An improved oil recovery prediction method for volatile oil reservoirs

To describe the complex phase transformation in the process of depletion exploitation of volatile oil reservoir,four fluid phases are defined,and production and remaining volume of these phases are calculated based on the principle of surface volume balance,then the recovery prediction method of volatile oil reservoir considering the influence of condensate content in released solution gas and the correction method of multiple degassing experiments data are established.Taking three typical kinds of crude oil(black oil,medium-weak volatile oil,strong volatile oil)as examples,the new improved method is used to simulate constant volume depletion experiments based on the corrected data of multiple degassing experiment to verify the reliability of the modified method.By using"experimental data and traditional method","corrected data and traditional method"and"corrected data and modified method",recovery factors of these three typical kinds of oil are calculated respectively.The source of parameters and the calculation methods have little effect on the recovery of typical black oil.However,with the increase of crude oil volatility,the oil recovery will be seriously underestimated by using experimental data or traditional method.The combination of"corrected data and modified method"considers the influence of condensate in gas phase in both experimental parameters and calculation method,and has good applicability to typical black oil and volatile oil.The strong shrinkage of volatile oil makes more"liquid oil"convert to"gaseous oil",so volatile oil reservoir can reach very high oil recovery by depletion drive.

A study on the numerical prediction method for the vertical thermal structure in the Bohai Sea and the Huanghai Sea-I.One-dimensional numerical prediction model

In this paper, on the basis of the heat conduction equation without consideration of the advection and turbulence effects, one-dimensional model for describing surface sea temperature ( T1), bottom sea temperature ( Tt ) and the thickness of the upper homogeneous layer ( h ) is developed in terms of the dimensionless temperature θT and depth η and self-simulation function θT - f(η) of vertical temperature profile by means of historical temperature data.The results of trial prediction with our one-dimensional model on T, Th, h , the thickness and gradient of thermocline are satisfactory to some extent.

Comparison of TEC prediction methods in mid-latitudes with GIM maps

There are many long-term and short-term prediction methods of Total Electron Content(TEC) that need to be tested for each specific region. Recently, much attention has been paid to testing TEC models in high-, low-latitude and equatorial regions. This paper compares the TEC prediction methods in the midlatitude zone according to the data of the Juliusruh, Rostov, Manzhouli stations in 2008 and 2015. For a long-term prediction, the IRI-Plas and Ne Quick models are compared with the Global Ionospheric Maps(GIM) presented by the Jet Propulsion Laboratory(JPL) and the Technical University of Catalonia(UPC).For a short-term prediction, the Standard Persistence Model(SPM) method, a 27 day median model, and the proposed short-term prediction method are compared for one day ahead. It is shown that for all stations the IRI-Plas model provides better compliance with GIM maps than the Ne Quick model irrespective of a solar activity level. An average absolute error lays in the range of 3 e3.5 TECU, relative root square mean(RMS) error in the range of 22 e27% in 2015 and 1.7 e2 TECU, 20 e25% in 2008. For the Ne Quick model, these estimates were 6.7 e8.2 TECU and 42 e45% in 2015 and 2.2 e3.6 TECU, 30 e37% in2008. For the short-term forecast, the best results were obtained by the SPM method with an average absolute error in the range of 1.95 e2.15 TECU in 2015 and 0.59 e0.98 TECU in 2008, a relative RMS error in the range of 17 e21% in 2015, 11.5 e15% in 2008. For the proposed short-term prediction method, these errors were 2.04 e2.2 TECU and 12 e14% in 2015 and 0.7 e1.0 TECU, 7 e11% in 2008. Using medians, the errors were 3.1 e3.4 TECU and 17 e21% in 2015 and 1.0 e1.3 TECU, 10 e15% in 2008. The dependence of results on the Dst-index was obtained.

The application of neural networks to comprehensive prediction by seismology prediction method

BP neural networks is used to mid-term earthquake prediction in this paper. Some usual prediction parameters of seismology are used as the import units of neural networks. And the export units of neural networks is called as the character parameter W_0 describing enhancement of seismicity. We applied this method to space scanning of North China. The result shows that the mid-term anomalous zone of W_0-value usually appeared obviously around the future epicenter 1~3 years before earthquake. It is effective to mid-term prediction.

Rockburst in underground excavations:A review of mechanism,classification,and prediction methods

Technical challenges have always been part of underground mining activities,however,some of these challenges grow in complexity as mining occurs in deeper and deeper settings.One such challenge is rock mass stability and the risk of rockburst events.To overcome these challenges,and to limit the risks and impacts of events such as rockbursts,advanced solutions must be developed and best practices implemented.Rockbursts are common in underground mines and substantially threaten the safety of personnel and equipment,and can cause major disruptions in mine development and operations.Rockbursts consist of violent wall rock failures associated with high energy rock projections in response to the instantaneous stress release in rock mass under high strain conditions.Therefore,it is necessary to develop a good understanding of the conditions and mechanisms leading to a rockburst,and to improve risk assessment methods.The capacity to properly estimate the risks of rockburst occurrence is essential in underground operations.However,a limited number of studies have examined and compared yet different empirical methods of rockburst.The current understanding of this important hazard in the mining industry is summarized in this paper to provide the necessary perspective or tools to best assess the risks of rockburst occurrence in deep mines.The various classifications of rockbursts and their mechanisms are discussed.The paper also reviews the current empirical methods of rockburst prediction,which are mostly dependent on geomechanical parameters of the rock such as uniaxial compressive strength of the rock,as well as its tensile strength and elasticity modulus.At the end of this paper,some current achievements and limitations of empirical methods are discussed.

Prediction method for nutritional quality of Korla pear during storage

It is difficult to control the quality of Korla pear with different degrees of maturity during storage.Here,a method was proposed for predicting the effects of harvest maturity and cold storage time on the quality indices(soluble solid content(SSC)and Vitamin C(Vc)content)of Korla pear.The generalized regression neural network(GRNN)and adaptive neuro-fuzzy inference system(ANFIS)were employed to predict the quality changes of Korla fragrant pear fruit during storage.The results demonstrated that during cold storage the SSC in pears with 10%-70%harvest maturity showed continuous increases in the first 90 d of storage and then a slight decline thereafter,while that in pears with 80%and 90%harvest maturity exhibited slow decreases throughout the storage process.With the extension of storage time,the Vc content of pears with 10%-90%harvest maturity showed continuous decreases.The harvest maturity of Korla pear was extremely positively correlated with SSC and Vc content(p<0.01)in a given storage period.Storage time showed an extremely significant negative correlation with the Vc content(p<0.01)at the 40%-90%harvest maturity and an significant negative correlation with the Vc content(p<0.05)at the 10%-30%harvest maturity.At the 10%-70%harvest maturity,storage time showed a significant positive correlation with the SSC(p<0.05).The trained model could well predict the variation trend of quality indices of pear fruit during storage.The ANFIS with the input membership function of gbellmf had the best performance in predicting the SSC(RMSE=0.175;R2=0.98),and that with the input membership function of trimf exhibited the best performance in predicting Vc content(RMSE=0.075;R2=0.99).The research findings can provide reference for predicting the fruit nutritional quality at delivery and decision-making on the storage time of Korla fragrant pear.

Prediction Method for Regional Logistics

Currently applied prediction methods of regional freight traffic and freight ton-kilometer forecasting were analyzed using typical Chinese regional goods transportation characteristics. The review of prediction methods showes that practical planning experts tend to apply the traditional methods which are easier to implement. The comparison also demonstrates that a combination of traditional methods is more effective than the simple models for practical planning. Research using the statistical data for the Yangtze Delta, Pearl River Delta, and Bohai Rim areas shows that ignoring differences between transport modes impacts the prediction accuracy. The four main transport modes suit different methods. The results show that the power model is better for railways, and the linear model is better for highways and waterways. Thus a combined model gives better results for all modes. The results for regional systems can be generalized to national transportation systems.

An Accurate Common-Mode Current Prediction Method for Transformerless Photovoltaic Inverters Based on Graph Theory

The common-mode current is an important indicator with transformerless photovoltaic inverters.However,up to now,there is not an accurate method to predict common-mode current in the inverter design process,resulting from inappropriate device selection or exceeded the expected common-mode current.In order to solve this problem,this paper proposes an accurate common-mode current prediction method based on graph theory for transformerless photovoltaic inverters.In this paper,the mathematic model of the common-mode current is derived using graph theory analysis method in the full-bridge topology,and it is used to predict common-mode current.The validity and correctness of the proposed prediction method are validated by simulation and experiment.The oscillation frequency and amplitude can be predicted by the proposed common-mode prediction method,whereas the traditional common-mode analysis method cannot.This paper provides a novel way to predict and analyze common-mode current in the transformerless photovoltaic inverters.

A new prediction method at the edge of optical burst switching network

To achieve lower assembly delay at optical burst switching edge node, this paper proposes an approach called current weight length prediction (CWLP) to improve existing estimate mechanism in burst assembly. CWLP method takes into account the arrived traffic in prediction time adequately. A parameter 'weight' is introduced to make a dynamic tradeoff between the current and past traffic under different offset time. Simulation results show that CWLP can achieve a significant improvement in terms of traffic estimation in various offset time and offered load.

A method for predicting the water-flowing fractured zone height based on an improved key stratum theory

In the process of using the original key stratum theory to predict the height of a water-flowing fractured zone(WFZ),the influence of rock strata outside the calculation range on the rock strata within the calculation range as well as the fact that the shape of the overburden deformation area will change with the excavation length are ignored.In this paper,an improved key stratum theory(IKS theory)was proposed by fixing these two shortcomings.Then,a WFZ height prediction method based on IKS theory was established and applied.First,the range of overburden involved in the analysis was determined according to the tensile stress distribution range above the goaf.Second,the key stratum in the overburden involved in the analysis was identified through IKS theory.Finally,the tendency of the WFZ to develop upward was determined by judging whether or not the identified key stratum will break.The proposed method was applied and verified in a mining case study,and the reasons for the differences in the development patterns between the WFZs in coalfields in Northwest and East China were also fully explained by this method.

Interval finite difference method for steady-state temperature field prediction with interval parameters

A new numerical technique named interval finite difference method is proposed for the steady-state temperature field prediction with uncertainties in both physical parameters and boundary conditions. Interval variables are used to quantitatively describe the uncertain parameters with limited information. Based on different Taylor and Neumann series, two kinds of parameter perturbation methods are presented to approximately yield the ranges of the uncertain temperature field. By comparing the results with traditional Monte Carlo simulation, a numerical example is given to demonstrate the feasibility and effectiveness of the proposed method for solving steady-state heat conduction problem with uncertain-but-bounded parameters.

IMPROVED METHOD FOR RNA SECONDARY STRUCTURE PREDICTION'

A simple stepwise folding process has been developed to simulate RNA secondary structure formation.Modifications for the energy parameters of various loops were included in the program.Five possible types of pseudoknots including the well known H-type pseudoknot were permitted to occur if reasonable.We have applied this approach to e number of RNA sequences.The prediction accuracies we obtained were higher than those in published papers.

Genomic selection of eight fruit traits in pear

Genomic selection (GS) has the potential to improve selection efficiency and shorten the breeding cycle in fruit tree breeding. In this study,we evaluated the effect of prediction methods, marker density and the training population (TP) size on pear GS for improving its performance and reducing cost. We evaluated GS under two scenarios:(1) five-fold cross-validation in an interspecific pear family;(2) independent validation. Based on the cross-validation scheme, the prediction accuracy (PA) of eight fruit traits varied between 0.33 (fruit core vertical diameter)and 0.65 (stone cell content). Except for single fruit weight, a slightly better prediction accuracy (PA) was observed for the five parametrical methods compared with the two non-parametrical methods. In our TP of 310 individuals, 2 000 single nucleotide polymorphism (SNP) markers were sufficient to make reasonably accurate predictions. PAs for different traits increased by 18.21%-46.98%when the TP size increased from 50to 100, but the increment was smaller (-4.13%-33.91%) when the TP size increased from 200 to 250. For independent validation, the PAs ranged from 0.11 to 0.45 using rrBLUP method. In summary, our results showed that the TP size and SNP numbers had a greater impact on the PA than prediction methods. Furthermore, relatedness among the training and validation sets, and the complexity of traits should be considered when designing a TP to predict the test panel.

Accident and hazard prediction models for highway–rail grade crossings:a state-of-the-practice review for the USA

Highway–rail grade crossings(HRGCs)are one of the most dangerous segments of the transportation network.Every year numerous accidents are recorded at HRGCs between highway users and trains,between highway users and traffic control devices,and solely between highway users.These accidents cause fatalities,severe injuries,property damage,and release of hazardous materials.Researchers and state Departments of Transportation(DOTs)have addressed safety concerns at HRGCs in the USA by investigating the factors that may cause accidents at HRGCs and developed certain accident and hazard prediction models to forecast the occurrence of accidents and crossing vulnerability.The accident and hazard prediction models are used to identify the most hazardous HRGCs that require safety improvements.This study provides an extensive review of the state-of-the-practice to identify the existing accident and hazard prediction formulae that have been used over the years by different state DOTs.Furthermore,this study analyzes the common factors that have been considered in the existing accident and hazard prediction formulae.The reported performance and implementation challenges of the identified accident and hazard prediction formulae are discussed in this study as well.Based on the review results,the US DOT Accident Prediction Formula was found to be the most commonly used formula due to its accuracy in predicting the number of accidents at HRGCs.However,certain states still prefer customized models due to some practical considerations.Data availability and data accuracy were identified as some of the key model implementation challenges in many states across the country.

Prediction of DC Corona Onset Voltage for Rod-Plane Air Gaps by a Support Vector Machine

This paper proposes a new method to predict the corona onset voltage for a rod- plane air gap, based on the support vector machine （SVM）. Because the SVM is not limited by the size, dimension and nonlinearity of the samples, this method can realize accurate prediction with few training data. Only electric field features are chosen as the input; no geometric parameter is included. Therefore, the experiment data of one kind of electrode can be used to predict the corona onset voltages of other electrodes with different sizes. With the experimental data obtained by ozone detection technology, and experimental data provided by the reference, the efficiency of the proposed method is validated. Accurate predicted results with an average relative less than 3% are obtained with only 6 experimental data.

AN EXTENSION OF THE METHOD FOR PREDICTING PERMEABILITY THROUGH POLYMER MEMBRANES FROM SIMPLE GASES TO WATER VAPOR

It is found that there is a linear relationship between log P-w, and the parameter term V-f/0.5 E(coh) [1+(delta(w) - delta(p))(2)/delta(p)(2), from the water permeability (P-w) data of 21 polymers covering 4 orders of magnitude. This correlation may be useful in choosing membrane materials for dehumidification of gases.

A NEW METHOD FOR INCREASING PRECISION IN SURVEY SAMPLING

This paper proposes a new method for increasing the precision in survey sam- pling, i.e., a method combining sampling with prediction. The two cases where auxiliary information is or not available are considered. A numerical example is given.

Method of Predicting Water Content in Crude Oil Based on Measuring Range Automatic Switching

Water content in output crude oil is hard to measure precisely because of wide range of dielectric coefficient of crude oil caused by injected dehydrating and demulsifying agents.The method to reduce measurement error of water content in crude oil proposed in this paper is based on switching measuring ranges of on-line water content analyzer automatically.Measuring precision on data collected from oil field and analyzed by in-field operators can be impressively improved by using back propogation （BP） neural network to predict water content in output crude oil.Application results show that the difficulty in accurately measuring water-oil content ratio can be solved effectively through this combination of on-line measuring range automatic switching and real time prediction,as this method has been tested repeatedly on-site in oil fields with satisfactory prediction results.

Bitcoin price change and trend prediction through twitter sentiment and data volume

Twitter sentiment has been shown to be useful in predicting whether Bitcoin’s price will increase or decrease.Yet the state-of-the-art is limited to predicting the price direction and not the magnitude of increase/decrease.In this paper,we seek to build on the state-of-the-art to not only predict the direction yet to also predict the magnitude of increase/decrease.We utilise not only sentiment extracted from tweets,but also the volume of tweets.We present results from experiments exploring the relation between sentiment and future price at different temporal granularities,with the goal of discovering the optimal time interval at which the sentiment expressed becomes a reliable indicator of price change.Two different neural network models are explored and evaluated,one based on recurrent nets and one based on convolutional networks.An additional model is presented to predict the magnitude of change,which is framed as a multi-class classification problem.It is shown that this model yields more reliable predictions when used alongside a price trend prediction model.The main research contribution from this paper is that we demonstrate that not only can price direction prediction be made but the magnitude in price change can be predicted with relative accuracy(63%).

PREDICTION OF FATIGUE LIVES OF RC BEAMS STRENGTHENED WITH CFL UNDER RANDOM LOADING

The investigation on fatigue lives of reinforced concrete （RC） structures strength- ened with fiber laminate under random loading is important for the repairing or the strengthening of bridges and the safety of the traffic. In this paper, two methods are developed for predicting the fatigue lives of RC structures strengthened with carbon fiber [aminate （CFL） under random loading based on a residual life and a residual strength model. To discuss the efficiency of the model, 12 RC beams strengthened with CFL are tested under random loading by the MTS810 testing system. The predicted residual strength approximately agrees with test results.