Estimation of reservoir porosity using probabilistic neural network and seismic attributes

Porosity is one of the most important properties of oil and gas reservoirs. The porosity data that come from well log are only available at well points. It is necessary to use other method to estimate reservoir porosity.Seismic data contain abundant lithological information. Because there are inherent correlations between reservoir property and seismic data,it is possible to estimate reservoir porosity by using seismic data and attributes.Probabilistic neural network is a powerful tool to extract mathematical relation between two data sets. It has been used to extract the mathematical relation between porosity and seismic attributes. Firstly,a seismic impedance volume is calculated by seismic inversion. Secondly,several appropriate seismic attributes are extracted by using multi-regression analysis. Then a probabilistic neural network model is trained to obtain a mathematical relation between porosity and seismic attributes. Finally,this trained probabilistic neural network model is implemented to calculate a porosity data volume. This methodology could be utilized to find advantageous areas at the early stage of exploration. It is also helpful for the establishment of a reservoir model at the stage of reservoir development.     

Analyses and forecasts of a tornadic supercell outbreak using a 3DVAR system ensemble

As part of NOAA’s "Warn-On-Forecast" initiative, a convective-scale data assimilation and prediction system was developed using the WRF-ARW model and ARPS 3DVAR data assimilation technique. The system was then evaluated using retrospective short-range ensemble analyses and probabilistic forecasts of the tornadic supercell outbreak event that occurred on 24 May 2011 in Oklahoma, USA. A 36-member multi-physics ensemble system provided the initial and boundary conditions for a 3-km convective-scale ensemble system. Radial velocity and reflectivity observations from four WSR-88 Ds were assimilated into the ensemble using the ARPS 3DVAR technique. Five data assimilation and forecast experiments were conducted to evaluate the sensitivity of the system to data assimilation frequencies, in-cloud temperature adjustment schemes, and fixed- and mixed-microphysics ensembles. The results indicated that the experiment with 5-min assimilation frequency quickly built up the storm and produced a more accurate analysis compared with the 10-min assimilation frequency experiment. The predicted vertical vorticity from the moist-adiabatic in-cloud temperature adjustment scheme was larger in magnitude than that from the latent heat scheme. Cycled data assimilation yielded good forecasts, where the ensemble probability of high vertical vorticity matched reasonably well with the observed tornado damage path. Overall, the results of the study suggest that the 3DVAR analysis and forecast system can provide reasonable forecasts of tornadic supercell storms.     

Evaluation of WRF-based convection-permitting multi-physics ensemble forecasts over China for an extreme rainfall event on 21 July 2012 in Beijing

On 21 July 2012, an extreme rainfall event that recorded a maximum rainfall amount over 24 hours of 460 mm, occurred in Beijing, China. Most operational models failed to predict such an extreme amount. In this study, a convective-permitting ensemble forecast system (CEFS), at 4-km grid spacing, covering the entire mainland of China, is applied to this extreme rainfall case. CEFS consists of 22 members and uses multiple physics parameterizations. For the event, the predicted maximum is 415 mm d^-1 in the probability-matched ensemble mean. The predicted high-probability heavy rain region is located in southwest Beijing, as was observed. Ensemble-based verification scores are then investigated. For a small verification domain covering Beijing and its surrounding areas, the precipitation rank histogram of CEFS is much flatter than that of a reference global ensemble. CEFS has a lower (higher) Brier score and a higher resolution than the global ensemble for precipitation, indicating more reliable probabilistic forecasting by CEFS. Additionally, forecasts of different ensemble members are compared and discussed. Most of the extreme rainfall comes from convection in the warm sector east of an approaching cold front. A few members of CEFS successfully reproduce such precipitation, and orographic lift of highly moist low-level flows with a significantly southeasterly component is suggested to have played important roles in producing the initial convection. Comparisons between good and bad forecast members indicate a strong sensitivity of the extreme rainfall to the mesoscale environmental conditions, and, to less of an extent, the model physics.     

Flood forecasting in Niger-Benue basin using satellite and quantitative precipitation forecast data

Availability of reliable, timely and accurate rainfall data is constraining the establishment of flood forecasting and early warning systems in many parts of Africa. We evaluated the potential of satellite and weather forecast data as input to a parsimonious flood forecasting model to provide information for flood early warning in the central part of Nigeria. We calibrated the HEC-HMS rainfall-runoff model using rainfall data from post real time Tropical Rainfall Measuring Mission (TRMM) Multi satellite Precipitation Analysis product (TMPA). Real time TMPA satellite rainfall estimates and European Centre for Medium-Range Weather Forecasts (ECMWF) rainfall products were tested for flood forecasting. The implication of removing the systematic errors of the satellite rainfall estimates (SREs) was explored. Performance of the rainfall-runoff model was assessed using visual inspection of simulated and observed hydrographs and a set of performance indicators. The forecast skill was assessed for 1–6 days lead time using categorical verification statistics such as Probability Of Detection (POD), Frequency Of Hit (FOH) and Frequency Of Miss (FOM). The model performance satisfactorily reproduced the pattern and volume of the observed stream flow hydrograph of Benue River. Overall, our results show that SREs and rainfall forecasts from weather models have great potential to serve as model inputs for real-time flood forecasting in data scarce areas. For these data to receive application in African transboundary basins, we suggest (i) removing their systematic error to further improve flood forecast skill; (ii) improving rainfall forecasts; and (iii) improving data sharing between riparian countries.     

A method to predict typhoon waves

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An Advanced Probabilistic Neural Network for the Design of Breakwater Armor Blocks

In this study,an advanced probabilistic neural network(APNN)method is proposed to reflect the global probability density function(PDF)by summing up the heterogeneous local PDF which is automatically determined in the individual standard deviation of variables.The APNN is applied to predict the stability number of armor blocks of breakwaters using the experimental data of van der Meer,and the estimated results of the APNN are compared with those of an empirical formula and a previous artificial neural network(ANN)model.The APNN shows better results in predicting the stability number of armor blocks of breakwater and it provided the promising probabilistic viewpoints by using the individual standard deviation in a variable.     

Acute ecological risk associated with soot deposition: a Persian Gulf case study

Many studies have been carried out in the past to provide solutions to the threat of chemicals to the ecosystem. However, the basic scientific capability to predict the risk of adverse effects on the ecological system has not kept pace with society's increasing demand for uses of chemicals. As a scientific methodology for quantifying the risk to the environment associated with exposure to chemicals, ecological risk assessment is increasingly important in environmental problem solving. The purpose of this paper is to present a methodology for conducting ecological risk assessment using deterministic and probabilistic approaches. A systematic discussion on elements of ecological risk assessment is presented. A framework of ecological risk assessment is explained with the help of the Persian Gulf environmental problem as a case study. The study was based on the output of a long-range transport model of soot deposition in the Gulf. Results of the assessment using the deterministic and probabilistic approaches are discussed.     

基于风浪联合概率模型的海洋平台结构系统可靠度分析

基于多变量极值理论的联合概率模型,根据BZ28—1油田一年多的风浪同步观测资料统计得到风浪联合概率分布及其参数,讨论了海洋平台结构系统可靠度分析的等效荷载方法,在此基础上分析了JZ20—2MUQ平台结构的系统可靠度,并与不考虑风浪相关性影响的JZ20—2MUQ平台结构的系统可靠度比较,得到了风浪相关性对系统可靠度的影响情况。     

Probabilistic analyses of soil consolidation by prefabricated vertical drains for single‐drain and multi‐drain systems

Natural soils are one of the most inherently variables in the ground. Although the significance of inherent soil variability in relation to reliable predictions of consolidation rates of soil deposits has long been realized, there have been few studies that addressed the issue of soil variability for the problem of ground improvement by prefabricated vertical drains. Despite showing valuable insights into the impact of soil spatial variability on soil consolidation by prefabricated vertical drains, available stochastic works on this subject are based on a single‐drain (or unit cell) analyses. However, how the idealized unit cell solution can be a supplement to the complex multi‐drain systems for spatially variable soils has never been addressed in the literature. In this study, a rigorous stochastic finite elements modeling approach that allows the true nature of soil spatial variability to be considered in a reliable and quantifiable manner, both for the single‐drain and multi‐drain systems, is presented. The feasibility of performing an analysis based on the unit cell concept as compared with the multi‐drain analysis is assessed in a probabilistic context. It is shown that with proper input statistics representative of a particular domain of interest, both the single‐drain and multi‐drain analyses yield almost identical results. Copyright © 2016 John Wiley & Sons, Ltd.