate that the success of any technique is a function of the geological environment, selection of proper reservoir parameters for that environment, an adequate number of samples, an appropriate sample spacing, and most importantly, the correct application of the technique. Geostatistical techniques have more potential to incorporate the underlying correlation present in the data in the final analysis.
Considering trajectory curves, integral of n-dimensional dynamical systems, within the framework of Differential Geometry as curves in Euclidean n-space, it will be established in this article that the curvature of the flow, i.e. the curvature of the trajectory curves of any n-dimensional dynamical system directly provides its slow manifold analytical equation the invariance of which will be then proved according to Darboux theory. Thus, it will be stated that the flow curvature method, which uses neither eigenvectors nor asymptotic expansions but only involves time derivatives of the velocity vector field, constitutes a general method simplifying and improving the slow invariant manifold analytical equation determination of high-dimensional dynamical systems. Moreover, it will be shown that this method generalizes the Tangent Linear System Approximation and encompasses the so-called Geometric Singular Perturbation Theory. Then, slow invariant manifolds analytical equation of paradigmatic Chua's piecewise linear and cubic models of dimensions three, four and five will be provided as tutorial examples exemplifying this method as well as those of high-dimensional dynamical systems.
Exopolyphosphatase of Escherichia coli (PPX) is a highly processive enzyme demonstrating the ability to recognize polyphosphates of specific lengths. The mechanisms responsible for the processivity and polymer length recognition of the enzyme were investigated in relation to the manner in which polyphosphate is bound to the enzyme. Multiple polyphosphate binding sites were identified on distant portions of the enzyme and were determined to be responsible for the polymer length recognition of the enzyme. In addition, two independently folded domains were identified. The N-terminal domain contained a quasi-processive polyphosphatase active site belonging to the sugar kinase/actin/hsp70 superfamily. The C-terminal domain contained a single polyphosphate binding site and was responsible for nearly all of the PPX affinity for polyphosphate. This domain was also found to confer a highly processive mode of action to PPX. Collectively, these results were used to describe the interaction of polyphosphate with PPX.
<ns4:p>A growing literature is reporting on how the terrestrial carbon cycle is experiencing year-to-year variability because of climate anomalies and trends caused by global change. As CO<ns4:sub>2</ns4:sub> concentration records in the atmosphere exceed 50 years and as satellite records reach over 30 years in length, we are becoming better able to address carbon cycle variability and trends. Here we review how variable the carbon cycle is, how large the trends in its gross and net fluxes are, and how well the signal can be separated from noise. We explore mechanisms that explain year-to-year variability and trends by deconstructing the global carbon budget.</ns4:p><ns4:p> The CO<ns4:sub>2</ns4:sub> concentration record is detecting a significant increase in the seasonal amplitude between 1958 and now. Inferential methods provide a variety of explanations for this result, but a conclusive attribution remains elusive. Scientists have reported that this trend is a consequence of the greening of the biosphere, stronger northern latitude photosynthesis, more photosynthesis by semi-arid ecosystems, agriculture and the green revolution, tropical temperature anomalies, or increased winter respiration.</ns4:p><ns4:p> At the global scale, variability in the terrestrial carbon cycle can be due to changes in constituent fluxes, gross primary productivity, plant respiration and heterotrophic (microbial) respiration, and losses due to fire, land use change, soil erosion, or harvesting. It remains controversial whether or not there is a significant trend in global primary productivity (due to rising CO<ns4:sub>2</ns4:sub>, temperature, nitrogen deposition, changing land use, and preponderance of wet and dry regions). The degree to which year-to-year variability in temperature and precipitation anomalies affect global primary productivity also remains uncertain. For perspective, interannual variability in global gross primary productivity is relatively small (on the order of 2 Pg-C y<ns4:sup>-1</ns4:sup>) with respect to a large and uncertain background (123 +/- 4 Pg-C y<ns4:sup>-1</ns4:sup>), and detected trends in global primary productivity are even smaller (33 Tg-C y<ns4:sup>-2</ns4:sup>). Yet residual carbon balance methods infer that the terrestrial biosphere is experiencing a significant and growing carbon sink. Possible explanations for this large and growing net land sink include roles of land use change and greening of the land, regional enhancement of photosynthesis, and down regulation of plant and soil respiration with warming temperatures. Longer time series of variables needed to provide top-down and bottom-up assessments of the carbon cycle are needed to resolve these pressing and unresolved issues regarding how, why, and at what rates gross and net carbon fluxes are changing.</ns4:p>
In the pursuit of a decarbonized society, electrocatalytic CO<sub>2</sub> conversion has drawn tremendous research interest in recent years as a promising route to recycling CO<sub>2</sub> into more valuable chemicals. To achieve high catalytic activity and selectivity, nanocatalysts of diverse structures and compositions have been designed. However, the dynamic structural transformation of the nanocatalysts taking place under operating conditions makes it difficult to study active site configurations present during the CO<sub>2</sub> reduction reaction (CO<sub>2</sub>RR). In addition, although recognized as consequential to the catalytic performance, the reaction microenvironment generated near the nanocatalyst surface during CO<sub>2</sub>RR and its impact are still an understudied research area. In this Perspective, we discuss current understandings and difficulties associated with investigating such dynamic aspects of both the surface reaction site and its surrounding reaction environment as a whole. We further highlight the interactive influence of the structural transformation and the microenvironment on the catalytic performance of nanocatalysts. We also present future research directions to control the structural evolution of nanocatalysts and tailor their reaction microenvironment to achieve an ideal catalyst for improved electrochemical CO<sub>2</sub>RR.
This paper explores the roles of empiricism and determinism in science and concludes that the intellectual exercise that we call “science” is best described as the transition from empiricism (i.e., observation) to determinism, which is the philosophy that the future can be predicted from the past based on the natural laws that are condensations of all previous scientific knowledge. This transition (i.e., “science”) is accomplished by formulating theories to explain the observations and models that are based on those theories to predict new phenomena. Thus, models are the computational arms of theories, and all models must possess a theoretical basis, but not all theories need to predict. The structure of a deterministic model is reviewed, and it is emphasized that all models must contain an input, a model engine, and an output, together with a feedback loop that permits the continual updating of the model parameters and a means of assessing predictions against new observations. This latter feature facilitates the application of the “scientific method” of cyclical prediction/assessment that continues until the model can no longer account for new observations. At that point, the model (and possibly the theory, too) has been “falsified” and must be discarded and a new theory/model constructed. In this regard, it is important to stress that no amount of successful prediction can prove a theory/model to be “correct”, because theories and models are merely the figments of our imagination as developed through imperfect senses and imperfect intellect and, hence, are invariably wrong at some level of detail. Contrariwise, a single failure of a model to predict an observation invalidates (“falsifies”) the theory/model. The impediment to model building is complexity and its impact on model building is discussed. Thus, we employ instruments such as microscopes and telescopes to extend our senses to examining smaller and larger objects, respectively, just as we now employ computers to extend our intellects as reflected in our computational prowess. The process of model building is illustrated with reference to the deterministic Coupled Environment Fracture Model (CEFM) that has proven to be highly successful in predicting crack growth rate in metals and alloys in contact with high-temperature aqueous environments of the type that exist in water-cooled nuclear power reactor primary coolant circuits.
