Estimation of the unfrozen water content of saturated sandstones by ultrasonic velocity

The unfrozen water content(UWC)of rocks at low temperature is an important index for evaluating the stability of the rock engineering in cold regions and artificial freezing engineering.This study addresses a new method to estimate the UWC of saturated sandstones at low temperature by using the ultrasonic velocity.Ultrasonic velocity variations can be divided into the normal temperature stage(20 to 0℃),quick phase transition stage(0 to-5℃)and slow phase transition stage(-5 to-25℃).Most increment of ultrasonic velocity is completed in the quick phase transition stage and then turns to be almost a constant in the slow phase transition stage.In addition,the UWC is also measured by using nuclear magnetic resonance(NMR)technology.It is validated that the ultrasonic velocity and UWC have a similar change law against freezing and thawing temperatures.The WE(weighted equation)model is appropriate to estimate the UWC of saturated sandstones,in which the parameters have been accurately determined rather than by data fitting.In addition,a linear relationship between UWC and ultrasonic velocity is built based on pore ice crystallization theory.It is evidenced that this linear function can be adopted to estimate the UWC at any freezing temperature by using P-wave velocity,which is simple,practical,and accurate enough compared with the WE model.

Rapid testing and prediction of soil–water characteristic curve of subgrade soils considering stress state and degree of compaction

The subgrade soil is generally in saturated or unsaturated condition. To analyze complex thermo-hydro-mechanical-chemical (THMC) behaviors of subgrade, it is essential to determine the soil–water characteristic curve (SWCC) that represents the relationship between matric suction and moisture content. In this study, a full-automatic rapid stress-dependent SWCC pressure-plate extractor was developed. Then, the influences of overburden stress and degree of compaction on the SWCC of subgrade soil such as high liquid limit silt (MH) and low liquid limit clay (CL) were analyzed. Accordingly, a new model taking into account the influences of overburden stress and degree of compaction based on the well-known Van Genuchten (VG) SWCC fitting model was presented and validated. The results show that with the increase of the degree of compaction and overburden stress, the saturated moisture content of subgrade soil decreases, while the air-entry value increases and the transition section curve becomes flat. The influences of the degree of compaction and overburden stress on the SWCC of MH is greater than that of CL. Meanwhile, there was a satisfactory agreement between the prediction and measurement, indicating a good performance of the new model for predicting the SWCC.

Modeling of Total Dissolved Solids (TDS) and Sodium Absorption Ratio (SAR) in the Edwards-Trinity Plateau and Ogallala Aquifers in the Midland-Odessa Region Using Random Forest Regression and eXtreme Gradient Boosting

Efficient water quality monitoring and ensuring the safety of drinking water by government agencies in areas where the resource is constantly depleted due to anthropogenic or natural factors cannot be overemphasized. The above statement holds for West Texas, Midland, and Odessa Precisely. Two machine learning regression algorithms (Random Forest and XGBoost) were employed to develop models for the prediction of total dissolved solids (TDS) and sodium absorption ratio (SAR) for efficient water quality monitoring of two vital aquifers: Edward-Trinity (plateau), and Ogallala aquifers. These two aquifers have contributed immensely to providing water for different uses ranging from domestic, agricultural, industrial, etc. The data was obtained from the Texas Water Development Board (TWDB). The XGBoost and Random Forest models used in this study gave an accurate prediction of observed data (TDS and SAR) for both the Edward-Trinity (plateau) and Ogallala aquifers with the R2 values consistently greater than 0.83. The Random Forest model gave a better prediction of TDS and SAR concentration with an average R, MAE, RMSE and MSE of 0.977, 0.015, 0.029 and 0.00, respectively. For the XGBoost, an average R, MAE, RMSE, and MSE of 0.953, 0.016, 0.037 and 0.00, respectively, were achieved. The overall performance of the models produced was impressive. From this study, we can clearly understand that Random Forest and XGBoost are appropriate for water quality prediction and monitoring in an area of high hydrocarbon activities like Midland and Odessa and West Texas at large.

A Water Level Forecast of Pattani River in the Southern of Thailand by Deep Learning

Nowadays, the deep learning methods are widely applied to analyze and predict the trend of various disaster events and offer the alternatives to make the appropriate decisions. These support the water resource management and the short-term planning. In this paper, the water levels of the Pattani River in the Southern of Thailand have been predicted every hour of 7 days forecast. Time Series Transformer and Linear Regression were applied in this work. The results of both were the water levels forecast that had the high accuracy. Moreover, the water levels forecasting dashboard was developed for using to monitor the water levels at the Pattani River as well.

Application of Artificial Neural Networks for the Prediction of Water Quality Variables in the Nile Delta

The quality of a water body is usually characterized by sets of physical, chemical, and biological parameters, which are mutually interrelated. Since August 1997, monthly records of 33 parameters, monitored at 102 locations on the Nile Delta drainage system, are stored in a National Database operated by the Drainage Research Institute (DRI). Correlation patterns may be found between water quantity and water quality parameters at the same location, or among water quality parameters within a monitoring location or among locations. Serial correlation is also detected in water quality variables. Through the investigation of the level of information redundancy, assessment and redesign of water quality monitoring network aim to improve the overall network efficiency and cost effectiveness. In this study, the potential of the Artificial Neural Network (ANN) on simulating interrelation between water quality parameters is examined. Several ANN inputs, structures and training possibilities are assessed and the best ANN model and modeling procedure is selected. The prediction capabilities of the ANN are compared with the linear regression models with autocorrelated residuals, usually used for this purpose. It is concluded that the ANN models are more accurate than the linear regression models having the same inputs and output.

Application of a Neural Network Technique for Prediction of the Water Quality Index in the Dong Nai River, Vietnam

Recent trends in environmental management of water resource have enlarged the demand for predicting techniques that can provide reliable, efficient and accurate water quality. In this case study, the authors applied the Artificial Neural Networks （ANN） to estimate the water quality index on the Dong Nai River flowing through Dong Nai and Binh Duong provinces. The information and data including 10 water quality parameters of the Dong Nai River at 23 monitoring stations were collected during the recorded time period from 2010 to 2014 to build water quality prediction models. The results of the study demonstrated that the Water Quality Index （WQI） forecasted with GRNN was very significant and had high correlation coefficient （R2 = 0.974 and p = 0.0） compared to the real values of the WQI. Moreover, the ANN models provided better predicted values than the multiple regression models did.

Prediction of Water Activity for Mixed Aqueous Solutions from the Data of Their Binary Constituent Solutions

The equation of Patwardhan and Kumar for water activities of mixed electrolyte solutions is extended to aqueous solutions containing non-electrolytes. This equation and the linear isopiestic relation are used to predict water activities of 56 ternary aqueous solutions in terms of the data of their binary subsystems. Both equation of Patwardhan and Kumar and the linear isopiestic relation can provide good predictions for water activities of the present 40 electrolyte solutions, and the linear isopiestic relation generally yields better predictions. The predictions of the extended equation of Patwardhan and Kumar and the linear isopiestic relation are in general quite reasonable for the present 8 ternary solutions of electrolytes and non-electrolytes, and the results of the linear isopiestic relation are usually better. The predictions of these two methods generally agree well with the experimental data for the 8 non-electrolyte mixtures being studied, and the linear isopiestic relation is better.

Development and application of a GIS-based artificial neural network system for water quality prediction: a case study at the Lake Champlain area

Artificial Neural Network(ANN)models have been extensively applied in the prediction of water resource variables,and Geographical Information System(GIS)includes powerful functions to visualize spatial data.In order to provide an efficient tool for environmental assessment and management that combines the advantages of these two modules,a GIS-based ANN water quality prediction system was developed in the present study.The ANN module and ArcGIS Engine module,along with a dynamic database,were imbedded in the system,which integrates water quality prediction via the ANN model and spatial presentation of the model results.The structure of the ANN model could be modified through the graphical user interface to optimize the model performance.The developed system was applied to a real case study for the prediction of the total phosphorus concentration in the Lake Champlain area.The prediction results were verified with the monitoring data,and the performance of the developed model was further evaluated through graphical techniques and quantitative statistical methods.Overall,the developed system provided satisfactory prediction results,and spatial distribution maps of the predicted results were obtained,which coincided with the monitored values.The developed GIS-based ANN water quality prediction system could serve as an efficient tool for engineers and decision makers.

Combined Method of Chaotic Theory and Neural Networks for Water Quality Prediction

Chaos theory was introduced for water quality, prediction, and the model of water quality prediction was established by combining phase space reconstruction theory and BP neural network forecasting method. Through the phase space reconstruction, the one-dimensional water quality time series were mapped to be multi-dimensional sequence, which enriched the spatial information of water quality change and expanded mapping region of training samples of BP neural network. Established model of combining chaos theory and BP neural network were applied to forecast turbidity time series of a certain reservoir. Contrast to BP neural network method, the relative error and the mean squared error of the combined method had all varying degrees of lower. Results indicated the neural network model with chaos theory had the higher prediction accuracy, at the same time, it had better fault-tolerant capability and generalization performance .

Test of numerical prediction of sea water temperature in the Taiwan Strait

A dynamic numerical prediction model of sea water temperature for limited sea area is used to predict the sea water temperature at the sea area near Fujian. Essential adjustments have been made in accordance with the characteristics of this region. Two Tests have been made. One is in summer (3 d) and the other is in winter (10 d). In the summer test, a typhoon is just passing by and the calculated current field well responds to typhoon. In the winter test, variation tendency of the predicted sea water temperature field agrees with that of the observation basically, the absolute mean error in the whole sea area is 0 .6 ℃. The variation of the sea water temperature is mostly af- fected by entrainment and pumping, which is related to the topography of the strait.

Comparison of different spectral decompositions for non-marine deep water sandstone reservoir prediction in the Xingma area

It is difficult to identify and predict non-marine deep water sandstone reservoir facies and thickness,using routine seismic analyses in the Xingma area of the western Liaohe sag,due to low dominant frequencies,low signal-to-noise ratios,rapid lateral changes and high frequencies of layered inter-bedding.Targeting this problem,four types of frequency spectral decomposition techniques were tested for reservoir prediction.Among these,the non-orthogonal Gabor-Morlet wavelet frequency decomposition method proved to be the best,was implemented directly in our frequency analysis and proved to be adaptable to non-stationary signals as well.The method can overcome the limitations of regular spectral decomposition techniques and highlights local features of reservoir signals.The results are found to be in good agreement with well data.Using this method and a 3-D visualization technology, the distribution of non-marine deep water sandstone reservoirs can be precisely predicted.

Prediction of the maximum water inflow in Pingdingshan No.8 mine based on grey system theory

In order to prevent and control the water inflow of mines, this paper built a new initial GM（1, 1） model to torecast the maximum water inflow according to the principle of new information. The effect of the new initial GM（1, 1） model is not ideal by the concrete example. Then according to the principle of making the sum of the squares of the difference between the calculated sequences and the original sequences, an optimized GM（1, I） model was established. The result shows that this method is a new prediction method which can predict the maximum water inflow accurately. It not only conforms to the guide- line of prevention primarily, but also provides reference standards to managers on making prevention measures.

Dissolution mechanism of a deep-buried sandstone reservoir in a deep water area:A case study from Baiyun Sag,Zhujiang River(Pearl River)Mouth Basin

Dissolution mechanism and favorable reservoir distribution prediction are the key problems restricting oil and gas exploration in deep-buried layers.In this paper,the Enping Formation and Zhuhai Formation in Baiyun Sag of South China Sea was taken as a target.Based on the thin section,scanning electron microscopy,X-ray diffraction,porosity/permeability measurement,and mercury injection,influencing factors of dissolution were examined,and a dissolution model was established.Further,high-quality reservoirs were predicted temporally and spatially.The results show that dissolved pores constituted the main space of the Paleogene sandstone reservoir.Dissolution primarily occurred in the coarse-and medium-grained sandstones in the subaerial and subaqueous distributary channels,while dissolution was limited in fine-grained sandstones and inequigranular sandstones.The main dissolved minerals were feldspar,tuffaceous matrix,and diagenetic cement.Kaolinization of feldspar and illitization of kaolinite are the main dissolution pathways,but they occur at various depths and temperatures with different geothermal gradients.Dissolution is controlled by four factors,in terms of depositional facies,source rock evolution,overpressure,and fault activities,which co-acted at the period of 23.8–13.8 Ma,and resulted into strong dissolution.Additionally,based on these factors,high-quality reservoirs of the Enping and Zhuhai formations are predicted in the northern slope,southwestern step zone,and Liuhua uplift in the Baiyun Sag.

Predicting the height of water-flow fractured zone during coal mining under the Xiaolangdi Reservoir

It is very important to determine the extent of the fractured zone through which water can flow before coal mining under the water bodies.This paper deals with methods to obtain information about overburden rock failure and the development of the fractured zone while coal mining in Xin'an Coal Mine.The risk of water inrush in this mine is great because 40%of the mining area is under the Xiaolangdi reservoir.Numerical simulations combined with geophysical methods were used in this paper to obtain the development law of the fractured zone under different mining conditions.The comprehensive geophysical method described in this paper has been demonstrated to accurately predict the height of the water-flow fractured zone.Results from the new model, which created from the results of numerical simulations and field measurements,were successfully used for making decisions in the Xin'an Coal Mine when mining under the Xiaolangdi Reservoir.Industrial scale experiments at the number 11201,14141 and 14191 working faces were safely carried out.These achievements provide a successful background for the evaluation and application of coal mining under large reservoirs.

MECHANISM ON PROGRESSIVE INTRUSION OF PRESSURE WATER UNDER COAL SEAMS INTO PROTECTIVE AQUICLUDE AND ITS APPLICATION IN PREDICTION OF WATER INRUSH

This article proposes a water inrush mechanism of progressive intrusion of pressure water up into the coal floor aquiclude according to injection tests and observations. A numerical model and a criterion of water inrush are established based on the mechanism. The theory is succesSfully used in predictions of water inrush.

Chemical kinetics evaluation and its application of natural gas generation derived from the Yacheng Formation in the deep-water area of the Qiongdongnan Basin,China

The natural gas generation process is simulated by heating source rocks of the Yacheng Formation, including the onshore-offshore mudstone and coal with kerogens of Type II2-III in the Qiongdongnan Basin. The aim is to quantify the natural gas generation from the Yacheng Formation and to evaluate the geological prediction and kinetic parameters using an optimization procedure based on the basin modeling of the shallow-water area. For this, the hydrocarbons produced have been grouped into four classes（C1, C2, C3 and C4-6）. The results show that the onset temperature of methane generation is predicted to occur at 110℃ during the thermal history of sediments since 5.3 Ma by using data extrapolation. The hydrocarbon potential for ethane, propane and heavy gaseous hydrocarbons（C4-6） is found to be almost exhausted at geological temperature of 200℃ when the transformation ratio（TR） is over 0.8, but for which methane is determined to be about 0.5 in the shallow-water area. In contrast, the end temperature of the methane generation in the deep-water area was over 300℃ with a TR over 0.8. It plays an important role in the natural gas exploration of the deep-water basin and other basins in the broad ocean areas of China. Therefore, the natural gas exploration for the deep-water area in the Qiongdongnan Basin shall first aim at the structural traps in the Ledong, Lingshui and Beijiao sags, and in the forward direction of the structure around the sags, and then gradually develop toward the non-structural trap in the deep-water area basin of the broad ocean areas of China.

Application of Time Serial Model in Water Quality Predicting

Water resources are an indispensable and valuable resource for human survival and development.Water quality predicting plays an important role in the protection and development of water resources.It is difficult to predictwater quality due to its random and trend changes.Therefore,amethod of predicting water quality which combines Auto Regressive Integrated Moving Average(ARIMA)and clusteringmodelwas proposed in this paper.By taking thewater qualitymonitoring data of a certain river basin as a sample,thewater quality Total Phosphorus(TP)index was selected as the prediction object.Firstly,the sample data was cleaned,stationary analyzed,and white noise analyzed.Secondly,the appropriate parameters were selected according to the Bayesian Information Criterion(BIC)principle,and the trend component characteristics were obtained by using ARIMA to conduct water quality predicting.Thirdly,the relationship between the precipitation and the TP index in themonitoring water field was analyzed by the K-means clusteringmethod,and the random incremental characteristics of precipitation on water quality changes were calculated.Finally,by combining with the trend component characteristics and the random incremental characteristics,the water quality prediction results were calculated.Compared with the ARIMA water quality prediction method,experiments showed that the proposed method has higher accuracy,and its Mean Absolute Error(MAE),Mean Square Error(MSE),and Mean Absolute Percentage Error(MAPE)were respectively reduced by 44.6%,56.8%,and 45.8%.

Evaluation of reservoir environment by chemical properties of reservoir water‒A case study of Chang 6 reservoir in Ansai oilfield,Ordos Basin,China

The Ordos Basin is the largest continental multi-energy mineral basin in China,which is rich in coal,oil and gas,and uranium resources.The exploitation of mineral resources is closely related to reservoir water.The chemical properties of reservoir water are very important for reservoir evaluation and are significant indicators of the sealing of reservoir oil and gas resources.Therefore,the caprock of the Chang 6 reservoir in the Yanchang Formation was evaluated.The authors tested and analyzed the chemical characteristics of water samples selected from 30 wells in the Chang 6 reservoir of Ansai Oilfield in the Ordos Basin.The results show that the Chang 6 reservoir water in Ansai Oilfield is dominated by calcium-chloride water type with a sodium chloride coefficient of generally less than 0.5.The chloride magnesium coefficients are between 33.7 and 925.5,most of which are greater than 200.The desulfurization coefficients range from 0.21 to 13.4,with an average of 2.227.The carbonate balance coefficients are mainly concentrated below 0.01,with an average of 0.008.The calcium and magnesium coefficients are between 0.08 and 0.003,with an average of 0.01.Combined with the characteristics of the four-corner layout of the reservoir water,the above results show that the graphics are basically consistent.The study indicates that the Chang 6 reservoir in Ansai Oilfield in the Ordos Basin is a favorable block for oil and gas storage with good sealing properties,great preservation conditions of oil and gas,and high pore connectivity.

A production prediction method of single well in water flooding oilfield based on integrated temporal convolutional network model

Since the oil production of single well in water flooding reservoir varies greatly and is hard to predict, an oil production prediction method of single well based on temporal convolutional network(TCN) is proposed and verified. This method is started from data processing, the correspondence between water injectors and oil producers is determined according to the influence radius of the water injectors, the influence degree of a water injector on an oil producer in the month concerned is added as a model feature, and a Random Forest(RF) model is built to fill the dynamic data of water flooding. The single well history is divided into 4 stages according to its water cut, that is, low water cut, middle water cut, high water cut and extra-high water cut stages. In each stage, a TCN based prediction model is established, hyperparameters of the model are optimized by the Sparrow Search Algorithm(SSA). Finally, the models of the 4 stages are integrated into one whole-life model of the well for production prediction. The application of this method in Daqing Oilfield, NE China shows that:(1) Compared with conventional data processing methods, the data obtained by this processing method are more close to the actual production, and the data set obtained is more authentic and complete.(2) The TCN model has higher prediction accuracy than other 11 models such as Long Short Term Memory(LSTM).(3) Compared with the conventional full-life-cycle models, the model of integrated stages can significantly reduce the error of production prediction.