scholarly journals Combining K-Means Clustering and Random Forest to Evaluate the Gas Content of Coalbed Bed Methane Reservoirs

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Jie Yu ◽  
Linqi Zhu ◽  
Ruibao Qin ◽  
Zhansong Zhang ◽  
Li Li ◽  
...  

The accurate calculation of the gas content of coalbed bed methane (CBM) reservoirs is of great significance. However, due to the weak correlation between the logging response of coalbed methane reservoirs and the gas content parameters and strong nonlinear characteristics, it is difficult for conventional gas content calculation algorithms to obtain more reliable results. This paper proposes a CBM reservoir gas content assessment method combining K-means clustering and random forest. The K-means clustering is used to divide the reservoirs and distinguish the types to establish a random forest model. Judging from the evaluation effect of the research block, the prediction accuracy of the new method is significantly higher than that of the original method, and more accurate gas content prediction values can be obtained for different types of reservoirs. Studies have shown that this method can help the gas content evaluation of CBM reservoirs, improve the accuracy of gas content evaluation, and better support the exploration and development of CBM reservoirs. The results of this study show that the random forest method based on clustering can effectively distinguish the relationship between different logging responses and gas content. On this basis, the random forest algorithm modeling can effectively characterize the complex relationship between gas content and logging curve response. In the case of poor correlation between gas content and logging curve, the gas content of the reservoir can also be accurately calculated.

2019 ◽  
Vol 37 (9-10) ◽  
pp. 745-763 ◽  
Author(s):  
Zhijun Wang ◽  
Xiaojuan Wang ◽  
Weiqin Zuo ◽  
Xiaotong Ma ◽  
Ning Li

The capacity of coal to adsorb methane is greatly affected by temperature and, in recent years, temperature-dependent adsorption has been studied by many researchers. Even so, comprehensive conclusions have not been reached and conflicting experimental results are common. This paper reviews the current state of research regarding the temperature-dependent adsorption of methane in coal and catalogs the conclusions from experiments conducted on that subject by 28 researchers, as published between 1995 and 2017. Probability theory and statistics are used to show that the conclusion generally accepted by most researchers is that the amount of methane adsorbed by coal decreases with increasing temperature. It is highly likely that the Langmuir volume decreases as the temperature rises, and it is also probable that the Langmuir pressure increases at higher temperatures. Equations are presented that express the relationships between methane adsorption, Langmuir volume, Langmuir pressure, and temperature. Future research should be directed toward determining the relationship between Langmuir pressure and temperature. The results of the study presented herein provide a theoretical basis for predicting the gas content in coal seams and improving the efficiency of coalbed methane development.


2013 ◽  
Vol 868 ◽  
pp. 696-699 ◽  
Author(s):  
Cheng Long Liu ◽  
Xiang Hao Wang ◽  
Kun Liu ◽  
Jin Wang ◽  
Hui Guo ◽  
...  

Junggar Basin is located in north Xinjiang and it has a huge amount of coalbed methane resources with less exploration and mining. The most vital characteristic in junggar basin is coal dip angel and gas content varies a lot in different areas. This paper reveals the relationship between gas content and coal seam dip angel, bigger the coal seam dip angel lower the gas content. The target area of CBM exploration and mining in junggar basin is HEGSH-STH area, HEGSHX area, BSMY-JJM area,LJM area, KLMY area, HSTLG area and XZJQ area. Gas content is mainly influenced by tectonic movement in junggar basin, it is low in complex structure area and high in simple structure area. Inclination of the coal seam stands for the complexity of the structure in junggar basin, the structure is complex when the coal seam is steep, it is simple when the coal seam is flat. The result can be used as a new method for coalbed methane exploration and development in inclined coal seam areas, small coal dip area should be chosen as the high gas content target.


2013 ◽  
Vol 734-737 ◽  
pp. 1362-1366 ◽  
Author(s):  
Jie Hou ◽  
Chang Chun Zou ◽  
Zhao Hui Huang ◽  
Liang Xiao

In the log interpretation, the CBM content evaluation methods include: regression, LAN’s equation, KIM method, neural network, and so on. This paper investigates the suitability of these methods for a CBM reservoir in Southern Qinshui Basin, Shanxi Province of China, and finds out the most reasonable interpretation model of CBM content in this area. The results show that the composite parameter(COMP) method and the LAN’s equation method are more suitable for evaluating the CBM content in this study area, while the other methods are not.


Energies ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 213
Author(s):  
Chao Cui ◽  
Suoliang Chang ◽  
Yanbin Yao ◽  
Lutong Cao

Coal macrolithotypes control the reservoir heterogeneity, which plays a significant role in the exploration and development of coalbed methane. Traditional methods for coal macrolithotype evaluation often rely on core observation, but these techniques are non-economical and insufficient. The geophysical logging data are easily available for coalbed methane exploration; thus, it is necessary to find a relationship between core observation results and wireline logging data, and then to provide a new method to quantify coal macrolithotypes of a whole coal seam. In this study, we propose a L-Index model by combing the multiple geophysical logging data with principal component analysis, and we use the L-Index model to quantitatively evaluate the vertical and regional distributions of the macrolithotypes of No. 3 coal seam in Zhengzhuang field, southern Qinshui basin. Moreover, we also proposed a S-Index model to quantitatively evaluate the general brightness of a whole coal seam: the increase of the S-Index from 1 to 3.7, indicates decreasing brightness, i.e., from bright coal to dull coal. Finally, we discussed the relationship between S-Index and the hydro-fracturing effect. It was found that the coal seam with low S-Index values can easily form long extending fractures during hydraulic fracturing. Therefore, the lower S-Index values indicate much more favorable gas production potential in the Zhengzhuang field. This study provides a new methodology to evaluate coal macrolithotypes by using geophysical logging data.


2020 ◽  
Author(s):  
Olivia M Bernstein ◽  
Joshua D. Grill ◽  
Daniel L. Gillen

Abstract Background: Early study exit is detrimental to statistical power and increases the risk for bias in Alzheimer’s disease clinical trials. Previous analyses in early phase academic trials demonstrated associations between rates of trial incompletion and participants’ study partner type, with participants enrolling with non-spouse study partners being at greater risk.Methods: We conducted secondary analyses of two multinational phase III trials of semagacestat, an oral gamma secretase inhibitor, for mild-to-moderate AD dementia. Cox’s proportional hazards regression model was used to estimate the relationship between study partner type and the risk of early exit from the trial after adjustment for a priori identified potential confounding factors. Additionally, we used a random forest model to identify top predictors of dropout.Results: Among participants with spousal, adult child, and other study partners, respectively, 35%, 38%, and 36% dropped out or died prior to protocol-defined study completion, respectively. In unadjusted models, the risk of trial incompletion differed by study partner type (unadjusted p-value=0.027 for test of differences by partner type), but in models adjusting for potential confounding factors the differences were not statistically significant (p-value=0.928). In exploratory modeling, participant age was identified as the primary characteristic to explain the relationship between study partner type and the risk of failing to complete the trial. Participant age was also the strongest predictor of trial incompletion in the random forest model.Conclusions: After adjustment for age, no qualitative differences in the risk of incompletion were observed when comparing participants with different study partner types in these trials. Differences between our findings and the findings of previous studies may be explained by differences in trial phase, size, geographic regions, or the composition of academic and non-academic sites.


2016 ◽  
Vol 20 (3) ◽  
pp. 1 ◽  
Author(s):  
Teng Li ◽  
Caifang Wu

With a burial depth of 1000 m as the demarcation, the coal reservoir in South Yanchuan Block, China is divided into deep reservoir and shallow reservoir regions. A combination of coalbed methane well production data, well logging interpretation, coalbed methane numerical simulations and reservoir properties were used to research various production characteristics at different depths. The results indicate that coal thickness and gas content are not key factors that influence methane production. The shallow reservoir is located in a tension zone, while the deep reservoir is located in both a transformation zone and a compression zone. Although the reservoir and closure pressures increase with the burial depth, the pressures fluctuate in the deep reservoir, especially in the transformation zone. This fluctuation influences the opened degree of the fractures in the reservoir. The effective stress is lower in the deep reservoir than in the shallow reservoir, leading to higher permeability in the deep reservoir. This difference in effective stress is the key factor that influences the methane production. The combination of coal thickness and gas content also significantly influenced the methane production. Influenced by the reservoir and closure pressures, the Type III coal in the shallow reservoir is more developed, while the deep reservoir contained more developed Type I and Type II coal. The permeability increases exponentially with increasing thickness of Type I and Type II coal, which determines the high reservoir permeability in the deep reservoir. The development of Type III coal leads to the poor reservoir hydraulic fracturing effect. However, a reservoir with thick Type I and Type II coal can have a positive effect. Influencia de la presión, la estructura del carbón y su permeabilidad sobre la productividad de gas metano de carbón en profundidades de enterramiento del bloque Yanchuan Sur, ChinaResumenCon una profundidad de enterramiento de 1000 metros, el yacimiento de carbón del bloque Yanchuan Sur, en China, se divide en dos: el depósito profundo y el depósito superficial. Este trabajo combina los datos de la información de producción de gas metano asociado carbón, la interpretación de registros de pozo, las simulaciones numéricas de metano asociado a carbón y las propiedades del reservorio para encontrar las características de producción a diferentes profundidades. Los resultados indican que el espesor del carbón y el contenido de gas no son factores que alcancen a influir en la producción de metano. El depósito superficial se encuentra en una zona de tensión, mientras el depósito profundo está ubicado en una región tanto de transformación como de compresión. Aunque el reservorio y la presión de cierre se incrementan con la profundidad de enterramiento, las presiones fluctúan en el depósito profundo, especialmente en la zona de transformación. Esta fluctuación influye en el grado de apertura de las fracturas en el depósito. La tensión efectiva es más baja en el depósito profundo, lo que significa una mayor permeabilidad. La diferencia en la tensión efectiva es el factor clave que incide en la producción de metano. Afectado por las presiones de cierre y del yacimiento, el carbón tipo III en el depósito superficial está más desarrollado, mientras que el depósito profundo contiene carbón tipo I y tipo II más desarrollado. La permeabilidad se incrementa exponencialmente con el incremento del espesor en el carbón tipo I y tipo II, lo que determina la alta porosidad en el depósito profundo. El desarrollo de carbón tipo III lleva a un pobre efecto de la fractura hidráulica en el depósito. Sin embargo, un depósito con carbón tipo I y tipo II espeso podría tener un efecto positivo.


2021 ◽  
pp. 1-24
Author(s):  
Heng Wang ◽  
Lifa Zhou ◽  
Wang Yuxia

Laser Raman spectroscopy can be used to acquire the unique fingerprint of a specific molecule, and it is widely used to identify substances and study the spectral line characteristics of molecular structures. The measurement of coalbed methane (CBM) content is essential in the exploration and development of CBM fields for optimizing the fracture design. For this purpose, laser Raman spectroscopy can be extremely beneficial because it detects the gas content rapidly and accurately. Moreover, conventional gas content testing methods are laborious, time-intensive, expensive, and yield inaccurate results. Therefore, we integrated a laser Raman spectroscopy system with a coiled tubing (CT) equipment for downhole deployment in gas wells to accurately determine the CBM content in situ. The developed system can directly determine the CBM content at a specific location in the target layer. The trace test characteristics enable this system to rapidly detect downhole gas components and contents. The real-time detection data are transmitted via a cable to a computer on the surface and are processed using a baseline correction algorithm and data enhancement algorithm. Fourier transform and wavelet transform are used to identify the Raman spectral lines, while analysis of Raman spectra is used to determine CBM content. By employing this equipment, we can shorten the cycle of depressurization, drainage, and recovery processes from multiple days to just a few hours. Furthermore, the integrated laser Raman spectroscopy-CT system enables a flexible operation and possesses strong site operability, making it suitable for complex and high-risk wells.


2019 ◽  
Vol 491 (2) ◽  
pp. 2939-2952 ◽  
Author(s):  
Benjamin D Oppenheimer ◽  
Jonathan J Davies ◽  
Robert A Crain ◽  
Nastasha A Wijers ◽  
Joop Schaye ◽  
...  

ABSTRACT Davies et al. established that for L* galaxies the fraction of baryons in the circumgalactic medium (CGM) is inversely correlated with the mass of their central supermassive black holes (BHs) in the EAGLE hydrodynamic simulation. The interpretation is that, over time, a more massive BH has provided more energy to transport baryons beyond the virial radius, which additionally reduces gas accretion and star formation. We continue this research by focusing on the relationship between the (1) BH masses (MBH), (2) physical and observational properties of the CGM, and (3) galaxy colours for Milky Way-mass systems. The ratio of the cumulative BH feedback energy over the gaseous halo binding energy is a strong predictor of the CGM gas content, with BHs injecting significantly higher than the binding energy resulting in gas-poor haloes. Observable tracers of the CGM, including $\rm {C\, \small{IV}}$, $\rm {O\, \small{VI}}$, and ${\rm {H\, \small{I}}}$ absorption line measurements, are found to be effective tracers of the total z ∼ 0 CGM halo mass. We use high-cadence simulation outputs to demonstrate that BH feedback pushes baryons beyond the virial radius within 100 Myr time-scales, but that CGM metal tracers take longer (0.5–2.5 Gyr) to respond. Secular evolution of galaxies results in blue, star-forming or red, passive populations depending on the cumulative feedback from BHs. The reddest quartile of galaxies with M* = 1010.2−10.7 M⊙ (median u − r = 2.28) has a CGM mass that is 2.5 times lower than the bluest quartile (u − r = 1.59). We propose observing strategies to indirectly ascertain fCGM via metal lines around galaxies with measured MBH. We predict statistically detectable declines in $\rm {C\, \small{IV}}$ and $\rm {O\, \small{VI}}$ covering fractions with increasing MBH for central galaxies with M* = 1010.2−10.7M⊙.


2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shifeng Niu ◽  
Guiqiang Li

The approaches monitoring fatigue driving are studied because of the fact that traffic accidents caused by fatigue driving often have fatal consequences. This paper proposes a new approach to predict driving fatigue using location data of commercial dangerous goods truck (CDT) and driver’s yawn data. The proposed location data are from an existing dataset of a transportation company that was collected from 166 vehicles and drivers in an actual driving environment. Six different categories of the predictor set are considered as fatigue-related indexes including travel time, day of week, road type, continuous driving time, average velocity, and overall mileage. The driver’s yawn data are used as a proxy for ground truth for the classification algorithm. From the six different categories of the predictor set, we obtain a set of 17 predictor variables to train logistic regression, neural network, and random forest classifiers. Then, we evaluate the predictive performance of the classifiers based on three indexes: accuracy, F1-measure, and area under the ROC curve (AUROC). The results show that the random forest is more suitable for predicting fatigue driving using location data according to its best accuracy (74.18%), F1-measure (62.02%), and AUROC (0.8059). Finally, we analyze the relationship between fatigue driving and driving environment according to variable importance described by random forest. In summary, our results obviously exhibit the potential of location data for reducing the accident rate caused by fatigue driving in practice.


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