Statistical prediction of waterflooding performance by K-means clustering and empirical modeling

Statistical prediction is often required in reservoir simulation to quantify production uncertainty or assess potential risks.Most existing uncertainty quantification procedures aim to decompose the input random field to independent random variables,and may suffer from the curse of dimensionality if the correlation scale is small compared to the domain size.In this work,we develop and test a new approach,K-means clustering assisted empirical modeling,for efficiently estimating waterflooding performance for multiple geological realizations.This method performs single-phase flow simulations in a large number of realizations,and uses K-means clustering to select only a few representatives,on which the two-phase flow simulations are implemented.The empirical models are then adopted to describe the relation between the single-phase solutions and the two-phase solutions using these representatives.Finally,the two-phase solutions in all realizations can be predicted using the empirical models readily.The method is applied to both 2D and 3D synthetic models and is shown to perform well in the P10,P50 and P90 of production rates,as well as the probability distributions as illustrated by cumulative density functions.It is able to capture the ensemble statistics of the Monte Carlo simulation results with a large number of realizations,and the computational cost is significantly reduced.

Empirical modeling of normal/cyclo-alkanes pyrolysis to produce light olefins

Due to the complexity of feedstock,it is challenging to build a general model for light olefins production.This work was intended to simulate the formation of ethylene,propene and 1,3-butadiene in alkanes pyrolysis by referring the effects of normal/cyclo-structures.First,the pyrolysis of n-pentane,n-hexane,n-heptane,n-octane,n-nonane,n-decane,cyclohexane,methylcyclohexane,n-hexane and cyclohexane mixtures,and n-heptane and methylcyclohexane mixtures were carried out at 650–800℃,and a particular attention was paid to the measurement of ethylene,propene and 1,3-butadiene.Then,pseudo-first order kinetics was taken to characterize the pyrolysis process,and the effects of feedstock composition were studied.It was found that chain length and cyclo-alkane content can be qualitatively and quantitively represented by carbon atom number and pseudo-cyclohexane content,which made a significant difference on light olefins formation.Furthermore,the inverse proportional/quadratic function,linear function and exponential function were proposed to simulate the effects of chain length,cycloalkane content and reaction temperature on light olefins formation,respectively.Although the obtained empirical model well reproduced feedstock conversion,ethylene yield and propene yield in normal/cycloalkanes pyrolysis,it exhibited limitations in simulating 1,3-butadiene formation.Finally,the accuracy and flexibility of the present model was validated by predicting light olefins formation in the pyrolysis of multiple hydrocarbon mixtures.The prediction data well agreed with the experiment data for feedstock conversion,ethylene yield and propene yield,and overall characterized the changing trend of 1,3-butadiene yield along with reaction temperature,indicating that the present model could basically reflect light olefins production in the pyrolysis process even for complex feedstock.

Pyrolysis Empirical Modeling of Polyester Glass Fiber Reinforced Plastics Using Sestak-Berggren Model Method

Polyester Glass Fiber Reinforced Plastic(Polyester GFRP),a thermosetting plastic comprised of glass fiber and polyester polymer compounds,is extensively utilized in high-speed trains.Unraveling its pyrolysis mechanism is crucial as it significantly influences the combustion characteristics and fire safety aspects.Currently,kinetic research on polyester GFRP primarily focuses on employing the Coats-Redfern method to derive a theoretical kinetic model.However,the pyrolysis process of polyester GFRP is complex and the aforementioned theoretical model fails to accurately describe the pyrolysis mechanism.Therefore,this study seeks to utilize the Sestak and Berggren(SB) model as a methodological approach to reveal the complex reaction mechanism during the pyrolysis process.Based on thermogravimetric analysis,the entire pyrolysis process of polyester GFRP is divided into two primary stages.Furthermore,model-free methods are employed to ascertain the activation energy and pre-exponential factor.The results show that the fitted empirical models of the two main pyrolysis stages are f(α)=(1-α)^(1.47)[-ln(1-α)]^(1.50) and f(α)=(1-α)^(1.77)[-ln(1-α)]^(1.72),respectively.The predicted results are in good agreement with experimental data under different heating rates,which indicates that the empirical model can sufficiently describe the pyrolysis process of polyester GFRP.

Anisotropic time-dependent behaviors of shale under direct shearing and associated empirical creep models

Understanding the anisotropic creep behaviors of shale under direct shearing is a challenging issue.In this context,we conducted shear-creep and steady-creep tests on shale with five bedding orientations (i.e.0°,30°,45°,60°,and 90°),under multiple levels of direct shearing for the first time.The results show that the anisotropic creep of shale exhibits a significant stress-dependent behavior.Under a low shear stress,the creep compliance of shale increases linearly with the logarithm of time at all bedding orientations,and the increase depends on the bedding orientation and creep time.Under high shear stress conditions,the creep compliance of shale is minimal when the bedding orientation is 0°,and the steady-creep rate of shale increases significantly with increasing bedding orientations of 30°,45°,60°,and 90°.The stress-strain values corresponding to the inception of the accelerated creep stage show an increasing and then decreasing trend with the bedding orientation.A semilogarithmic model that could reflect the stress dependence of the steady-creep rate while considering the hardening and damage process is proposed.The model minimizes the deviation of the calculated steady-state creep rate from the observed value and reveals the behavior of the bedding orientation's influence on the steady-creep rate.The applicability of the five classical empirical creep models is quantitatively evaluated.It shows that the logarithmic model can well explain the experimental creep strain and creep rate,and it can accurately predict long-term shear creep deformation.Based on an improved logarithmic model,the variations in creep parameters with shear stress and bedding orientations are discussed.With abovementioned findings,a mathematical method for constructing an anisotropic shear creep model of shale is proposed,which can characterize the nonlinear dependence of the anisotropic shear creep behavior of shale on the bedding orientation.

Assessment of the three representative empirical models for zenith tropospheric delay(ZTD)using the CMONOC data

The precise correction of atmospheric zenith tropospheric delay(ZTD)is significant for the Global Navigation Satellite System(GNSS)performance regarding positioning accuracy and convergence time.In the past decades,many empirical ZTD models based on whether the gridded or scattered ZTD products have been proposed and widely used in the GNSS positioning applications.But there is no comprehensive evaluation of these models for the whole China region,which features complicated topography and climate.In this study,we completely assess the typical empirical models,the IGGtropSH model(gridded,non-meteorology),the SHAtropE model(scattered,non-meteorology),and the GPT3 model(gridded,meteorology)using the Crustal Movement Observation Network of China(CMONOC)network.In general,the results show that the three models share consistent performance with RMSE/bias of 37.45/1.63,37.13/2.20,and 38.27/1.34 mm for the GPT3,SHAtropE and IGGtropSH model,respectively.However,the models had a distinct performance regarding geographical distribution,elevation,seasonal variations,and daily variation.In the southeastern region of China,RMSE values are around 50 mm,which are much higher than that in the western region,approximately 20 mm.The SHAtropE model exhibits better performance for areas with large variations in elevation.The GPT3 model and the IGGtropSH model are more stable across different months,and the SHAtropE model based on the GNSS data exhibits superior performance across various UTC epochs.

Modeling the Kinetics of Moisture Desorption from Safou Pulp [Dacryodes edulis (G. Don) H.J.Lam] before the Oil Extraction Step

The aim of this work is to model the drying kinetics of Safou pulp with or without endocarp using a phenomenological approach. Oven-drying kinetics at 70˚C, 90˚C and 105˚C were monitored using the curves given by the reduced mass as a function of time, which are modeled according to the Avrami/page, Fick and Peleg models using OringinPro 2018 software. The results showed that parameters k and n of the Avrami/Page model vary very little with fruit size and drying temperature (0.0018 ± 0.0002 k n k (Avrami model/page) were virtually identical, while b (Fick model) and n (Avrami model/page) were virtually identical for the same sample. For the Peleg model, the parameter a, varies from 0.0018 ± 0.0002 to 0.03328 ± 0.0079, with a ratio of 18.6 for all experimental conditions studied. However, with 0.977 R2 χ2 < 0.00002, we have a good fit of the model to the experimental data. The same applies to parameter b, which ranges from 0.82 ± 0.05 to 1.21 ± 0.02. Thus, drying modeling by these three models can be used to describe and predict the progress of oven-drying of safou pulp.

Empirical modeling of ionospheric F2 layer critical frequency over Wakkanai under geomagnetic quiet and disturbed conditions

The hourly values of the ionospheric F2 layer critical frequency, foF2, recorded at Wakkanai ionosonde station （45.4°N, 141.7°E） have been collected to construct a middle-latitude single-station model for forecasting foF2 under geomagnetic quiet and disturbed conditions. The module for the geomagnetic quiet conditions incorporates local time, seasonal, and solar vari- ability of climatological foF2 and its upper and lower quartiles. It is the first attempt to predict the upper and lower quartiles of foF2 to account for the notable day-to-day variability in ionospheric foF2. The validation statistically verifies that the model captures the climatological variations of foF2 with higher accuracy than IRI does. The storm-time module is built to capture the geomagnetic storm induced relative deviations of foF2 from the quiet time references. In the geomagnetically disturbed module, the storm-induced deviations are described by diumal and semidiumal waves, which are modulated by a modified magnetic activity index, the Kf index, reflecting the delayed responses of foF2 to geomagnetic activity forcing. The coeffi- cients of the model in each month are determined by fitting the model formula to the observation in a least-squares way. We provide two options for the geomagnetic disturbed module, including or not including Kalman filter algorithm. The Kalman filter algorithm is introduced to optimize these coefficients in real time. Our results demonstrate that the introduction of the Kalman filter algorithm in the storm time module is promising for improving the accuracy of predication. In addition, comparisons indicate that the IRI model prediction of the F2 layer can be improved to provide better performances over this region.

Evolution and Development of the Information Concept in Biological Systems: From Empirical Description to Informational Modeling of the Living Structures

With the purpose to smooth the way of a correct understanding of information concepts and their evolution,in this paper,is discussed the evolution and development of the concept of information in biological systems,showing that this concept was intuitively perceived even since ancient times by our predecessors,and described according to their language level of that times,but the crystallization of the real meaning of information is an achievement of our nowadays,by successive contribution of various scientific branches and personalities of the scientific community of the world,leading to a modern description/modeling of reality,in which information plays a fundamental role.It is shown that our reality can be understood as a contribution of matter/energy/information and represented/discussed as the model of the Universal Triangle of Reality(UTR),where various previous models can be suggestively inserted,as a function of their basic concern.The modern concepts on information starting from a theoretic experiment which would infringe the thermodynamics laws and reaching the theory of information and modern philosophic concepts on the world structuration allow us to show that information is a fundamental component of the material world and of the biological structures,in correlation with the structuration/destructuration processes of matter,involving absorption/release of information.Based on these concepts,is discussed the functionality of the biologic structures and is presented the informational model of the human body and living structures,as a general model of info-organization on the entire biological scale,showing that a rudimentary proto-consciousness should be operative even at the low-scale biological systems,because they work on the same principles,like the most developed bio-systems.The operability of biologic structures as informational devices is also pointed out.

Observation and modeling on irregular purple soil water infiltration process

The infiltration process is a critical link between surface water and groundwater. In this research, a specific device to observe infiltration processes in homogeneous and heterogeneous soils with triangular and inverted triangular profiles was designed, and the Green-Ampt model was employed for the process simulation. The results indicate that(1) the wetting front in coarse texture soils transports faster than in fine texture soils;(2) for the homogeneous case, the wetting front in triangularshaped soils transports faster than the inverted triangular type, but the triangular-shaped soils show a lower infiltration rate;(3) in the initial step, the wetting front in triangular-shaped soils shows higher transport speed, but depicts lower speed with increase in the time;(4) both the wetting front and infiltration rate show a significant exponential relation with the time. From these findings, an empirical model was developed which agrees well with the observed data and provides a useful method for this field of soil research.

Simulation and Analysis of the Effects of Pressure and Temperature on the Output Voltage of Proton Exchange Membrane Fuel Cells

Proton exchange membrane fuel cell has advantages of high energy conversion efficiency, high reliability, no pollution, low operating temperature and rapid start-up. It has become an ideal method of hydrogen energy utilization and is also ideally suited to be used as the main source of energy for automobiles. Currently, it constitutes a research hot spot in the field of new energy vehicles. Based on the working mechanism of proton exchange membrane fuel cells and empirical models, a terminal voltage dynamic model, an open circuit voltage model and three voltage loss models are established. Matlab/Simulink software is utilized to simulate the model and perform analyses in response to the impact of operating temperature and pressure on its performance. To enhance the efficiency of the proton exchange membrane fuel cell, the operating temperature should be increased in the medium and low current density zones and the operating pressure should be increased in the high current density zone.

A New Empirical Large Signal Model of 4H-SiC MESFETs for the Nonlinear Analysis

A new comprehensive empirical large signal model for 4H-SiC MESFETs is proposed. An enhanced drain current model,along with an improved charge conservation capacitance model,is presented by the improvement of the channel length modulation and the hyperbolic tangent function coefficient based on the Materka model. The Levenberg-Marquardt method is used to optimize the parameter extraction. A comparison of simulation resuits with experimental data is made,and good agreements of I-V curves, Pout （output power）, PAE （power added efficiency） ,and gain at the bias of Vos = 20V, Ips = 80mA as well as the operational frequency of 1.8GHz are obtained.

QUANTITATIVE MODELING OF SUSPENDED SEDIMENT IN MIDDLE CHANGJIANG RIVER FROM MODIS

Remote sensing techniques have been widely used to observe bodies of water. Among satellite sensors commonly used for water quality studies, the Moderate-resolution Imaging Spectroradiometer (MODIS) has potential in water quality monitoring, because of its moderate spatial resolution and high temporal resolution. In this paper, the utility of MODIS data for suspended sediment monitoring in the middle Changjiang (Yangtze) River is studied. It is concluded that suspended sediment concentration correlates well with reflectance values (R1-R2)/(R1+R2) retrieved from MODIS 250m image data (R2=0.72, n=41). Based on this correlation, we obtain the empirical model of suspended sediment concentration in the middle Changjiang River from MODIS. It is shown that it is useful for MODIS data to monitor this parameter of water quality.

An accurate empirical correlation for predicting natural gas viscosity

Natural gas viscosity is an important parameter in many gas and petroleum engineering calculations.This study presents a new empirical model for quickly calculating the natural gas viscosity.The model was derived from 4089 experimental viscosity data with varieties ranging from 0.01 to 21,and 1 to 3 of pseudo reduced pressure and temperature,respectively.The accuracy of this new empirical correlation has been compared with commonly used empirical models,including Lee et al.,Heidaryan et al.,Carr et al.,and Adel Elsharkawy correlations.The comparison indicates that this new empirical model can predict viscosity of natural gas with average absolute relative deviation percentage AARD （%） of 2.173.

Testing and modeling of cyclically loaded rock anchors

The Norwegian Public Roads Administration(NPRA) is planning for an upgrade of the E39 highway route at the westcoast of Norway. Fixed links shall replace ferries at seven fjord crossings. Wide spans and large depths at the crossings combined with challenging subsea topography and environmental loads call for an extension of existing practice. A variety of bridge concepts are evaluated in the feasibility study. The structures will experience significant loads from deadweight, traffic and environment. Anchoring of these forces is thus one of the challenges met in the project. Large-size subsea rock anchors are considered a viable alternative. These can be used for anchoring of floating structures but also with the purpose of increasing capacity of fixed structures. This paper presents first a thorough study of factors affecting rock anchor bond capacity. Laboratory testing of rock anchors subjected to cyclic loading is thereafter presented. Finally, the paper presents a model predicting the capacity of a rock anchor segment, in terms of a ribbed bar, subjected to a cyclic load history. The research assumes a failure mode occurring in the interface between the rock anchor and the surrounding grout. The constitutive behavior of the bonding interface is investigated for anchors subjected to cyclic one-way tensile loads. The model utilizes the static bond capacity curve as a basis, defining the ultimate bond sbuand the slip s1 at τ. A limited number of input parameters are required to apply the model. The model defines the bond-slip behavior with the belonging rock anchor capacity depending on the cyclic load level(τcy/τ), the cyclic load ratio(R= τcy/τcy), and the number of load cycles(N). The constitutive model is intended to model short anchor lengths representing an incremental length of a complete rock anchor.

Science of empirical design in mining ground control

Many problems in rock engineering are limited by our imperfect knowledge of the material properties and failure mechanics of rock masses. Mining problems are somewhat unique, however, in that plenty of real world experience is generally available and can be turned into valuable experimental data.Every pillar that is developed, or stope that is mined, represents a full-scale test of a rock mechanics design. By harvesting these data, and then using the appropriate statistical techniques to interpret them,mining engineers have developed powerful design techniques that are widely used around the world.Successful empirical methods are readily accepted because they are simple, transparent, practical, and firmly tethered to reality. The author has been intimately associated with empirical design for his entire career, but his previous publications have described the application of individual techniques to specific problems. The focus of this paper is the process used to develop a successful empirical method. A sixstage process is described: identification of the problem, and of the end users of the final product; development of a conceptual rock mechanics model, and identification of the key parameters in that model;identification of measures for each of the key parameters, and the development of new measures(such as rating scales) where necessary; data sources and data collection; statistical analysis; and packaging of the final product. Each of these stages has its own potential rewards and pitfalls, which will be illustrated by incidents from the author's own experience. The ultimate goal of this paper is to provide a new and deeper appreciation for empirical techniques, as well as some guidelines and opportunities for future developers.

An accurate empirical correlation for predicting natural gas compressibility factors

The compressibility factor of natural gas is an important parameter in many gas and petroleum engineering calculations. This study presents a new empirical model for quick calculation of natural gas compressibility factors. The model was derived from 5844 experimental data of compressibility factors for a range of pseudo reduced pressures from 0.01 to 15 and pseudo reduced temperatures from 1 to 3. The accuracy of the new empirical correlation has been compared with commonly used existing methods. The comparison indicates the superiority of the new empirical model over the other methods used to calculate compressibility factor of natural gas with average absolute relative deviation percent （AARD%） of 0.6535.

“Refractivity-from-clutter” based on local empirical refractivity model

Constructing sophisticated refractivity models is one of the key problems for the RFC(refractivity from clutter)technology. If prior knowledge of the local refractivity environment is available, more accurate parameterized model can be constructed from the statistical information, which in turn can be used to improve the quality of the local refractivity retrievals. The validity of this proposal was demonstrated by range-dependent refractivity profile inversions using the adjoint parabolic equation method to the Wallops’ 98 experimental data.

Evaluation and Improvement of a SVD-Based Empirical Atmospheric Model

An empirical atmospheric model(EAM) based on the singular value decomposition(SVD) method is evaluated using the composite El Ni(?)o/Southern Oscillation(ENSO) patterns of sea surface temperature (SST) and wind anomalies as the target scenario.Two versions of the SVD-based EAM were presented for comparisons.The first version estimates the wind anomalies in response to SST variations based on modes that were calculated from a pair of global wind and SST fields(i.e.,conventional EAM or CEAM).The second version utilizes the same model design but is based on modes that were calculated in a region-wise manner by separating the tropical domain from the remaining extratropical regions(i.e.,region-wise EAM or REAM). Our study shows that,while CEAM has shown successful model performance over some tropical areas, such as the equatorial eastern Pacific(EEP),the western North Pacific(WNP),and the tropical Indian Ocean(TIO),its performance over the North Pacific(NP) seems poor.When REAM is used to estimate the wind anomalies instead of CEAM,a marked improvement over NP readily emerges.Analyses of coupled modes indicate that such an improvement can be attributed to a much stronger coupled variability captured by the first region-wise SVD mode at higher latitudes compared with that captured by the conventional one. The newly proposed way of constructing the EAM(i.e.,REAM) can be very useful in the coupled studies because it gives the model a wider application beyond the commonly accepted tropical domain.

Modeling of a Demethanizer Tower Using Statistical Tools

Monitoring of industrial plant performance and detection on flaws is important to the successful operation on industrial production units. Malfunctioning equipment can greatly impact plant performance by reducing the efficiency and increasing the production cost. Phenomenological equations cannot properly describe industrial processes. Thus, it is necessary to develop new equations for model industrial operations. The purpose of this study is to develop an empirical model for industrial demethanizer tower which is malfunctioning due to an error in the design in one of its plates. A nonlinear statistical model was designed to predict the pressure variation in the column, and consequently, the flooding conditions. This model was validated using industrial data to predict the maximum loads in the column.

Improvement of the prediction accuracy of polar motion using empirical mode decomposition

Previous studies revealed that the error of pole coordinate prediction will significantly increase for a prediction period longer than 100 days, and this is mainly caused by short period oscillations. Empirical mode decomposition （EMD）, which is increasingly popular and has advantages over classical wavelet decomposition, can be used to remove short period variations from observed time series of pole co- ordinates. A hybrid model combing EMD and extreme learning machine （ELM）, where high frequency signals are removed and processed time series is then modeled and predicted, is summarized in this paper. The prediction performance of the hybrid model is compared with that of the ELM-only method created from original time series. The results show that the proposed hybrid model outperforms the pure ELM method for both short-term and long-term prediction of pole coordinates. The improvement of prediction accuracy up to 360 days in the future is found to be 24.91% and 26.79% on average in terms of mean absolute error （MAE） for the xp and yp components of pole coordinates, respectively.