Chapter 9 - Repeatability

ABSTRACTUpdating of reservoir models by history matching of 4D seismic data along with production data gives us a better understanding of changes to the reservoir, reduces risk in forecasting and leads to better management decisions. This process of seismic history matching requires an accurate representation of predicted and observed data so that they can be compared quantitatively when using automated inversion. Observed seismic data is often obtained as a relative measure of the reservoir state or its change, however. The data, usually attribute maps, need to be calibrated to be compared to predictions. In this paper we describe an alternative approach where we normalize the data by scaling to the model data in regions where predictions are good. To remove measurements of high uncertainty and make normalization more effective, we use a measure of repeatability of the monitor surveys to filter the observed time‐lapse data.We apply this approach to the Nelson field. We normalize the 4D signature based on deriving a least squares regression equation between the observed and synthetic data which consist of attributes representing measured acoustic impedances and predictions from the model. Two regression equations are derived as part of the analysis. For one, the whole 4D signature map of the reservoir is used while in the second, 4D seismic data is used from the vicinity of wells with a good production match. The repeatability of time‐lapse seismic data is assessed using the normalized root mean square of measurements outside of the reservoir. Where normalized root mean square is high, observations and predictions are ignored. Net: gross and permeability are modified to improve the match.The best results are obtained by using the normalized root mean square filtered maps of the 4D signature which better constrain normalization. The misfit of the first six years of history data is reduced by 55 per cent while the forecast of the following three years is reduced by 29 per cent. The well based normalization uses fewer data when repeatability is used as a filter and the result is poorer. The value of seismic data is demonstrated from production matching only where the history and forecast misfit reductions are 45% and 20% respectively while the seismic misfit increases by 5%. In the best case using seismic data, it dropped by 6%. We conclude that normalization with repeatability based filtering is a useful approach in the absence of full calibration and improves the reliability of seismic data.

Computational modeling, such as finite element analysis, is employed in a range of biomechanics specialties, including impact biomechanics and surgical planning. These models rely on accurate material properties for skeletal muscle, which comprises roughly 40% of the human body. Due to surrounding tissues, compressed skeletal muscle in vivo likely experiences a semi-confined state. Nearly all previous studies investigating passively compressed muscle at the tissue level have focused on muscle in unconfined compression. The goals of this study were to (1) examine the stiffness and time-dependent material properties of skeletal muscle subjected to both confined and unconfined compression (2) develop a model that captures passive muscle mechanics under both conditions and (3) determine the extent to which different assumptions of volumetric behavior affect model results. Muscle in confined compression exhibited stiffer behavior, agreeing with previous assumptions of near-incompressibility. Stress relaxation was found to be faster under unconfined compression, suggesting there may be different mechanisms that support load these two conditions. Finite element calibration was achieved through nonlinear optimization (normalized root mean square error <6%) and model validation was strong (normalized root mean square error <17%). Comparisons to commonly employed assumptions of bulk behavior showed that a simple one parameter approach does not accurately simulate confined compression. We thus recommend the use of a properly calibrated, nonlinear bulk constitutive model for modeling of skeletal muscle in vivo. Future work to determine mechanisms of passive muscle stiffness would enhance the efforts presented here.

The first lactation weekly test day milk yield (WTDMY) records (57233) and monthly test day milk yield (MTDMY) records (11970) spread over a period of 23 years (1984– 2006) sired by 82 bulls of Karan Fries cattle were used to develop the best lactation curve model. The lactation curve parameters of Quadratic cum log model (QCLM), Gamma function (GF), Cobby Le Du model (CLDM), Polynomial regression function (PRF) and Multiphasic logistic function (MLF) were estimated. The average weekly test day milk yield was predicted with high degree of accuracy (R2 &gt; 85%) by all the models with the maximum accuracy (R2 = 99.50%) obtained by polynomial regression function (PRF) and the least fit (R2 = 87.90%) was obtained by Gamma function (GF). However Quadratic cum log model (R2 = 99.20%) was almost equal to polynomial regression function and was better than Cobby Le Du model (R2 = 92.80%). The average root mean square error (RMSE) was found to be minimum with PRF (0.0121 Kg) followed by MLF (Triphasic). Thus the best fit model was polynomial regression function, which was better than other functions for prediction of first lactation WTDMY. The average monthly test day milk yield was predicted with high degree of accuracy (R2 &gt; 75%) by all the models with the maximum accuracy (R2 &gt; 99.41%) obtained by polynomial regression function (PRF) and the least fit was obtained with gamma function (R2 = 79.05%). However, quadratic-cum-log model (R2 = 99.28%) was almost equal to polynomial regression function and was better than CLDM (R2 = 89.50%). The average root mean square error (RMSE) was found to be minimum with PRF (0.0061 kg) followed by QCLM (0.0620). Thus the best fit model was polynomial regression function for prediction of both weekly and monthly test day milk yield of first lactation in Karan Fries cows.

A total of 110 patients with obstructive sleep apnea hypoventilation syndrome were selected from January 2017 to December 2018, including 76 cases of men and 34 females, aged 40-50 y and according to the apnea hyponea index, patients were classified into 3 groups of moderate to moderate and 5 apnea hyponea index 20 was divided into group A (36 cases), the 40 of 20 to apnea hyponea index is divided into group B (45 cases) and apnea hyponea index the 40 of 20 to 40 is divided into group B (45 cases) and apnea hyponea index to 40 is divided into group C (29 cases). In the same period in the health check-up center to extract 40 cases without obstructive sleep apnea hypopnea syndrome, arrhythmia and other medical history of health check-up objects. 24 h Holter-monitoring and polysomography monitoring of 4 groups of people, observed and recorded heart rate variability indicators for each group, including all standard deviations for sinus cardiac arrest, standard deviation per 5 min sinus cardiac interstitial period, the average root mean square of successive differences of the total sinus intercardial period difference is divided by the number of individuals of the sinus cardiac interstitial period of 50 ms the average root mean square of successive differences of the total sinus intercardial period difference is divided by the number of individuals of the sinus cardiac inner struggle period of 50 ms divided by the percentage of the total sinus cardiac inner struggle period (proportion of NN50), low frequency and high frequency. Standard deviation of all sinus beats, standard deviation of the mean value of sinus, root mean square of successive differences, proportion of NN50, low frequency and high frequency were lower than those in the control group (p<0.05) in patients with obstructive sleep apnea hypopnea syndrome. The proportion of NN50 and high frequency in group C were lower than A and B (p<0.05) and the low frequency was higher than the proportion of NN50 and high frequency in group C were lower than A and B (p<0.05) and the low frequency was higher than group A, group B and control group (p<0.05). Holter-monitoring is a non-invasive test, the price is not high and it can be a regular test for testing the autonomic nervous function in patients with obstructive sleep apnea hypopnea syndrome.

Field repeatability of OBC 3D seismic data is investigated for evaluating feasibility of time-lapse (4D) seismic survey for monitoring water saturation in a carbonate reservoir in the Middle East. We use co-located data sets, which were acquired in a small part of a giant 3D survey by re-deployed shots/receivers at a short time interval, between which no reservoir change is supposed. Both pre- and post-stack seismic data are examined using two repeatability indices: normalized root mean square (NRMS) and predictability, each complementarily quantifying the similarity of two seismic traces, i.e., data repeatability. For pre-stack data, source-receiver mis-location along the offset orientation is found to control the repeatability. Post-stack repeatability NRMS evaluated after basic data processing including noise removal, stacking, and time shift, turns out to be around 10 %. The presence of noise and static shift severely degrades repeatability in post-stack. This data-driven repeatability evaluation takes account of the influences from shot/receiver mis-positioning, source-generated coherent and incoherent noise, and location-dependent near surface effect, which conventional approaches may under-estimate. The post-stack repeatability, however, is evaluated in a small area and can be laterally variable, since the 3D seismic data quality is strongly area-dependent. We therefore correlate the NRMS with "snapshot" 3D S/N (SN ratio of a 3D) of a single set of the repeated 3D and then use this relation for extrapolating the NRMS to the entire field through 3D S/N of the full field 3D. Combining this analysis with the result of our rock physics modeling, which reveals the magnitude of expected change in reservoir response, we conclude the feasibility of 4D seismic technique in this field is area-dependent. The proposed procedure provides more realistic area-dependent 4D repeatability than conventional approaches.

HighlightsSoftware was developed for estimation of DSSAT CSM-CROPGRO-Soybean cultivar coefficients.Phenology-related coefficients were estimated based on observed phenological events.Growth-related cultivar coefficients were estimated based on time-series observations.Cultivar coefficients were optimized based on single- and multiple-experiment data sets.Abstract. The Decision Support System for Agrotechnology Transfer (DSSAT) is one of the most popular software solutions for predicting crop growth and yield while capturing the effects of management practices and interactions between the crop and the environment. Accurate estimation of the crop cultivar coefficients that govern in-season growth and development is critical for correct yield estimates. The manual cultivar coefficient estimation process is time-consuming and results in user-dependent, subjective optimums that are difficult to reproduce. Typically, end-of-season observations (point-based) are used for estimating dynamic in-season biomass accumulation rates. The objective of this study was to develop a time-series estimator (TSE) capable of using multiple in-season observations for estimating the coefficients that define in-season growth and biomass partitioning. Using the TSE, cultivar coefficients were estimated based on multiple in-season observations of leaf area index (LAI) and shoot, leaf, and grain dry matter weights. The cultivar coefficients were estimated from single- and multiple-treatment (seasons and locations) in-season observations. This was done for two cultivars for six management × environment combinations. Estimated multiple-treatment based cultivar coefficients were evaluated with an independent data set and compared to DSSAT standard (manual) coefficients and the cultivar coefficients estimated with the GLUE method. The average normalized root mean squared error (nRSME) for LAI and shoot, leaf, and grain weights was 26% lower for one cultivar and about the same for the other cultivar when compared to the DSSAT standard. Because GLUE uses end-of-season point-based cultivar coefficient estimation, the grain weight over time was underestimated in earlier phases and more accurate toward harvest. The TSE-estimated cultivar coefficients based on 346 in-season observations across multiple target variables and six experiments more accurately reflected in-season growth and grain weight without compromising final grain weight predictions. Keywords: . CROPGRO-Soybean, DSSAT, Genetic coefficients, Normalized root mean square error minimization, Time-seris observations.

Effects of Speed on Spatiotemporal Gluteus Maximus Activation During a Chair-Rise Following Chronic Stroke

Advances in corneal refractive surgery have allowed ophthalmologists to correct ocular higher-order aberrations. To obtain more information on the ocular aberrations generated from the optical axis, mydriasis is required. The aim of this study is to evaluate the changes in higher-order aberrations with the use of various mydriatics. Higher-order aberrations were measured in 21 eyes of 21 subjects (age range 24-37 years; 13 males, 8 females). Repeated measurements were conducted before and after the installation of three different mydriatics: 10% phenylephrine, 1% tropicamide, or 1% cyclopentolate. At a pupil size of 6 mm, the average root mean square value of higher-order aberrations (HO-RMS) was 0.430 mum in undilated eyes, and 0.413, 0.410, and 0.477 mum after installation of phenylephrine, tropicamide, and cyclopentolate, respectively. There were no statistically significant differences in the HO-RMS between the four conditions. There was a significant difference in the spherical aberration between the undilated or phenylephrine-treated eyes, compared to those treated with tropicamide or cyclopentolate. Cycloplegic mydriatics seemed to shift spherical aberration in a positive direction. These results suggest that mydriatics may affect higher-order aberrations, especially spherical aberration, and this should be considered when performing wavefront analysis and when correcting wavefront errors.

Abstract. Hydrological models are usually systems of nonlinear differential equations for which no analytical solutions exist and thus rely on numerical solutions. While some studies have investigated the relationship between numerical method choice and model error, the extent to which extreme precipitation such as that observed during hurricanes Harvey and Katrina impacts numerical error of hydrological models is still unknown. This knowledge is relevant in light of climate change, where many regions will likely experience more intense precipitation. In this experiment, a large number of hydrographs are generated with the modular modeling framework FUSE (Framework for Understanding Structural Errors), using eight numerical techniques across a variety of forcing data sets. All constructed models are conceptual and lumped. Multiple model structures, parameter sets, and initial conditions are incorporated for generality. The computational cost and numerical error associated with each hydrograph were recorded. Numerical error is assessed via root mean square error and normalized root mean square error. It was found that the root mean square error usually increases with precipitation intensity and decreases with event duration. Some numerical methods constrain errors much more effectively than others, sometimes by many orders of magnitude. Of the tested numerical methods, a second-order adaptive explicit method is found to be the most efficient because it has both a small numerical error and a low computational cost. A small literature review indicates that many popular modeling codes use numerical techniques that were suggested by this experiment to be suboptimal. We conclude that relatively large numerical errors may be common in current models, highlighting the need for robust numerical techniques, in particular in the face of increasing precipitation extremes.

Phenological shifts in plant species are one of the most conspicuous signs of climate change impact on the biosphere. Modeling phenological variations of plant species over broad regions is challenging because of the varied climatic requirements of geographic populations due to local adaptation. In this study, we developed an empirical method to calibrate phenological models of temperate trees using latitude as a predictor to account for local adaptation of populations to a N-S temperature gradient. Fourteen widely distributed tree species in the eastern U.S.A. were investigated using data from the USA-National Phenology Network. We implemented the method in a basic thermal time bud break model to introduce the algorithm of the method and test its effectiveness. For each species, dates of breaking leaf buds were first predicted using a traditional non-spatial model and then with a spatial model that has the critical thermal forcing requirements calibrated for different populations at varied latitudes. As anticipated, non-spatial model predictions that assumed a uniform forcing requirement across latitudes showed consistent and systematic biases at both higher (overestimation-predictions being later) and lower (underestimation-predictions being earlier) latitudes. Spatial models that have been calibrated using our method removed the geographic biases and yielded latitudinal gradients that more closely matched those of the observations. The spatial models also reduced the overall prediction errors from an average root mean square error (RMSE) of 32.2 days to 20.4 days for the training dataset and an average root mean square error for prediction (RMSEP) of 32.2 days to 19.9 days for the testing dataset. This paper is focused on introducing the new calibration method as a preparatory step toward developing operational models that may potentially predict large-scale and range-wide phenological responses of various plant species to climatic changes with improved local accuracy.

Summary In the present study , double-difference earthquake relocation techniques is applied to investigate sources of seismicity in North Tabriz Fault (NTF) with the sixlayered velocity model (Bayram nejad et al., 2008 , Table 1) in the northwest of Iran . The study area is a complex region of deformation and has the potential in which large earthquakes occur.In the first step ,we relocated approximately 1511 local earthquakes (figure 1),located by the Iranian Seismological Center (IRSC), that have been occurred on North Tabriz Fault (figure 1) within 15 years from 1996 to 2010.Our relocating process is based on the double-difference earthquake location algorithm (Waldhauser and Ellsworth 2000) and six-layered velocity model is obtained especially in North-West and we assumed the 1511 local earthquakes as one cluster.Under some circumstances , first P arrival times were recorded by at least 4 stations with epicentral distance of less than 250 km and MAXSEP of 10 km, second considering RMS < 0.5 sec and the depth from the surface to 30 km . In the next step , in order to identify significant structural features and increase accuracy we added one more premise which is accepting the events with the magnitude limitation of 2.5 and higher. we categorized the events into three clusters (figure 5) thus the 1511 events reduced to 331 events. Results showed the reduction of not only average horizontal and vertical errors but also average root mean square (RMS) travel time residuals over earthquake locations of IRSC catalog .The relocated seismicity (three clusters) images fault structures at different depths (figure 6) that are in close correlation with the tectonic structure of major fault systems expressed at the surface. The new findings show that the focal depth of most earthquakes in this region was at depths ranging from the surface to 25 km .

Objective To analyze the bio-mechanics when hemiplegics reach with the unaffected upper limb. Methods Thirty post-stroke hemiplegics were selected into the patient group, while 23 healthy counterparts were chosen for the control group. Both groups completed a reaching test of their upper limbs which divided reaching into a moving stage and a holding stage. Surface electromyography (sEMG) data were recorded during the tests along with the degree of torso twist, the range of motion of the shoulder, movement velocity, smoothness of movement and angle divergence collected using a wearable micro-sensor motion capture system. Results For the stroke patients whose dominant upper limb was unaffected, the average root mean square (RMS) signal from the upper trapezius (34.3 μV) and the average torso twist (-1.4°) in the moving phase were significantly larger than among the control subjects (19.7 μV and -2.3°), but their average movement velocity was significantly slower. In the holding phase the average RMS signal from the upper trapezius (55.4 μV) was still significantly higher than in the control group, but their average pectoralis major signal and the integrated EMG ratio of the anterior segments of the deltoid and upper trapezius muscle pairs were significantly lower. For the stroke patients whose dominant upper limbs were affected, in the moving phase their average signal from the middle segments of the deltoid were significantly greater than those of the controls, but their movement velocity was significantly slower. For the control subjects, in the moving phase the average signal from the upper trapezius on their non-dominant side was significantly higher than that from the dominant upper limb. The integrated EMG ratio from the anterior segments of the deltoid and upper trapezius muscle pairs on that side was smaller throughout the whole reaching movement. Conclusion The bio-mechanical characteristics in reaching of the unaffected upper limbs of stroke patients are not the same as those of the corresponding upper limbs of healthy subjects. It is more reasonable to select the corresponding upper limbs of healthy subjects as controls when a bio-mechanical study of the affected upper limbs is conducted. Key words: Stroke; Reaching; Kinematics; Electromyography

Multivariable Recursive Subspace Identification with Application to Artificial Pancreas Systems

The motor enzymes that belong to the family of RNA helicases catalyze the strand separation of duplex RNA via ATP hydrolysis. Among these enzymes, Escherichia coli DbpA is a unique RNA helicase because it possesses ATPase-specific activity toward the peptidyl transferase center in 23 S ribosomal RNA. For this reason, it has been the subject of numerous biochemical and structure-function studies. The ATP-stimulated unwinding activity of DbpA toward specific and nonspecific RNA duplexes has been demonstrated. However, the underlying molecular and structural basis, which facilitates its helicase activities, is presently not known. We combined time-dependent limited proteolysis digestion, fluorescence spectroscopy, and three-dimensional structural homology modeling techniques to study the structural conformations of DbpA with respect to its binding to stoichiometric ratios of RNA and cofactors. We show that the conformational state of DbpA is markedly different in the ADP-bound state than in any other state (ATP- or RNA-bound). These results, together with structural homology studies, suggest that a hinge region located in the core domain of DbpA mediates such conformational changes.

The aim of this study was to compare mechanomyogram (MMG) recorded by a condenser microphone (MIC) and an accelerometer (ACC) during submaximal isometric, concentric and eccentric contractions in 14 males. The maximal voluntary force (MVC) of the biceps brachii was measured. The subjects were asked to do short duration isometric, concentric and eccentric contraction at 10%, 30%, 50%, 70% MVC twice. For the concentric and eccentric contraction, the subject bent his arm for 3 s (concentric) then held it for 3 s and extended (eccentric) during 3 s. The normalized root mean square (RMS) and mean power frequency (MPF) increased linearly with increased force for both transducers. There was a correlation between MIC MPF and ACC MPF at 10%, 30%, 50% MVC, and between MIC RMS and ACC RMS at 30% MVC during isometric contractions. There was significantly higher MPF for the ACC than for the MIC in concentric and eccentric modes, while the RMS did not differ among transducers in the three contraction modes. The RMS and MPF values coefficient of variations were significantly larger during anisometric contractions compared with isometric contractions and were lower for the accelerometer than for the microphone. The present results obtained during isometric, concentric and eccentric contractions of increased intensity showed that the information contained in microphone- and accelerometer-based MMG signals is different despite similar trends. It can be concluded that at low-moderate movement velocity, concentric contractions can be investigated by means of accelerometer and microphone.