一种新的油气藏预测方法—概模法

提出新的储层分类和油气藏预测方法－概模法。介绍用该法对莺歌海盆地LD15－1气田的分布范围及CO2含量变化的描述分析和二连盆地阿尔善油田的反射特征异常解释。

RECURSIVE FILTERING RADON-AMBIGUITY TRANSFORM ALGORITHM FOR DETECTING MULTI-LFM SIGNALS

In multi-LFM signal condition, Radon-Ambiguity Transform (RAT) of the strong LFM component has strong suppression effect on that of the weak LFM component. A method named as Recursive Filtering RAT (RFRAT) algorithm is proposed for solving this problem. By fully using of the Maximum Likelihood (ML) estimation value of the frequency modulation rate got by RAT, RFRAT can detect the noisy multi-LFM signals out step by step. The merit of this new method is validated by an illustrative example in low Signal-to-Noise-Ratio (SNR) condition.

Numerical method of slope failure probability based on Bishop model

Based on Bishop's model and by applying the first and second order mean deviations method, an approximative solution method for the first and second order partial derivatives of functional function was deduced according to numerical analysis theory. After complicated multi-independent variables implicit functional function was simplified to be a single independent variable implicit function and rule of calculating derivative for composite function was combined with principle of the mean deviations method, an approximative solution format of implicit functional function was established through Taylor expansion series and iterative solution approach of reliability degree index was given synchronously. An engineering example was analyzed by the method. The result shows its absolute error is only 0.78% as compared with accurate solution.

Pattern recognition prediction of coal and gas outburst hazard in the sixth mine of Hebi

Based on the systematical analysis influence factors of coal and gas outburst, the main factors and their magnitude was determined by the corresponding methods.With the research region divided into finite predicting units,the internal relation between the factors and the hazard of coal and gas outburst,that was combination model of influence factors,was ascertained through multi-factor pattern recognition method.On the basis of contrastive analysis the pattern of coal and gas outburst between prediction region and mined region,the hazard of every predication unit was determined.The mining area was then divided into coal and gas outburst dangerous area,threaten area and safe area re- spectively according to the hazard of every predication unit.Accordingly the hazard of mining area is assessed.

Auto Identification of Rolling Layers in Roller-Compacted Concrete Dam

A scheme for identifying rolling layers in roller-compacted concrete (RCC) dam automatically was presented. First, a conceptual model was developed. Second, by using a computational geometry method, the auto identification of rolling layers and auto matching between rolling compaction machines and rolling layers were realized based on spatial control points. An application to the construction of Guandi RCC dam showed that the auto identification of rolling layers played an important role in ensuring the engineering quality.

FUZZY REASONING IN CYCLES

By the similarity between the syllogism in logic and a path proposition in graph theory,a new concept,fuzzy reasoning graph G has been given in this paper. Transitive closure has been studied and used to do reasoning related to self-loop in G,and an algorithm has been designed to cope with reasoning in other cycles in G. Both approaches are applicable and efficient.

Adaptive Local Outlier Probability for Dynamic Process Monitoring

Complex industrial processes often have multiple operating modes and present time-varying behavior. The data in one mode may follow specific Gaussian or non-Gaussian distributions. In this paper, a numerically efficient movingwindow local outlier probability algorithm is proposed, lies key feature is the capability to handle complex data distributions and incursive operating condition changes including slow dynamic variations and instant mode shifts. First, a two-step adaption approach is introduced and some designed updating rules are applied to keep the monitoring model up-to-date. Then, a semi-supervised monitoring strategy is developed with an updating switch rule to deal with mode changes. Based on local probability models, the algorithm has a superior ability in detecting faulty conditions and fast adapting to slow variations and new operating modes. Finally, the utility of the proposed method is demonstrated with a numerical example and a non-isothermal continuous stirred tank reactor.

Reliability analysis of laminated composite under compression and shear loads

Carrying on a series of compression and shear tests by a large number of specimens, reliabilities of T300/QY8911 laminated composite were studied when dispersibility models were described. The results show that the stress is linearly dependent on the strain and the damage modes of specimens are brittle fracture for both kinds of tests. Dispersibility models of compression and shear strength are expressed as Re-N（415.39, 6 586.36） and Rs-ln（5.071 8, 0.155 3）, respectively. When normal and lognormal distributions were used to describe the dispersibility models of compression and shear strength, and the compression or shear load follows the normal distribution, the almost same failure probability can be obtained from different reliability analysis methods.

A New Proposal to Adjust a Straight Line to a Normal Q-Q Plot

In this paper a new proposal of a straight line, the ＂modified Tukey＇s line＂, for fitting to a normal quantile-quantile Plot, or normal Q-Q plot, is presented. This probability plot allows us to determine whether a set of sample observations is distributed according to a normal distribution. For this, the sample quantiles are represented against the quantiles of a theoretical probability model, which in this case is the normal distribution. If the data set follows the above mentioned distribution, the plotted points will have a rectilinear configuration. To verify this, there are different alternatives for fitting a straight line to the plotted points. Among the straight lines which can be fitted to a Q-Q plot, in this paper, besides the proposed straight line, the following straight lines are considered： straight line that passes through the first and third quartiles, straight line that passes through the 10th and 90th percentiles, straight line fitted by the method of least squares, straight line with slope s and constant the average of the data set, Theil＇s line and Tukey＇s line. In addition, an example, in which there are represented the different straight lines considered and the proposed straight line on a normal Q-Q plot obtained for the same set of observations, is developed. In this example the existing differences among the straight lines are observed.

Probabilistic seismic stability of three-dimensional slopes by pseudo-dynamic approach

Probabilistic analysis is a rational approach for engineering design because it provides more insight than traditional deterministic analysis. Probabilistic evaluation on seismic stability of three dimensional (3D) slopes is studied in this paper. The slope safety factor is computed by combining the kinematic approach of limit analysis using a three-dimensional rotational failure mechanism with the pseudo-dynamic approach. The variability of input parameters, including six pseudo-dynamic parameters and two soil shear strength parameters, are taken into account by means of Monte-Carlo Simulations (MCS) method. The influences of pseudo-dynamic input variables on the computed failure probabilities are investigated and discussed. It is shown that the obtained failure probabilities increase with the pseudo-dynamic input variables and the pseudo-dynamic approach gives more conservative failure probability estimates compared with the pseudo-static approach.

Nash Model Parameter Uncertainty Analysis by AM-MCMC Based on BFS and Probabilistic Flood Forecasting

A hydrologic model consists of several parameters which are usually calibrated based on observed hy-drologic processes. Due to the uncertainty of the hydrologic processes, model parameters are also uncertain, which further leads to the uncertainty of forecast results of a hydrologic model. Working with the Bayesian Forecasting System (BFS), Markov Chain Monte Carlo simulation based Adaptive Metropolis method (AM-MCMC) was used to study parameter uncertainty of Nash model, while the probabilistic flood forecasting was made with the simu-lated samples of parameters of Nash model. The results of a case study shows that the AM-MCMC based on BFS proposed in this paper is suitable to obtain the posterior distribution of the parameters of Nash model according to the known information of the parameters. The use of Nash model and AM-MCMC based on BFS was able to make the probabilistic flood forecast as well as to find the mean and variance of flood discharge, which may be useful to estimate the risk of flood control decision.

Dynamic alarm prediction for critical alarms using a probabilistic model

Alarm systems play important roles for the safe and efficient operation of modern industrial plants. Critical alarms are configured with a higher priority and are safety related among many other alarms. If critical alarms can be predicted in advance, the operator will have more time to prevent them from happening. In this paper,we present a dynamic alarm prediction algorithm, which is a probabilistic model that utilizes alarm data from distributed control system, to calculate the occurrence probability of critical alarms. It accounts for the local interdependences among the alarms using the n-gram model, which occur because of the nonlinear relationships between variables. Finally, the dynamic alarm prediction algorithm is applied to an industrial case study.

Nonlinear finite-element-based structural system failure probability analysis methodology for gravity dams considering correlated failure modes

The structural system failure probability(SFP) is a valuable tool for evaluating the global safety level of concrete gravity dams.Traditional methods for estimating the failure probabilities are based on defined mathematical descriptions,namely,limit state functions of failure modes.Several problems are to be solved in the use of traditional methods for gravity dams.One is how to define the limit state function really reflecting the mechanical mechanism of the failure mode;another is how to understand the relationship among failure modes and enable the probability of the whole structure to be determined.Performing SFP analysis for a gravity dam system is a challenging task.This work proposes a novel nonlinear finite-element-based SFP analysis method for gravity dams.Firstly,reasonable nonlinear constitutive modes for dam concrete,concrete/rock interface and rock foundation are respectively introduced according to corresponding mechanical mechanisms.Meanwhile the response surface(RS) method is used to model limit state functions of main failure modes through the Monte Carlo(MC) simulation results of the dam-interface-foundation interaction finite element(FE) analysis.Secondly,a numerical SFP method is studied to compute the probabilities of several failure modes efficiently by simple matrix integration operations.Then,the nonlinear FE-based SFP analysis methodology for gravity dams considering correlated failure modes with the additional sensitivity analysis is proposed.Finally,a comprehensive computational platform for interfacing the proposed method with the open source FE code Code Aster is developed via a freely available MATLAB software tool(FERUM).This methodology is demonstrated by a case study of an existing gravity dam analysis,in which the dominant failure modes are identified,and the corresponding performance functions are established.Then,the dam failure probability of the structural system is obtained by the proposed method considering the correlation relationship of main failure modes on the basis of the mechanical mechanism analysis with the MC-FE simulations.

SVM model for estimating the parameters of the probability-integral method of predicting mining subsidence

A new mathematical model to estimate the parameters of the probability-integral method for mining subsidence prediction is proposed.Based on least squares support vector machine(LS-SVM) theory, it is capable of improving the precision and reliability of mining subsidence prediction.Many of the geological and mining factors involved are related in a nonlinear way.The new model is based on statistical theory(SLT) and empirical risk minimization(ERM) principles.Typical data collected from observation stations were used for the learning and training samples.The calculated results from the LS-SVM model were compared with the prediction results of a back propagation neural network(BPNN) model.The results show that the parameters were more precisely predicted by the LS-SVM model than by the BPNN model.The LS-SVM model was faster in computation and had better generalized performance.It provides a highly effective method for calculating the predicting parameters of the probability-integral method.

Gas drainage efficiency improvement by increasing coalbed permeability using waterjet cutting system

This paper presented a method to create artificial fractures along the existing gas drainage borehole and increase the permeability of the coalbed using a high pressure waterjet cutting system.The field work conducted in Rujigou Colliery, Shenhua Ningxia Coal Group demonstrate that the coalbed permeability is increased, and accordingly, gas drainage efficiency is improved up to 3 to 6 times over the traditional methods using high pressure waterjet technique.Also, based on the monitoring data, the conceptual model for gas drainage process associated with different mining activities has been proposed, and few major advantages using waterjet assistance method have been identified.

Hybrid reliability model for fatigue reliability analysis of steel bridges

A kind of hybrid reliability model is presented to solve the fatigue reliability problems of steel bridges. The cumulative damage model is one kind of the models used in fatigue reliability analysis. The parameter characteristics of the model can be described as probabilistic and interval. The two-stage hybrid reliability model is given with a theoretical foundation and a solving algorithm to solve the hybrid reliability problems. The theoretical foundation is established by the consistency relationships of interval reliability model and probability reliability model with normally distributed variables in theory. The solving process is combined with the definition of interval reliability index and the probabilistic algorithm. With the consideration of the parameter characteristics of the S-N curve, the cumulative damage model with hybrid variables is given based on the standards from different countries. Lastly, a case of steel structure in the Neville Island Bridge is analyzed to verify the applicability of the hybrid reliability model in fatigue reliability analysis based on the AASHTO.

Particle swarm optimization and its application to seismic inversion of igneous rocks

In order to improve the fine structure inversion ability of igneous rocks for the exploration of underlying strata, based on particle swarm optimization(PSO), we have developed a method for seismic wave impedance inversion. Through numerical simulation, we tested the effects of different algorithm parameters and different model parameterization methods on PSO wave impedance inversion, and analyzed the characteristics of PSO method. Under the conclusions drawn from numerical simulation, we propose the scheme of combining a cross-moving strategy based on a divided block model and high-frequency filtering technology for PSO inversion. By analyzing the inversion results of a wedge model of a pitchout coal seam and a coal coking model with igneous rock intrusion, we discuss the vertical and horizontal resolution, stability and reliability of PSO inversion. Based on the actual seismic and logging data from an igneous area, by taking a seismic profile through wells as an example, we discuss the characteristics of three inversion methods, including model-based wave impedance inversion, multi-attribute seismic inversion based on probabilistic neural network(PNN) and wave impedance inversion based on PSO.And we draw the conclusion that the inversion based on PSO method has a better result for this igneous area.

Non-deterministic fatigue life analysis using convex set models

The non-probabilistic approach to fatigue life analysis was studied using the convex models-interval, ellipsoidal and multiconvex models. The lower and upper bounds of the fatigue life were obtained by using the second-order Taylor series and Lagrange multiplier method. The solving process for derivatives of the implicit life function was presented. Moreover, a median ellipsoidal model was proposed which can take into account the sample blind zone and almost impossibility of concurrence of some small probability events. The Monte Carlo method for multi-convex model was presented, an important alternative when the analytical method does not work. A project example was given. The feasibility and rationality of the presented approach were verified. It is also revealed that the proposed method is conservative compared to the traditional probabilistic method, but it is a useful complement when it is difficult to obtain the accurate probability densities of parameters.

Path planning for taxi of carrier aircraft launching

Launching efficiency is an important index to measure the fighting capacity of an aircraft carrier. The study on path planning for taxi of carrier aircraft is of great significance for enhancing the launching efficiency. Considering the launching efficiency and the safety in operation of carrier aircraft launching and taking into account the carrier aircraft maneuver performance, deck environment and feature of mission, we proposed a conceptual model which contains the key elements of path planning for taxi of carrier aircraft. Subsequently, the objective function for the path planning problem and its mathematical model containing various constraints were established. With the A * search algorithm, a dynamic weight heuristic function was designed. According to the characteristic of path planning model for taxi of carrier aircraft, a simple and effective detection method was introduced. Finally, a feasible path for taxi of carrier aircraft, which meets the constraints, was presented. Taking the Nimitz-class aircraft carrier as an example, the paths for taxi of carrier aircraft launching from elevators to catapults were planned. Simulation results demonstrated the rationality of the model and the effectiveness of the algorithm.

A random medium model for simulation of concrete failure

A random medium model is developed to describe damage and failure of concrete.In the first place,to simulate the evolving cracks in a mesoscale,the concrete is randomly discretized as irregular finite elements.Moreover,the cohesive elements are inserted into the adjacency of finite elements as the possible cracking paths.The spatial variation of the material properties is considered using a 2-D random field,and the stochastic harmonic function method is adopted to simulate the sample of the fracture energy random field in the analysis.Then,the simulations of concrete specimens are given to describe the different failure modes of concrete under tension.Finally,based on the simulating results,the probability density distributions of the stress-strain curves are solved by the probability density evolution methods.Thus,the accuracy and efficiency of the proposed model are verified in both the sample level and collection level.