Factors influencing pore-pressure prediction in complex carbonates based on effective medium theory

A calculation model based on effective medium theory has been developed for predicting elastic properties of dry carbonates with complex pore structures by integrating the Kuster-Toksǒz model with a differential method.All types of pores are simultaneously introduced to the composite during the differential iteration process according to the ratio of their volume fractions.Based on this model,the effects of pore structures on predicted pore-pressure in carbonates were analyzed.Calculation results indicate that cracks with low pore aspect ratios lead to pore-pressure overestimation which results in lost circulation and reservoir damage.However,moldic pores and vugs with high pore aspect ratios lead to pore-pressure underestimation which results in well kick and even blowout.The pore-pressure deviation due to cracks and moldic pores increases with an increase in porosity.For carbonates with complex pore structures,adopting conventional pore-pressure prediction methods and casing program designs will expose the well drilling engineering to high uncertainties.Velocity prediction models considering the influence of pore structure need to be built to improve the reliability and accuracy of pore-pressure prediction in carbonates.

An Improved Machine Learning Technique with Effective Heart Disease Prediction System

Heart disease is the leading cause of death worldwide.Predicting heart disease is challenging because it requires substantial experience and knowledge.Several research studies have found that the diagnostic accuracy of heart disease is low.The coronary heart disorder determines the state that influences the heart valves,causing heart disease.Two indications of coronary heart disorder are strep throat with a red persistent skin rash,and a sore throat covered by tonsils or strep throat.This work focuses on a hybrid machine learning algorithm that helps predict heart attacks and arterial stiffness.At first,we achieved the component perception measured by using a hybrid cuckoo search particle swarm optimization(CSPSO)algorithm.With this perception measure,characterization and accuracy were improved,while the execution time of the proposed model was decreased.The CSPSO-deep recurrent neural network algorithm resolved issues that state-of-the-art methods face.Our proposed method offers an illustrative framework that helps predict heart attacks with high accuracy.The proposed technique demonstrates the model accuracy,which reached 0.97 with the applied dataset.

PREDICTION OF THE THERAPEUTIC EFFECTIVENESS OF NEW DRUGS FROM CLINICAL PHARMACOLOGY STUDIES

The development of new drugs for therapeutic purposes has become very expensive and time-consuming in American and European countries.It is estimated that on the average 50 to 100 million dollars and 10 or more years from the time of patenting are required to make a new drug available for general prescription. Every new drug needs to be charac-

Gravitational effect of water circulation in the northwest Yunnan

Water circulation is the main disturbance source against precise gravimetry measurement which is one of the principal means of geodynamic study. Some scientists studied the disturbance of water level changes in lakes andrivers and groundwater activities on gravity field.Taking water circulation as a whole and combining it with thehydrogeological conditions in northwest Yunnan mountainous area and the measured gravity data,this paperstudies the features, connections of water circulation in atmosphere,on surface and under ground and its effecton gravimetric data. The main conclusions are as follows: 1) The water circulation in atmosphere has little directdisturbance on gravity field 1 2 ) The change of lake water level may cause gravitational effect of (10- 20) × 10-8m. s-2 ; 3) In the NW Yunnan, types of the groundwater are various,and its changes are complicated, it isnecessary to study point by point. In general, its disturbance on gravity field in this region is about 10 × 10- 8m. s- 2, less than that in plain area.

Comparison of REML and MINQUE for Estimated Variance Components and Predicted Random Effects

Linear mixed model (LMM) approaches have been widely applied in many areas of research data analysis because they offer great flexibility for different data structures and linear model systems. In this study, emphasis is placed on comparing the properties of two LMM approaches: restricted maximum likelihood (REML) and minimum norm quadratic unbiased estimation (MINQUE) with and without resampling techniques being included. Bias, testing power, Type I error, and computing time were compared between REML and MINQUE approaches with and without Jackknife technique based on 500 simulated data sets. Results showed that MINQUE and REML methods performed equally regarding bias, Type I error, and power. Jackknife-based MINQUE and REML greatly improved power compared to non-Jackknife based linear mixed model approaches. Results also showed that MINQUE is more time-saving compared to REML, especially with the use of resampling techniques and large data set analysis. Results from the actual cotton data analysis were in agreement with our simulated results. Therefore, Jackknife-based MINQUE approaches could be recommended to achieve desirable power with reduced time for a large data analysis and model simulations.

A numerical investigation of hydraulic fracturing on coal seam permeability based on PFC‑COMSOL coupling method

Hydraulic fracturing and permeability enhancement are effective methods to improve low-permeability coal seams.However,few studies focused on methods to increase permeability,and there are no suitable prediction methods for engineering applications.In this work,PFC2D software was used to simulate coal seam hydraulic fracturing.The results were used in a coupled mathematical model of the interaction between coal seam deformation and gas flow.The results show that the displacement and velocity of particles increase in the direction of minimum principal stress,and the cracks propagate in the direction of maximum principal stress.The gas pressure drop rate and permeability increase rate of the fracture model are higher than that of the non-fracture model.Both parameters decrease rapidly with an increase in the drainage time and approach 0.The longer the hydraulic fracturing time,the more complex the fracture network is,and the faster the gas pressure drops.However,the impact of fracturing on the gas drainage effect declines over time.As the fracturing time increases,the difference between the horizontal and vertical permeability increases.However,this difference decreases as the gas drainage time increases.The higher the initial void pressure,the faster the gas pressure drops,and the greater the permeability increase is.However,the influence of the initial void pressure on the permeability declines over time.The research results provide guidance for predicting the anti-reflection effect of hydraulic fracturing in underground coal mines.

Study on eliminating noises in short-period data of geomagnetic intensity by using nu-merical multichannel predictive filtering

The effectiveness of eliminating the noises in short-period data of geomagnetic intensity recorded in a little seismo-geomagnetic array by using numerical multichannel predictive filtering has been studied. The result shows that this technique is effective to fit external magnetic disturbance to inner electromagnetic induced difference field and reduce the noise level of difference data successfully. The filter quality factor Q of two examples in this work are 0. 86 and 0.68 respectively. The spectral analysis shows that during geomagnetic-calm days the fourfold-frequency harmonics of S q in difference data are main components. The length of the optimum filter depends on not only the frequency of predicable energy in difference data but also maybe the phase difference between input and expected output data. It is difficult to obtain the filter fitting both the data during magnetic-disturbed days and calm days. The result shows that the conductivity in Yanqing-Huailai basin west to Beijing may be much non-uniform.

Clustering Gene Expression Data Based on Predicted Differential Effects of GV Interaction

Microarray has become a popular biotechnology in biological and medical research. However, systematic and stochastic variabilities in microarray data are expected and unavoidable, resulting in the problem that the raw measurements have inherent “noise” within microarray experiments. Currently, logarithmic ratios are usually analyzed by various clustering methods directly, which may introduce bias interpretation in identifying groups of genes or samples. In this paper, a statistical method based on mixed model approaches was proposed for microarray data cluster analysis. The underlying rationale of this method is to partition the observed total gene expression level into various variations caused by different factors using an ANOVA model, and to predict the differential effects of GV （gene by variety） interaction using the adjusted unbiased prediction （AUP） method. The predicted GV interaction effects can then be used as the inputs of cluster analysis. We illustrated the application of our method with a gene expression dataset and elucidated the utility of our approach using an external validation.