House Price Expectations

Utilizing new survey data collected between 2009 and 2014, this paper analyzes American households' subjective expectations on future home values. We explore the relationship between house price expectations, local economic conditions, and households' individual characteristics. We examine the heterogeneity in expectations based on panel data models. In particular, we estimate the individual- and time-specific subjective probability distributions for five-year-ahead home values. House price expectations vary significantly over time, and are positively related to past housing returns and perceived economic conditions. There is large variation in both the central tendency and the uncertainty of expectations on future home values across individuals, which is associated with several socio-economic and demographic factors. Comparing expectations and realizations shows that households only partially anticipated the large downward changes in home values in the time period 2009-2011

Characterization of Fractured Reservior and Simulation of EOR Techniques

Oil production from the fractured tight reservoirs has been increasing since the early 2000s and now accounts for a large portion of onshore US oil production. Complex fracture network composed of hydraulic fractures and natural fractures plays an important role in well depletion, but its geometry and properties have large uncertainty. On the other hand, different methods to improve oil recovery in the fractured tight reservoir have been tested in labs and fields recently. Although people generally understand the micro-mechanisms of these methods, the impact of fracture network on production enhancement has not been systematically studied. In this study, unstructured gridding algorithms are improved to apply to the reservoir with high fracture density. Then the depletion behavior of the dual-porosity methods and the discrete fracture network (DFN) method are compared based on the conceptual model, demonstrating the necessity of using DFN method in the fractured tight reservoirs. In terms of DFN application in real formations, microseismicity (MS), core observation, pumping schedule, outcrop map, and FMI log are used to characterize their fracture network geometry. History match is done to calibrate the reservoir models, which can gain confidence in using the DFN models for further study. To model EOR techniques in field scale, micro-mechanisms revealed from lab experiments about surfactant imbibition and COv2 huff n’ puff are used to generate appropriate simulation parameters. A series of surfactant spontaneous imbibition and COv2 huff n’ puff simulations are done on those calibrated models to study the EOR performance and seek the optimal operation parameters. Simulation results show that dual-porosity methods cannot take the transition flow between fracture-matrix into account, and cannot accurately model the discontinuity feature of fracture networks, which are critical to EOR performance. After calibration, DFN dynamic fluid flow models can approximately match the production data. Surfactant spontaneous imbibition simulation shows a marginal production increase compared to water imbibition cases. It is found that wettability alteration incurred in the fracture system could play a more important role in production enhancement, compared to the wettability alteration incurred in a small range of matrix. Simulation results of COv2 huff n’ puff indicate injection pressure and injection schedule impact the recovery performance. This study proposes a workflow to build reliable DFN models to represent fractured tight reservoirs with the use of multiple data sources. Furthermore, the performance of EOR technics is investigated with various scenarios. It is found that fracture geometry significantly affects depletion and EOR performance, so that appropriate field-scale simulation, in addition to corescale experiments, is recommended for EOR pilot design

Simulation Study for Improving Seawater Polymer Flood Performance in Stratified High Temperature Reservoirs

Polymer flood has achieved technical and commercial success, especially for its large-scale application in the Daqing oilfield in China. However, previous field tests indicated polymer flood was not economically successful for high temperature reservoirs when injected with high salinity, high hardness water. Novel thermal and salinity-resistance polymers have been developed, and their properties are tested via comprehensive lab experiments, which encourage further development of polymer flooding in high-temperature and high-salinity reservoirs. To achieve a promising recovery effect, numerical simulation, including all significant physicochemical phenomena, must be carried out before field implementation to realize the reservoir response to polymer. An optimized recovery design, which minimizes costs and increases the process efficiency, should be proposed for reservoir models representing real harsh conditions including severe heterogeneity. In this work, the effects of shear thinning, thermal thinning, degradation of polymer/seawater solution to oil recovery performance in stratified reservoir are studied in various temperature conditions. Also supporting measures for polymer flood, such as mechanical water shutoff and in-depth profile control, are studied to evaluate their ability in harsh reservoir conditions. Thermal thinning and shear thinning properties of polymer/seawater solution were measured by a rheometer, and compared with published data. Degradation and adsorption properties of the polymers, as well as the gelation reaction and resistance properties of the gel were summarized from literature review generating reasonable parameters for simulation. Simulation results indicate that thermal thinning of polymers has a marginal effect on the final oil recovery. Another property related to temperature, polymer thermal degradation, is obviously influenced by temperature, leading to decrease of the final oil recovery to different extent. Both water shutoff and in-depth profile control can improve waterflood. However deep profile control will be more efficient if polymer flood is applied, and the combination of in-depth profile control and polymer flood carried out with low injection temperature achieve the best recovery performance

Low resistance, large dimension entrance to the inner cavity of BK channels determined by changing side-chain volume

Large-conductance Ca2+- and voltage-activated K+ (BK) channels have the largest conductance (250–300 pS) of all K+-selective channels. Yet, the contributions of the various parts of the ion conduction pathway to the conductance are not known. Here, we examine the contribution of the entrance to the inner cavity to the large conductance. Residues at E321/E324 on each of the four α subunits encircle the entrance to the inner cavity. To determine if 321/324 is accessible from the inner conduction pathway, we measured single-channel current amplitudes before and after exposure and wash of thiol reagents to the intracellular side of E321C and E324C channels. MPA− increased currents and MTSET+ decreased currents, with no difference between positions 321 and 324, indicating that side chains at 321/324 are accessible from the inner conduction pathway and have equivalent effects on conductance. For neutral amino acids, decreasing the size of the entrance to the inner cavity by substituting large side-chain amino acids at 321/324 decreased outward single-channel conductance, whereas increasing the size of the entrance with smaller side-chain substitutions had little effect. Reductions in outward conductance were negated by high [K+]i. Substitutions had little effect on inward conductance. Fitting plots of conductance versus side-chain volume with a model consisting of one variable and one fixed resistor in series indicated an effective diameter and length of the entrance to the inner cavity for wild-type channels of 17.7 and 5.6 Å, respectively, with the resistance of the entrance ∼7% of the total resistance of the conduction pathway. The estimated dimensions are consistent with the structure of MthK, an archaeal homologue to BK channels. Our observations suggest that BK channels have a low resistance, large entrance to the inner cavity, with the entrance being as large as necessary to not limit current, but not much larger

Effect of Resting-State fNIRS Scanning Duration on Functional Brain Connectivity and Graph Theory Metrics of Brain Network

As an emerging brain imaging technique, functional near infrared spectroscopy (fNIRS) has attracted widespread attention for advancing resting-state functional connectivity (FC) and graph theoretical analyses of brain networks. However, it remains largely unknown how the duration of the fNIRS signal scanning is related to stable and reproducible functional brain network features. To answer this question, we collected resting-state fNIRS signals (10-min duration, two runs) from 18 participants and then truncated the hemodynamic time series into 30-s time bins that ranged from 1 to 10 min. Measures of nodal efficiency, nodal betweenness, network local efficiency, global efficiency, and clustering coefficient were computed for each subject at each fNIRS signal acquisition duration. Analyses of the stability and between-run reproducibility were performed to identify optimal time length for each measure. We found that the FC, nodal efficiency and nodal betweenness stabilized and were reproducible after 1 min of fNIRS signal acquisition, whereas network clustering coefficient, local and global efficiencies stabilized after 1 min and were reproducible after 5 min of fNIRS signal acquisition for only local and global efficiencies. These quantitative results provide direct evidence regarding the choice of the resting-state fNIRS scanning duration for functional brain connectivity and topological metric stability of brain network connectivity

Patient Dropout Prediction in Virtual Health: A Multimodal Dynamic Knowledge Graph and Text Mining Approach

Virtual health has been acclaimed as a transformative force in healthcare delivery. Yet, its dropout issue is critical that leads to poor health outcomes, increased health, societal, and economic costs. Timely prediction of patient dropout enables stakeholders to take proactive steps to address patients' concerns, potentially improving retention rates. In virtual health, the information asymmetries inherent in its delivery format, between different stakeholders, and across different healthcare delivery systems hinder the performance of existing predictive methods. To resolve those information asymmetries, we propose a Multimodal Dynamic Knowledge-driven Dropout Prediction (MDKDP) framework that learns implicit and explicit knowledge from doctor-patient dialogues and the dynamic and complex networks of various stakeholders in both online and offline healthcare delivery systems. We evaluate MDKDP by partnering with one of the largest virtual health platforms in China. MDKDP improves the F1-score by 3.26 percentage points relative to the best benchmark. Comprehensive robustness analyses show that integrating stakeholder attributes, knowledge dynamics, and compact bilinear pooling significantly improves the performance. Our work provides significant implications for healthcare IT by revealing the value of mining relations and knowledge across different service modalities. Practically, MDKDP offers a novel design artifact for virtual health platforms in patient dropout management