Ionic composition, geological signature and environmental impacts of coalbed methane produced water in China

Guo, Chen; Qin, Yong; Ma, Dongmin; Xia, Yucheng; Chen, Yue; Si, Qinghong; Lu, Lingling

doi:10.1080/15567036.2019.1636161

Cited by 22 publications

(11 citation statements)

References 27 publications

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“…Distance to rivers was divided into five classes with an interval of 200 m ( Figure 3i, Table 1). The natural conditions of the slope were destroyed during the construction of transportation facilities, and oil and gas development, and other human engineering activities [74]. In the present study, distance to roads was grouped into five buffing zones using an interval of 100 m ( Figure 3j, Table 1).…”

Section: Data Preparationmentioning

confidence: 99%

Landslide Susceptibility Evaluation Using Hybrid Integration of Evidential Belief Function and Machine Learning Techniques

Chen

2019

Water

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In this study, Random SubSpace-based classification and regression tree (RSCART) was introduced for landslide susceptibility modeling, and CART model and logistic regression (LR) model were used as benchmark models. 263 landslide locations in the study area were randomly divided into two parts (70/30) for training and validation of models. 14 landslide influencing factors were selected, such as slope angle, elevation, aspect, sediment transport index (STI), topographical wetness index (TWI), stream power index (SPI), profile curvature, plan curvature, distance to rivers, distance to road, soil, normalized difference vegetation index (NDVI), land use, and lithology. Finally, the hybrid RSCART model and two benchmark models were applied for landslide susceptibility modeling and the receiver operating characteristic curve method is used to evaluate the performance of the model. The susceptibility is quantitatively compared based on each pixel to reveal the system spatial pattern between susceptibility maps. At the same time, area under ROC curve (AUC) and landslide density analysis were used to estimate the prediction ability of landslide susceptibility map. The results showed that the RSCART model is the optimal model with the highest AUC values of 0.852 and 0.827, followed by LR and CART models. The results also illustrate that the hybrid model generally improves the prediction ability of a single landslide susceptibility model.Water 2020, 12, 113 2 of 29 weights of evidence [10][11][12], frequency ratio [13][14][15][16][17], logistic regression [18][19][20][21], linear multivariate regression, multivariate adaptive regression spline [22][23][24], and statistical index [25,26] have been widely used. However, these traditional statistical methods do not provide satisfactory evaluation of the correlation between landslide influencing factors [4,27].Therefore, machine learning technologies have drawn extensive attention, and many kinds of machine learning methods have been developed and used, such as classification and regression trees [28,29], adaptive neuro-fuzzy inference systems [30,31], fuzzy logic [32,33], alternating decision trees [34][35][36], support vector machine [37][38][39], artificial neural networks [40,41], and random forest [4,[42][43][44][45]. In particular, hybrid models are increasingly used, such as the rotation forest-based decision trees [46,47], frequency ratio-based ANFIS model [48], bagging-based reduced error pruning trees [49], and multiboost-based support vector machines [50].Spatial prediction of landslide is not only the first important step, but also one of the most difficult tasks [31,51]. Many modeling methods have been used in the construction of landslide susceptibility maps in the past, but the accuracy of these models has not been accepted by all researchers [52]. Youssef et al. used CART model to study landslide susceptibility in Wadi Tayyah Basin. The result of post-cart model is not optimal [53]. However, when Felicísimo et al. studied the landslide susceptibility, CART shows...

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Section: Data Preparationmentioning

confidence: 99%

Landslide Susceptibility Evaluation Using Hybrid Integration of Evidential Belief Function and Machine Learning Techniques

Chen

2019

Water

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“…Slope can be perceived as a critical landform configuration; however, it exposes many natural and man-made disasters such as landslides, collapse events, and debris flows, which result indifferent human and asset loss levels globally, especially in developing countries. Geological environments are increasingly affected by human engineering activities [1][2][3]. Landslide hazard assessments represent a conditional probability based on time and spatial occurrence, and landslide magnitude for a given geo-environmental setting [4].…”

Section: Introductionmentioning

confidence: 99%

“…Statistical approaches are quantitative methods which can use different functional relationship. They can be subdivided into: (1) physically-based methods [10,11]; and (2) traditional statistical methods, such as the frequency ratio [12,13], evidential belief function [14,15], weight of evidence [16,17], discriminant analysis [18][19][20][21], and logistic regression [22,23], (3) advanced data mining technologies, such as artificial neural networks [24,25], support vector machines [26][27][28], adaptive neuro-fuzzy inference systems [29,30], alternating decision trees [31], and functional trees [32,33]. These methods need landslide inventories expressed as landslide density maps to produce functional relationships with causative factors [34].…”

Section: Introductionmentioning

confidence: 99%

Spatial Prediction of Landslide Susceptibility Based on GIS and Discriminant Functions

Wang

Chen

2020

IJGI

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The areas where landslides occur frequently pose severe threats to the local population, which necessitates conducting regional landslide susceptibility mapping (LSM). In this study, four models including weight-of-evidence (WoE) and three WoE-based models, which were linear discriminant analysis (LDA), Fisher’s linear discriminant analysis (FLDA), and quadratic discriminant analysis (QDA), were used to obtain the LSM in the Nanchuan region of Chongqing, China. Firstly, a dataset was prepared from sixteen landslide causative factors, including eight topographic factors, three distance-related factors, and five environmental factors. A landslide inventory map including 298 landslide locations was also constructed and randomly divided with a ratio of 70:30 as training and validation data. Subsequently, the WoE method was used to estimate the relationship between landslides and the landslide causative factors, which assign a weight value to each class of causative factors. Finally, four models were applied using the training dataset, and the predictive performance of each model was compared using the validation datasets. The results showed that FLDA had a higher performance than the other three models according to the success rate curve (SRC) and prediction rate curve (PRC), illustrating that it could be considered a promising approach for landslide susceptibility mapping in the study area.

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“…Thus, gas-water relative permeability (GWRP) more practically can reflect the flow behavior of two-phase fluid in coal reservoirs and the production performance of CBM wells, which is of great significance to CBM development (Wang et al, 2009;Clarkson et al, 2011;Durucan et al, 2013Durucan et al, , 2014Farokhpoor et al, 2014;Zhang et al, 2015;Gerami et al, 2016;Zhang et al, 2017;Salmachi and Karacan, 2017;Sun et al, 2018;Su et al, 2018). CBM reservoirs in China are characterized by low fluid pressure, low permeability, low gas saturation and high heterogeneity Kang et al, 2018;Guo et al, 2019). As a result, the characteristics of gas-water two-phase flow in coal reservoirs are significantly different from those in other areas, which determines the highly variable CBM productivity performance in China.…”

Section: Introductionmentioning

confidence: 99%

Pore Structure and Permeability Characterization of High‐rank Coal Reservoirs: A Case of the Bide‐Santang Basin, Western Guizhou, South China

Guo

Qin

et al. 2020

Acta Geologica Sinica (Eng)

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The methods of nuclear magnetic resonance (NMR) spectroscopy, mercury injection porosimetry (MIP), and gas -water relative permeability (GWRP) were used to reveal the pore structure and permeability characteristics of high-rank coal reservoirs in the Bide-Santang basin, western Guizhou, South China, to provide guidance for coalbed methane (CBM) exploration and exploitation and obtain direct insights for the development of CBM wells. The results indicate that the coal reservoirs in the study area are characterized by well-developed adsorption pores and poorly developed seepage pores. The bimodal NMR transverse relaxation time (T 2 ) spectra and the mutation in the fractal characteristic of the MIP pore volume indicate poor connectivity between the adsorption pores and the seepage pores. As a result, the effective porosity is relatively low, with an average of 1.70%. The irreducible water saturation of the coal reservoir is relatively high, with an average of 66%, leading to a low gas relative permeability under irreducible water saturation. This is the main reason for the low recovery of high-rank CBM reservoirs, and effective enhanced CBM recovery technology urgently is needed. As a nondestructive and less time-consuming technique, the NMR is a promising method to quantitatively characterize the pores and fractures of coals.

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Ionic composition, geological signature and environmental impacts of coalbed methane produced water in China

Cited by 22 publications

References 27 publications

Landslide Susceptibility Evaluation Using Hybrid Integration of Evidential Belief Function and Machine Learning Techniques

Landslide Susceptibility Evaluation Using Hybrid Integration of Evidential Belief Function and Machine Learning Techniques

Spatial Prediction of Landslide Susceptibility Based on GIS and Discriminant Functions

Pore Structure and Permeability Characterization of High‐rank Coal Reservoirs: A Case of the Bide‐Santang Basin, Western Guizhou, South China

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