Quantitative prediction of channel sand bodies based on seismic peak attributes in the frequency domain and its application

The boundary identification and quantitative thickness prediction of channel sand bodies are always difficult in seismic exploration.We present a new method for boundary identification and quantitative thickness prediction of channel sand bodies based on seismic peak attributes in the frequency domain.Using seismic forward modeling of a typical thin channel sand body,a new seismic attribute-the ratio of peak frequency to amplitude was constructed.Theoretical study demonstrated that seismic peak frequency is sensitive to the thickness of the channel sand bodies,while the amplitude attribute is sensitive to the strata lithology.The ratio of the two attributes can highlight the boundaries of the channel sand body.Moreover,the thickness of the thin channel sand bodies can be determined using the relationship between seismic peak frequency and thin layer thickness.Practical applications have demonstrated that the seismic peak frequency attribute can depict the horizontal distribution characteristics of channels very well.The ratio of peak frequency to amplitude attribute can improve the identification ability of channel sand body boundaries.Quantitative prediction and boundary identification of channel sand bodies with seismic peak attributes in the frequency domain are feasible.

Quantitative Prediction for Deep Mineral Exploration

On reviewing the characteristics of deep mineral exploration, this article elaborates on the necessity of employing quantitative prediction to reduce uncertainty. This is caused by complexity of mineral deposit formational environments and mineralization systems as increase of exploration depth and incompleteness of geo-information from limited direct observation. The authors wish to share the idea of ＂seeking difference＂ principle in addition to the ＂similar analogy＂ principle in deep mineral exploration, especially the focus is on the new ores in depth either in an area with discovered shallow mineral deposits or in new areas where there are no sufficient mineral deposit models to be compared. An on-going research project, involving Sn and Cu mineral deposit quantitative prediction in the Gejiu （个旧） area of Yunnan （云南） Province, China, was briefly introduced to demonstrate how the ＂three-component＂ （geoanomaly-mineralization diversity-mineral deposit spectrum） theory and non-linear methods series in conjunction with advanced GIS technology, can be applied in multi-scale and multi-task deep mineral prospecting and quantitative mineral resource assessment.

Formation Dynamics and Quantitative Prediction of Hydrocarbons of the Superpressure System in the Dongying Sag

Based on the theory of formation dynamics of oil/gas pools, the Dongying sag can be divided into three dynamic systems regarding the accumulation of oil and gas： the superpressure closed system, the semi-closed system and the normal pressure open system. Based on the analysis of genesis of superpressure in the superpressure closed system and the rule of hydrocarbon expulsion, it is found that hydrocarbon generation is related to superpressure, which is the main driving factor of hydrocarbon migration. Micro fractures formed by superpressure are the main channels for hydrocarbon migration. There are three dynamic patterns for hydrocarbon expulsion： free water drainage, hydrocarbon accumulation and drainage through micro fissures. In the superpressure closed system, the oil-driving-water process and oil/gas accumulation were completed in lithologic traps by way of such two dynamic patterns as episodic evolution of superpressure systems and episodic pressure release of faults. The oil-bearing capacity of lithologic traps is intimately related to reservoir-forming dynamic force. Quantitative evaluation of dynamic conditions for pool formation can effectively predict the oil-bearing capability of traps.

Quantitative prediction of the bitterness of atomoxetine hydrochloride and taste-masked using hydroxypropyl-β-cyclodextrin:A biosensor evaluation and interaction study

The bitterness of a drug is a major challenge for patient acceptability and compliance,especially for children.Due to the toxicity of medication,a human taste panel test has certain limitations.Atomoxetine hydrochloride(HCl),which is used for the treatment of attention deficit/hyperactivity disorder(ADHD),has an extremely bitter taste.The aim of this work is to quantitatively predict the bitterness of atomoxetine HCl by a biosensor system.Based on the mechanism of detection of the electronic tongue(Etongue),the bitterness of atomoxetine HCl was evaluated,and it was found that its bitterness was similar to that of quinine HCl.The bitterness threshold of atomoxetine HCl was 8.61μg/ml based on the Change of membrane Potential caused by Adsorption(CPA)value of the BT0 sensor.In this study,the taste-masking efficiency of 2-hydroxypropyl-β-cyclodextrin(HP-β-CyD)was assessed by Euclidean distances on a principle component analysis(PCA)map with the SA402B Taste Sensing System,and the host–guest interactions were investigated by differential scanning calorimetry(DSC),powder X-ray diffraction(XRD),nuclear magnetic resonance(NMR)spectroscopy and scanning electron microscopy(SEM).Biosensor evaluation and characterization of the inclusion complex indicated that atomoxetine HCl could actively react with 2-hydroxypropyl-β-cyclodextrin.

Quantitative prediction process and evaluation method for seafloor polymetallic sulfide resources

Seafloor polymetallic sulfide resources exhibit significant development potential. In 2011, China received the exploration rights for 10,000 km2 of a polymetallic sulfides area in the Southwest Indian Ocean; China will be permitted to retain only 25% of the area in 2021. However, an exploration of seafioor hydrothermal sulfide deposits in China remains in the initial stage. According to the quantitative prediction theory and the exploration status of seafloor sulfides, this paper systematically proposes a quantitative prediction evaluation process of oceanic polymetallic sulfide resources and divides it into three stages： prediction in a large area, prediction in the prospecting region, and the verification and evaluation of targets. The first two stages of the prediction process have been employed in seafloor sulfides prospecting of the Chinese contract area. The results of stage one suggest that the Chinese contract area is located in the high posterior probability area, which indicates good prospecting potential area in the Indian Ocean. In stage two, the Chinese contract area of 48^-52~E has the highest posterior probability value, which can be selected as the reserved region for additional exploration. In stage three, the method of numerical simulation is employed to reproduce the ore-forming process of sulfides to verify the accuracy of the reserved targets obtained from the three-stage prediction. By narrowing the exploration area and gradually improving the exploration accuracy, the prediction will provide a basis for the exploration and exploitation of seafloor polymetallic sulfide resources.

Quantitative prediction and ranking of the shock sensitivity ofexplosives via reactive molecular dynamics simulations

A deep understanding of explosive sensitivities and their factors is important for safe and reliable applications.However,quantitative prediction of the sensitivities is difficult.Here,reactive molecular dynamics simulation models for high-speed piston impacts on explosive supercells were established.Simulations were also performed to investigate shock-induced reactions of various high-energy explosives.The fraction of reacted explosive molecules in an initial supercell changed linearly with the propagation distance of the shock-wave front.The corresponding slope could be used as a reaction rate for a specific shock-loading velocity.Reaction rates that varied with the shock-loading pressure exhibited two-stage linearities with different slopes.The two inflection points corresponded to the initial and accelerated reactions,which respectively correlated to the thresholds of shock-induced ignition and detonation.Therefore,the ignition and detonation critical pressures could be determined.The sensitivity could then be a quantitative prediction of the critical pressure.The accuracies of the quantitative shock sensitivity predictions were verified by comparing the impact and shock sensitivities of common explosives and the characteristics of anisotropic shock-induced reactions.Molecular dynamics simulations quantitatively predict and rank shock sensitivities by using only crystal structures of the explosives.Overall,this method will enable the design and safe use of explosives.

Quantitative prediction of mixed-source crude oils and its significance for understanding oil accumulation in subtle pools in the Dongying Depression,Bohai Bay Basin

Conventional geochemical approaches were utilized in the quantitative prediction of the proportions of mixed-source crude oils derived from the Es3 and ES4 members of the Paleogene Shahejie Formation. The mixed-source oils are accumulated in the middle interval of the Es3 member （Es3 M） in the Niuzhuang Sag, which is one of the sags where subtle traps are primarily of lenticular basin-floor turbidite sands within mudstones mostly developed in the Dongying Depression, Bohai Bay Basin. The result showed that about 18-92% of the mixed-source oils were derived from the Es4 source rocks with an average of 55-60%）. Reservoirs associated with deep faults appear to have much more Es4 genetic affinity oils. A high proportion of the Es4-derived oils discovered in the Es3 M subtle lithological traps in the Niuzhuang Sag have long migration distances. This suggests that surrounding source rocks might not necessarily control the hydrocarbon supply for subtle traps. Subtle migration pathway may play an important role in the vertical oil migration. The traditional concept of hydrocarbons accumulation in these tenticular turbidite sandstone traps within a short migration distance from the surrounding source rocks is not supported by this study. The present result is also consistent with our previous findings that immature oils in the Bamianhe Oilfield in the south slope of the Niuzhuang Sag were actually mixed-source oils mostly sourced from the Es4 in the depocenter of the Niuzhuang Sag, and the petroleum potential of the ES4 member in the Dongying Depression should therefore be re-evaluated.

Quantitative Prediction of Concentrated Regions of Large and Superlarge Deposits in China

Identification and quantitative prediction of large and superlarge mineral deposits of solid mineral resources using the mineral resource prediction theory and method with comprehensive information is carried out nationwide in China at a scale of 1∶5 000 000. Using deposit concentrated regions as the model units and concentrated mineralization anomaly regions as prediction units, the prediction is performed on GIS platform. The technical route and research method of locating large and superlarge mineral deposits and principle of compiling attribute table of independent variables and functional variables are proposed. Upon methodology study, the qualitative locating and quantitative predicting mineral deposits are carried out with quantitative theory Ⅲ and characteristic analysis, respectively, and the advantage and disadvantage of two methods are discussed. This research is significant for mineral resource prediction in ten provinces of western China.

On quantitative prediction of mine structure and application of the method

Coal mining activity is often restricted by geologic structural conditions, so it is very important to know the distribution situation of mine structures in advance of mining. For this reason, traditional qualitative procedure must give way to quantitative prediction method backed by mathematics theory and computer technology. This paper explores some relevant problems with the method, introducing a software, MSPS, used to predict automatically and quantitatively the relative complexities of geologic structures in different blocks of a coal mining area, with an application example employing the software to select the most suitable mining sites.

Quantitative analysis and prediction of the sound field convergence zone in mesoscale eddy environment based on data mining methods

The mesoscale eddy(ME)has a significant influence on the convergence effect in deep-sea acoustic propagation.This paper use statistical approaches to express quantitative relationships between the ME conditions and convergence zone(CZ)characteristics.Based on the Gaussian vortex model,we construct various sound propagation scenarios under different eddy conditions,and carry out sound propagation experiments to obtain simulation samples.With a large number of samples,we first adopt the unified regression to set up analytic relationships between eddy conditions and CZ parameters.The sensitivity of eddy indicators to the CZ is quantitatively analyzed.Then,we adopt the machine learning(ML)algorithms to establish prediction models of CZ parameters by exploring the nonlinear relationships between multiple ME indicators and CZ parameters.Through the research,we can express the influence of ME on the CZ quantitatively,and achieve the rapid prediction of CZ parameters in ocean eddies.The prediction accuracy(R)of the CZ distance(mean R:0.9815)is obviously better than that of the CZ width(mean R:0.8728).Among the three ML algorithms,Gradient Boosting Decision Tree has the best prediction ability(root mean square error(RMSE):0.136),followed by Random Forest(RMSE:0.441)and Extreme Learning Machine(RMSE:0.518).

Productivity matching and quantitative prediction of coalbed methane wells based on BP neural network

It is a great challenge to match and predict the production performance of coalbed methane (CBM) wells in the initial production stage due to heterogeneity of coalbed, uniqueness of CBM production process, complexity of porosity-permeability variation and difficulty in obtaining some key parameters which are critical for the conventional prediction methods (type curve, material balance and numerical simulation). BP neural network, a new intelligent technique, is an effective method to deal with nonlinear, instable and complex system problems and predict the short-term change quantitatively. In this paper a BP neural model for the CBM productivity of high-rank CBM wells in Qinshui Basin was established and used to match the past gas production and predict the futural production performance. The results from two case studies showed that this model has high accuracy and good reliability in matching and predicting gas production with different types and different temporal resolutions, and the accuracy increases as the number of outliers in gas production data decreases. Therefore, the BP network can provide a reliable tool to predict the production performance of CBM wells without clear knowledge of coalbed reservoir and sufficient production data in the early development stage.

Quantitative multiparameter prediction of fault-related fractures： a case study of the second member of the Funing Formation in the Jinhu Sag, Subei Basin

In this paper, the analysis of faults with different scales and orientations reveals that the distribution of fractures always develops toward a higher degree of similarity with faults, and a method for calculating the multiscale areal fracture density is proposed using fault-fracture self-similarity theory. Based on the fracture parameters observed in cores and thin sections, the initial apertures of multiscale fractures are determined using the constraint method with a skewed distribution. Through calculations and statistical analyses of in situ stresses in combination with physical experiments on rocks, a numerical geomechanical model of the in situ stress field is established. The fracture opening ability under the in situ stress field is subsequently analyzed. Combining the fracture aperture data and areal fracture density at different scales, a calculation model is proposed for the prediction of multiscale and multiperiod fracture parameters, including the fracture porosity, the magnitude and direction of maximum permeability and the flow conductivity. Finally, based on the relationships among fracture aperture,density, and the relative values of fracture porosity and permeability, a fracture development pattern is determined.

To develop a quantitative method for predicting shrinkage porosity in squeeze casting

In order to secure high strength and high elongation of suspension parts,it is critical to predict shrinkage porosity quantitatively.A new simulation method for quantitative prediction of shrinkage porosity when replenishing molten metal has been proposed for squeeze casting process.To examine the accuracy of the calculation model,the proposed method was applied to a plate model.

Synthetic information prediction system for crisis mine based on GIS

Reserves of some kinds of the crisis mines will be lack now or from now on, because of lacking seriously reserves of mineral resources and the crisis of exploring bases in support. So that it is urgent to predict, appraise, development and utilize the replaceable resources of the crisis mines. The mineral resources prediction software system of synthetic information is intelligent GIS which is used to quantitative prediction of large-scale synthetic information mineral target. It takes the geological body and the mineral resource body as a unit. And it analyzes the ore deposit genesis and metallotect, knows the spatial distribution laws of the ore deposit and ore body, and establish the prospecting model based on the concept of establishing the three-dimensional space of a mine. This paper will primarily discuss some important problems as follows: the secondary development of various kinds of data(including geology, geophysical prospecting, geochemical prospecting and remote sensing, etc); process synthetically and establish the synthetic information interpretative map base; correspond prospecting model with synthetic information of ore deposit; divided into statistical units of metallogenic information synthetic anomalies based on the synthetic information anomalies of ore control, then research the metallogenic information variable of unit synthetically and make quantitative prediction according to choose the quantitative prediction math model which is suitable to the demands of large-scale precision; at last, finish the target area optimization of ore deposit (body).

Quantitative electroencephalography in predicting on outcome of awakening in long-term unconscious patients after severe traumatic brain injury

Objective To explore quantitative electroencephalography in unconscious patients after severe traumatic brain injury (TBI) to predict awakening. Methods All cases were divided into two groups(the awake group 19 cases and the unfavourable prognosis group 22 cases).Two weeks after admission the original EEGs were preformed in 41 patients suffering from severe TBI with duration of disturbance of

Novel mode-coupling vibrations of AlN thin film bulk acoustic resonator operating with thickness-extensional mode

The dispersion curves of bulk waves propagating in both AlN and ZnO film bulk acoustic resonators(FBARs)are presented to illustrate the mode flip of the thickness-extensional(TE)and 2nd thickness-shear(TSh2)modes.The frequency spectrum quantitative prediction(FSQP)method is used to solve the frequency spectra for predicting the coupling strength among the eigen-modes in AlN and ZnO FBARs.The results elaborate that the flip of the TE and TSh2 branches results in novel self-coupling vibration between the small-wavenumber TE and large-wavenumber TE modes,which has never been observed in the ZnO FBAR.Besides,the mode flip leads to the change in the relative positions of the frequency spectral curves about the TE cut-off frequency.The obtained frequency spectra can be used to predict the mode-coupling behaviors of the vibration modes in the AlN FBAR.The conclusions drawn from the results can help to distinguish the desirable operation modes of the AlN FBAR with very weak coupling strength from all vibration modes.

RS and GIS-based Statistical Analysis of Secondary Geological Disasters after the 2008 Wenchuan Earthquake

Using RS and GIS means, this article analyzes the general geological characteristics and the structural belt distribution features in Wenchuan County, Sichuan province, P.R. China as well as the characteristics of the large-scale landslides, mud-rock flows, earthquake lakes, etc., after the earthquake on May 12, 2008. Based on the above work, comprehensive indoor and outdoor research is launched on disaster distribution characteristics and their relationship with earthquakes, terrains, strata, lithology, and structures. Weights of evidence method is utilized to quantitatively analyze and evaluate the spatial distribution of secondary geological disasters after the earthquake occurred. 3 remedying grades for secondary geological disasters are derived from the results of the weights of Evidence, followed by suggestions given to remedy earthquake secondary disasters.

Construction of precise support vector machine based models for predicting promoter strength

Background： The prediction of the prokaryotic promoter strength based on its sequence is of great importance not only in the fundamental research of life sciences but also in the appfied aspect of synthetic biology. Much advance has been made to build quantitative models for strength prediction, especially the introduction of machine learning methods such as artificial neural network （ANN） has significantly improve the prediction accuracy. As one of the most important machine learning methods, support vector machine （SVM） is more powerful to learn knowledge from small sample dataset and thus supposed to work in this problem. Methods： To confirm this, we constructed SVM based models to quantitatively predict the promoter strength. A library of 100 promoter sequences and strength values was randomly divided into two datasets, including a training set （≥10 sequences） for model training and a test set （≥ 10 sequences） for model test. Results： The results indicate that the prediction performance increases with an increase of the size of training set, and the best performance was achieved at the size of 90 sequences. After optimization of the model parameters, a high-performance model was finally trained, with a high squared correlation coefficient for fitting the training set （R^2〉 0.99） and the test set （R^2〉 0.98）, both of which are better than that of ANN obtained by our previous work. Conclusions： Our results demonstrate the SVM-based models can be employed for the quantitative prediction of promoter strength.