frequency model
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2022 ◽  
Vol 9 ◽  
Author(s):  
Pengfei Dang ◽  
Qifang Liu ◽  
Linjian Ji

By using the stochastic finite-fault method based on static corner frequency (Model 1) and dynamic corner frequency (Model 2), we calculate the far-field received energy (FRE) and acceleration response spectra (SA) and then compare it with the observed SA. The results show that FRE obtained by the two models depends on the subfault size regardless of high-frequency scaling factor (HSF). Considering the HSF, the results obtained by Model 1 and Model 2 are found to be consistent. Then, similar conclusion was obtained from the Northridge earthquake. Finally, we analyzed the reasons and proposed the areas that need to be improved.


Author(s):  
Irina Homozkova ◽  
Yuriy Аndriyovych Plaksiy

On the basis of a programmed-numerical approach, new values of the coefficients in the Miller orientation algorithm are obtained. For this, an analytical reference model of the angular motion of a rigid body was applied in the form of a four-frequency representation of the orientation quaternion.The numerical implementation of the reference model for a given set of frequencies is presented in the form of constructed trajectories in the configuration space of orientation parameters. A software-numerical implementation of Miller's algorithm is carried out for different values of the coefficients and the values of the coefficients are obtained, which optimize the error of the accumulated drift. It is shown that for the presented reference model of angular motion, Miller's algorithm with a new set of coefficients provides a lower computational drift error compared to with the classic Miller algorithm and the Ignagni modification, which are optimized for conical motion.


Minerals ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 40
Author(s):  
Zhuen Ruan ◽  
Aixiang Wu ◽  
Raimund Bürger ◽  
Fernando Betancourt ◽  
Rafael Ordoñez ◽  
...  

Shear-induced polymer-bridging flocculation is widely used in the solid–liquid separation process in cemented paste backfill, beneficial to water recycling and tailings management in metal mines. A flocculation kinetics model based on Population Balance Model (PBM) is proposed to model the polymer-bridging flocculation process of total tailings. The PBM leads to a system of ordinary differential equations describing the evolution of the size distribution, and incorporates an aggregation kernel and a breakage kernel. In the aggregation kernel, a collision frequency model describes the particle collision under the combined effects of Brownian motions, shear flow, and differential sedimentation. A semi-empirical collision efficiency model with three fitting parameters is applied. In the breakage kernel, a new breakage rate coefficient model with another three fitting parameters is introduced. Values of the six fitting parameters are determined by minimizing the difference between experimental data obtained from FBRM and modeling result through particle swarm global optimization. All of the six fitting parameters vary with flocculation conditions. The six fitting parameters are regressed with the flocculation factors with six regression models obtained. The validation modeling demonstrates that the proposed PBM quantifies well the dynamic evolution of the floc size during flocculation under the given experimental setup. The investigation will provide significant new insights into the flocculation kinetics of total tailings and lay a foundation for studying the performance of the feedwell of a gravity thickener.


2021 ◽  
Author(s):  
Ivan Priezzhev ◽  
Dmitry Danko ◽  
Uwe Strecker

Abstract Instead of relying on analytical functions to approximate property relationships, this innovative hybrid neural network technique offers highly adaptive, full-function (!) predictions that can be applied to different subsurface data types ranging from (1.) core-to-log prediction (permeability), (2.) multivariate property maps (oil-saturated thickness maps), and, (3.) petrophysical properties from 3D seismic data (i.e., hydrocarbon pore volume, instantaneous velocity). For each scenario a separate example is shown. In case study 1, core measurements are used as the target array and well log data serve training. To analyze the uncertainty of predicted estimates, a second oilfield case study applies 100 iterations of log data from 350 wells to obtain P10-P50-P90 probabilities by randomly removing 40% (140 wells) for validation purposes. In a third case study elastic logs and a low-frequency model are used to predict seismic properties. KNN generates a high level of freedom operator with only one (or more) hidden layer(s). Iterative parameterization precludes that high correlation coefficients arise from overtraining. Because the key advantage of the Kolmogorov neural network (KNN) is to permit non-linear, full-function approximations of reservoir properties, the KNN approach provides a higher-fidelity solution in comparison to other linear or non-linear neural net regressions. KNN offers a fast-track alternative to classic reservoir property predictions from model-based seismic inversions by combining (a) Kolmogorov's Superposition Theorem and (b) principles of genetic inversion (Darwin's "Survival of the fittest") together with Tikhonov regularization and gradient theory. In practice, this is accomplished by minimizing an objective function on multiple and simultaneous outputs from full-function (via look-up table) Kolmogorov neural network runs. All case studies produce high correlations between actual and predicted properties when compared to other stochastic or deterministic inversions. For instance, in the log to seismic prediction better (simulated) resolution of neural network results can be discerned compared to traditional inversion results. Moreover, all blind tests match the overall shape of prominent log curve deflections with a higher degree of fidelity than from inversion. An important fringe benefit of KNN application is the observed increase in seismic resolution that by comparison falls between the seismic resolution of a model-based inversion and the simulated resolution from seismic stochastic inversion.


2021 ◽  
Author(s):  
Khalid Obaid ◽  
Abdelwahab Noufal ◽  
Abdulrahman Almessabi ◽  
Atef Abdelaal ◽  
Karim Elsadany ◽  
...  

Abstract This study summarizes the efforts taken to provide reliable reservoir characterizations products to mitigate seismic interpretation challenges and delineation of the reservoirs. ADNOC has conducted seismic exploration activities to assess Miocene to Upper Cretaceous aged reservoirs in East Onshore Abu Dhabi. The Oligo-Miocene section comprises of interbedded salt (mainly halite), anhydrite, limestones and marls. Deposited in the foreland basin related to the Oman thrust-belt. Ranging in thickness from nearly 1.5 km in the depocenter to almost nil on the forebulge located to the west of the studied area. The well data based geological model suggests that initially porous rocks (presumably grain-supported carbonates) encompassed polyphase sulfate cementation during recurrent subaerial exposure in which pores and grains were recrystallized sometimes completely too massive, tight anhydrite beds. This heterogeneity of the complex shallow section showing high variation of velocity impact seismic imaging, and interpretation to model the stratigraphic/structural framework and link it with reservoir characterization. Hence, ADNOC decided to conduct a trial on state-of-art technique Litho-Petro-Elastic (LPE) AVA Inversion to mitigate the seismic interpretation challenges and delineate the reservoirs. The LPE AVA inversion provides a single-loop approach to reservoir characterization based on rock physics models and compaction trends, reducing the dependency on a detailed prior the low frequency model, Where the rock modelling and lithology classification are not separate steps but interact directly with the seismic AVO inversion for optimal estimates of lithologies and elastic properties. The LPE inversion scope requires seismic data conditioning such as CMP gathers de-noising, de-multiple, flattening and amplitude preservation, in addition to detailed log conditioning, petro-elastic and rock physics analysis to maximize the quality and value of the results. The study proved that the LPE AVA Inversion can be used to guide seismic interpreters in mapping the structural framework in challenging seismic data, as it managed to improve the prospect evaluation.


Author(s):  
Enrico Bottaro ◽  
Andrea Del Pizzo ◽  
Luigi Pio Di Noia ◽  
Domenico Nardo ◽  
Santi Agatino Rizzo ◽  
...  

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