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This research examined thermal comfort in  church buildings in Indonesia by making a comparison between three different Indonesian climatic regions using three different research models. A static model, an adaptation study model and a CFD simulation were used to find the similarities and differences between the results generated from determining thermal comfort in church buildings in the three regions. The comparison revealed that church buildings had different PMV scores at each measuring point that were inversely proportional to the subjects’ response on thermal comfort inside the buildings, i.e. points adjoining with openings affect a low PMV score and a high perceived thermal sensation, and vice versa. The CFD simulation showed that changing the conditions of the openings affects air velocity and flow into the building, which influences the subjects’ thermal comfort response inside the churches.

This short note provides a simple static model to assess the effect of vaccination against hospitalization.

Background: The state of the art in computer modeling of radiofrequency catheter ablation (RFCA) only considers a static model, i.e. it does not allow modeling ablation electrode displacements induced by tissue movement due to heartbeats. This feature is theoretically required, since heartbeat-induced changes in contact force can be detected during this clinical procedure. Methods: We built a 2D RFCA model coupling electrical, thermal and mechanical problems and simulated a standard energy setting (25 W - 30 s). The mechanical interaction between the ablation electrode and tissue was dynamically modeled to reproduce heartbeat-induced changes in the electrode insertion depth from 0.86 to 2.05 mm, which corresponded with contact forces between 10 and 30 g when cardiac tissue was modeled by a hyperelastic Neo-Hookean model with a Young's modulus of 75 kPa and Poisson's ratio of 0.49. Results: The dynamic model computed a lesion depth of 5.86 mm, which is within the range of previous experimental results based on a beating heart for a similar energy setting and contact force (5.6-6.7 mm). Lesion size was practically identical (differences less than 0.02 mm) to that using a static model with the electrode inserted to an average depth (1.46 mm, equivalent to 20 g contact force). Conclusions: The RFCA dynamic model including heartbeat-induced electrode displacement predicts lesion depth reasonably well compared to previous experimental results based on a beating heart model, however this is true only at a standard energy setting and moderate contact force.

Abstract The Thamama group of reservoirs consist of porous carbonates laminated with tight carbonates, with pronounced lateral heterogeneities in porosity, permeability, and reservoir thickness. The main objective of our study was mapping variations and reservoir quality prediction away from well control. As the reservoirs were thin and beyond seismic resolution, it was vital that the facies and porosity be mapped in high resolution, with a high predictability, for successful placement of horizontal wells for future development of the field. We established a unified workflow of geostatistical inversion and rock physics to characterize the reservoirs. Geostatistical inversion was run in static models that were converted from depth to time domain. A robust two-way velocity model was built to map the depth grid and its zones on the time seismic data. This ensured correct placement of the predicted high-resolution elastic attributes in the depth static model. Rock physics modeling and Bayesian classification were used to convert the elastic properties into porosity and lithology (static rock-type (SRT)), which were validated in blind wells and used to rank the multiple realizations. In the geostatistical pre-stack inversion, the elastic property prediction was constrained by the seismic data and controlled by variograms, probability distributions and a guide model. The deterministic inversion was used as a guide or prior model and served as a laterally varying mean. Initially, unconstrained inversion was tested by keeping all wells as blind and the predictions were optimized by updating the input parameters. The stochastic inversion results were also frequency filtered in several frequency bands, to understand the impact of seismic data and variograms on the prediction. Finally, 30 wells were used as input, to generate 80 realizations of P-impedance, S-impedance, Vp/Vs, and density. After converting back to depth, 30 additional blind wells were used to validate the predicted porosity, with a high correlation of more than 0.8. The realizations were ranked based on the porosity predictability in blind wells combined with the pore volume histograms. Realizations with high predictability and close to the P10, P50 and P90 cases (of pore volume) were selected for further use. Based on the rock physics analysis, the predicted lithology classes were associated with the geological rock-types (SRT) for incorporation in the static model. The study presents an innovative approach to successfully integrate geostatistical inversion and rock physics with static modeling. This workflow will generate seismically constrained high-resolution reservoir properties for thin reservoirs, such as porosity and lithology, which are seamlessly mapped in the depth domain for optimized development of the field. It will also account for the uncertainties in the reservoir model through the generation of multiple equiprobable realizations or scenarios.

Abstract This paper proposes an integrated approach to model High Permeability Streaks (HPS) using the case study of heterogeneous carbonate Reservoir B, utilizing static and dynamic data. Modelling the HPS is critical as they play an important role in fluid dynamics within the reservoir. The impact is observed from 60 years of development, where flood front movement is captured by rich density of Pulsed Neutron and recently drilled open hole logs. Injection water is overriding from tighter lower subzones (injected zones) to permeable upper subzones of the reservoir, thereby leaving the tighter lower subzones unswept. Gas cusping down to the oil zone occurs through the HPS resulting in non-uniform gas cap expansion, which leads to early gas breakthrough in producers near the gas cap. The problem with characterizing HPS is associated with their thickness- in Reservoir B it ranges from 0.5 to 2.5ft and occur in multiple subzones in the upper part of the reservoir. The standard triple combo suite of logs does not have the resolution to detect these thin HPS. In addition, the cored interval of the HPS is mainly disintegrated which is attributed majorly to well sorted grain-supported lithofacies. Therefore, sampling for porosity & permeability via Routine Core Analysis (RCA) and Capillary pressure as well as pore throat distribution using Mercury Injection Capillary Pressure (MICP) method is extremely difficult. This results in a gap in the input dataset for the static models, where the higher permeability samples are not captured in logs or cores and are therefore under-represented. Current approach to unify this gap is to use permeability multipliers, which does not honor geological trends. The HPS in Reservoir B has added complexities when compared to other regional HPS. Not only are they multiple and distributed across subzones, there is also preferential movement of water through the HPS within the same area. Of the 3 upper subzones that have HPS, in some areas, water injected in lower subzone will override the HPS in the middle and move right to the HPS in the top subzone, thereby ignoring the hierarchical flood front movement from bottom to the top. A robust workflow was developed in order to address and resolve the above mentioned uncertainties related to High Permeability Streaks. The proposed integrated workflow consisted of five stages: Developing a robust geological conceptual model Mapping spatial distribution & continuity Capturing the vertical presence in cored & uncored wells (depth & thickness) Permeability Quantification of HPS using Well Test Measurements Modelling High Permeability Streaks The paper highlights the utilization of a range of static (core, Routine Core Analysis (RCA), image logs, OH logs) and dynamic data (Pulse Neutron Logs (PNL's), later drilled Open Hole Logs, Production Logging Tools (PLTs) and well test data). Quantitative (HPS depth indicated by water saturation profile indicated by waterflood movement) and Qualitative (Flooding observed but HPS depth is uncertain) depth indicators/flags were generated from the data set and became the foundation of the modelling the HPS. The first step in the workflow is to establish a robust geological conceptual model. For Reservoir B, certain facies contribute to HPS, which are mainly leached Rudist Rudstones and Coated grain Algal Floatstones as well as well sorted Skeletal Grainstones. Based on core observations, they have confirmed vertical stratigraphic presence in each subzone (top, mid, base) which is attributed to storm events. These were consequently mapped using average thickness from core descriptions and revised using contributing facies trend maps and qualitative dynamic observations. These maps served as basis for probability trend distribution for static rock type models. The vertical presence of HPS was increased from 10% to 30% by re-introducing them in the missing core intervals using quantitative dynamic flags and thickness from isochores. Consequently, permeability were assigned in the missing section using the proposed permeability enhancement technique that honors the verified well test measurements. Based on the above improvements, the HPS intervals were mapped to the static rock type with best reservoir quality (SRT 1), which is also linked to certain geological attributes (i.e. lithofacies, diagenetic overprint & depositional environment). The enhanced permeability in the identified HPS intervals is also reflected as upgraded SRT (from lower SRT 2 to best SRT 1). The overall impact is observed by improvement of poro-perm cloud, with added control points for HPS SRT (1), which is vital for permeability modelling. The updated permeability model, captures high perm streaks in terms of vertical presence and magnitude. By introducing higher permeability in the upper subzones of the reservoir, the water overriding/gas cusping phenomena could then be mimicked in the dynamic model. The proposed methodology is an integrated workflow that maximizes the input from each disciplines (G&G, Petrophysics and Reservoir Engineering) to create a robust static model through incorporation of high permeability streaks. The use static and dynamic data, has helped to establish HPS existence/preference, which then could be used to upgrade the permeability/SRT. This will in turn lead to a better static model and a better history match in the dynamic model. It will also led to better remaining in place prediction and enable accurate prediction for future field development, especially where EOR is involved.

Abstract FGIIP (Field Gas Initially in Place) is one of the most essential elements in building dependable static and Integrated Asset Model (IAM). A good estimation of FGIIP will improve history matching and generate reliable forecast. The mature gas field producing under depletion mode is an ideal example where P/Z technique can fit well to re-estimate the FGIIP. Even more, the estimation is also important to narrow down FGIIP uncertainties that initially existed in static model. Reliable FGIIP estimation is achieved by performing multiple P/Z calculations. The process involves dividing reservoir into key areas and each area is represented by individual P/Z prior to summing-up all P/Z to get the total FGIIP. Several scenarios are developed by defining key areas based on permeability variation, areal distribution and PVT behavior. The best FGIIP estimation is then fed back into the static model to generate numerous realizations considering the static uncertainties to produce the same FGIIP. Static models with realistic distribution of properties and good history match are used in the IAM model to generate forecast. The giant onshore gas field is highly heterogeneous having permeability, lateral composition variation and dynamic interaction between wells. To ensure that the heterogeneity observed in the field is honored, multiple key areas are defined by making areal sectorization and lateral PVT variation when estimating FGIIP with P/Z approach. Communication between areas was evidenced from the sectoral P/Z. The field history matching was improved after applying the new estimated FGIIP. It was observed that the sectoral history matching both for production and pressure matches from some selected realizations built in static model resulted in better matches. Succinctly the re-evaluation of static derived FGIIP with P/Z method for the mature gas field was able to reduce the uncertainty range that initially existed. Incorporating the correct estimation of FGIIP in IAM has helped to yield reliable forecast and has enabled the asset to plan proper work programs for further field development. Analytical material balance with P/Z is very applicable for mature gas reservoirs producing under depletion mode. The approach is worth doing to narrow down the uncertainty range that was previously calculated. Moreover, the integration of analytical P/Z with static and dynamic model (IAM) has achieved more reliable forecasting of the mature gas field to proceed with further development plan.

Hydrostatic bearings are superior in terms of their friction and load carrying characteristics when compared to contact based bearings, but non-usable in applications with non-constant curvature counter surfaces. A possible solution to this limitation is the introduction of deformable hydrostatic bearings components that cope with these required deformations. To reduce the required deformation of a single bearing pad, multiple pads can be connected through a so-called whiffletree support system. In this work, a symmetric whiffletree based hydrostatic bearing embodiment is introduced. A 2D quasi-static model is introduced that allows for determining the kinetostatic and path following properties of such a type of bearing. Design considerations are given regarding the joint rotational-, normal-, and shear stiffness of each individual joint, as well as basic bearing layout. The potential of a whiffletree suspended bearing is presented through the use of a case study.

ObjectiveAs part of an effort to design an implementation strategy tailoring tool, our research group sought to understand what is known about how contextual factors and prescriber characteristics affect the adoption of guideline-concordant opioid-prescribing practices in primary care settings.DesignWe conducted a realist synthesis of 71 articles.ResultsWe found that adoption is related to contextual factors at the individual, clinic, health system and environmental levels, which operate via intrapersonal, interpersonal, organisational and structural mechanisms.ConclusionA single static model cannot capture the complexity of the relationships between contexts, mechanisms and outcomes. Instead, a deeper understanding requires a dynamic model that conceptualises clusters of contextual factors and mechanisms that tend towards guideline concordance and clusters that tend toward non-concordance.Trail registration numberClinicalTrial.gov registration number NCT04044521.

A non-quasi-static model for ferroelectric capacitance is developed in this letter. A state transition in the voltage and time domains between two polarization states is formulated first. The quasi-static model is derived from the state transition of voltage domain, and supports the minor loops. Different from the Preisach model, an initial state is supported, and the modulated coercive voltages are responsible for minor loops. The non-quasi -static model is then derived with the state transition in the time domain, similar to a relaxation approximation in MOSFET modeling. The non-quasi-static model reproduces the saturation loop, minor loops, the frequency-dependent characteristics of measured ferroelectric capacitances, with their origins explained from polarization switching relaxation. The pulse width dependent switching is well reproduced with the model.