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Lithofacies and their relationships, textural content, sedimentary structures were used to delineate eight facies associations in the Benin west-1. Seven lithofacies are recognized; coarse grained sandstone with granules, laminated shaley sandstone, shaley siltstone, laminated shaley siltstone with fossil fragment, laminated shale, siltstone, coarse grained sandstone and micaceous sandstone facies. Each lithofacies is interpreted to represent different or similar depositional environments. Sediments of the Ameki Formation and Imo Shale encountered in the Benin west-1 were deposited in distributary channel and prodelta to marine shelf environments

The present work utilizes airborne gamma ray spectrometric data in a trial to refine surface geology of igneous, metamorphic and sedimentary rocks, detect any radioactive mineralization at Gabel Umm Tineidba area South Eastern Desert, Egypt. The study area is covered by rock exposures ranging in age from the Precambrian to Quaternary. Airborne gamma ray spectrometry can be very helpful in mapping surface geology. This provides estimates of the apparent surface concentrations of the most common naturally occurring radioactive elements, such as potassium (K), equivalent uranium (eU) and equivalent thorium (eTh). This is based on the assumption that, the absolute and relative concentrations of these radioelements vary measurably and significantly with lithology. The composite image technique is used to display simultaneously three parameters of the three radioelement concentrations and their three binary ratios on one image. The technique offers much in terms of lithological discrimination, based on color differences and showed efficiency in defining areas, where different lithofacies occur within areas mapped as one continuous lithology. The integration between surface geological information and geophysical data led to detailing the surface geology and the contacts between different rock units. Significant locations or favourable areas for uranium exploration are defined, where the measurements exceed (X+2S), taking X as the arithmetic mean of eU, eU/eTh and eU/K measurements and S as the standard deviation corresponding to each variables. The study area shows the presence of fifteen relatively high uraniferous zone. In addition, the trend analysis based on the total count map and the published geological map shows that, most of the well-developed structural lineaments have NS, ENE, NNE and NNW trends.

Based on core description, gas logging and laboratory analysis, this paper aims to study the controlling effect of the types of shale sedimentary microfacies in coal formations over shale reservoirs using the example of Shanxi formation in Northern Ordos Basin.

According to core observation, the authors selected typical samples of rock types for thin section analysis to determine the micro features and compositions of rocks.

By using core observation, we found that fine lithology in Shanxi formation included major shale, carbonaceous shale, partially carbonaceous shale, partially silty shale and silty shale with colors of gray, dark gray, black and/or gray. Shanxi Formation shale are deposited in plant-rich and plant-poor swamps, interdistributary depressions of delta plains, interfluvial depressions of meandering rivers as well as microfacies environment of natural levees and the distal crevasse splay.

Currently, the research on the shale gas in Shanxi Formation in the Ordos Basin is still in its infancy. There is yet no research on the fine-grained partition of the sedimentary facies in coal accumulation environment of Shanxi formation and the controlling effect of sedimentary microfacies over shale reservoirs.

Background: Geostatistics focuses on spatial or spatiotemporal datasets. Geostatistics was initially developed to generate probability distribution predictions of ore grade in the mining industry; however, it has been successfully applied in diverse scientific disciplines. This technique includes univariate, multivariate, and simulations. Kriging geostatistical methods, simple, ordinary, and universal Kriging, are not multivariate models in the usual statistical function. Notwithstanding, simple, ordinary, and universal kriging techniques utilize random function models that include unlimited random variables while modeling one attribute. The coKriging technique is a multivariate estimation method that simultaneously models two or more attributes defined with the same domains as coregionalization.

Objective: This study investigates the impact of populations on traffic volumes as a variable. The additional variable determines the strength or accuracy obtained when data integration is adopted. In addition, this is to help improve the estimation of annual average daily traffic (AADT).

Methods procedures, process: The investigation adopts the coKriging technique with AADT data from 2009 to 2016 from Montana, Minnesota, and Washington as primary attributes and population as a controlling factor (second variable). CK is implemented for this study after reviewing the literature and work completed by comparing it with other geostatistical methods.

Results, observations, and conclusions: The Investigation employed two variables. The data integration methods employed in CK yield more reliable models because their strength is drawn from multiple variables. The cross-validation results of the model types explored with the CK technique successfully evaluate the interpolation technique's performance and help select optimal models for each state. The results from Montana and Minnesota models accurately represent the states' traffic and population density. The Washington model had a few exceptions. However, the secondary attribute helped yield an accurate interpretation. Consequently, the impact of tourism, shopping, recreation centers, and possible transiting patterns throughout the state is worth exploring.

The most obvious symptom of the most obvious trend in the building of new libraries is the fact that, as yet, no spade has entered the ground of the site on Euston Road, London, upon which the new building for the British Library Reference Division has to be erected. Some twenty years of continued negotiation and discussion finally resulted in the choice of this site. The UK and much more of the world awaits with anticipation what could and should be the major building library of the twentieth century. The planning and design of a library building, however large or small, is, relatively speaking, a major operation, and deserves time, care and patience if the best results are to be produced.

The purpose of the study is to quickly identify significant heterogeneity of surrounding rock of tunnel face that generally occurs during the construction of large-section rock tunnels of high-speed railways.

Relying on the support vector machine (SVM)-based classification model, the nominal classification of blastholes and nominal zoning and classification terms were used to demonstrate the heterogeneity identification method for the surrounding rock of tunnel face, and the identification calculation was carried out for the five test tunnels. Then, the suggestions for local optimization of the support structures of large-section rock tunnels were put forward.

The results show that compared with the two classification models based on neural networks, the SVM-based classification model has a higher classification accuracy when the sample size is small, and the average accuracy can reach 87.9%. After the samples are replaced, the SVM-based classification model can still reach the same accuracy, whose generalization ability is stronger.

By applying the identification method described in this paper, the significant heterogeneity characteristics of the surrounding rock in the process of two times of blasting were identified, and the identification results are basically consistent with the actual situation of the tunnel face at the end of blasting, and can provide a basis for local optimization of support parameters.

A novel approach using artificial neural networks (ANNs) for predicting temperature profiles evaluated 27 wells in the Gulf of Mexico. Two artificial neural network models were developed that predict the temperature of the flowing fluid at any depth in flowing oil wells. Back propagation was used in training the networks. The networks were tested using measured temperature profiles from the 27 oil wells. Both neural network models successfully mapped the general temperature‐profile trends of naturally flowing oil wells. The highest accuracy was achieved with a mean absolute relative percentage error of 6.0 per cent. The accuracy of the proposed neural network models to predict the temperature profile is compared to that of existing correlations. Many correlations to predict temperature profiles of the wellbore fluid, for single‐phase or multiphase flow, in producing oil wells have been developed using theoretical principles such as energy, mass and momentum balances coupled with regression analysis. The Neural Network 2 model exhibited significantly lower mean absolute relative percentage error than other correlations. Furthermore, in order to test the accuracy of the neural network models to that of Kirkpatrick’s correlation, a mathematical model was developed for Kirkpatrick’s flowing temperature gradient chart.

The purpose of this paper is to take the early Permian no.6 coal seam in Jungar coalfield of North China as an example, this paper studied the net primary productivity (NPP) level of the early Permian peatland and its relationship with the atmospheric environment at that time, analyzed the influence of the atmospheric environment, and discussed its control factors.

First, geophysical logging signals were used for a spectrum analysis to obtain the Milankovitch cycle parameters in the no. 6 coal seam, including the eccentricity (95 ka); obliquity (35.6 ka); and precession (21.2 ka). These were then used to calculate the accumulation rate of the residual carbon in the no. 6 coal seam, which was determined to be between 49.44 and 50.57 gC/(m² · a). The carbon loss could be calculated according to the density and residual carbon content of the no. 6 coal seam. Then, the total carbon accumulation rate of the peatland was further derived as being between 64.91 and 66.40 gC/(m² · a). Also, the NPP of the peatland was determined to be between 129.82 and 132.8 gC/(m² · a).

The result showed that the NPP of the early Permian peatland area was lower than that of the Holocene at the same latitude, and also lower than that of the later Permian of South China.

This study’s comprehensive analysis indicated that the temperature and humidity conditions, along with the oxygen and carbon dioxide levels in the atmosphere, were the main control factors of the NPP of the early Permian peatland. Also, wildfires were found to play a role.

The aim of this paper is to revisit the albedo for uncertainty. The albedo is considered as a fuzzy value due to some realistic reasons which they will be discussed in details. After defining an appropriate uncertain albedo by using fuzzy set theory, the related energy balance model is also redefined as a fuzzy differential equation by using the concept of fuzzy derivative.

The well-known Earth energy balance model is redefined as a fuzzy differential equation by using the concept of fuzzy derivative. Thus, instead of an ordinary differential equation, a fuzzy differential equation arises which it's solution procedure will be discussed in details.

Results indicate that the fuzzy uncertainty for albedo causes more real results after solving the fuzzy energy balance equation. Considering albedo as a fuzzy number is more realistic than considering a single certain number for albedo of a surface. This is due to this fact that the Earth's surface coverage is not crisp and the boundaries of different types of lands are not consistent. The proposed approach of this paper can help us to provide more realistic climate models and construct dynamical models which can model the albedo based on its variability.

In this paper, we defined fuzzy energy balance model as a fuzzy differential equation for the first time. We also, considered albedo as a fuzzy number which is another novel approach.