Impact of geological modeling processes on spatial coalbed methane resource estimation

Zhou, Fengde; Yao, Guangqing; Tyson, Stephen

doi:10.1016/j.coal.2015.04.010

Cited by 31 publications

(13 citation statements)

References 32 publications

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“…The package of petrel reservoir engineering core modules, combined with an ECLIPSE industry-reference simulator, provides a set of numerical solutions for forecasting the dynamic reservoir processes and for designing plans for the exploration, development, and production. The modeling of bituminous coal seams using Schlumberger Petrel software enables to perform numerical simulations of coal bed methane extraction by means of directional drilling from the surface [19,20], as well as computer simulations of enhanced methane recovery combined with injecting CO 2 into the coal seams-enhanced coal bed methane (ECBM) [24][25][26][27][28][29][30][31].…”

Section: Institution Cbm Resources (Million Nm 3 )mentioning

confidence: 99%

Resource Assessment and Numerical Modeling of CBM Extraction in the Upper Silesian Coal Basin, Poland

et al. 2020

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The paper presents the assessment of the resources of methane considered as the main mineral in the most prospective selected areas of the Upper Silesian Coal Basin, Poland in the region of undeveloped deposits. The methane resources were estimated by means of a volumetric method at three depth levels, 1000, 1250, and 1500 m. A part of the Studzienice deposit comprising three coal seams, 333, 336, and 337, located in a methane zone was chosen for the numerical modeling of simulated methane production. The presented static 3D model has been developed using Petrel Schlumberger software. The total resources of methane in the area amount to approximately 446.5 million of Nm3. Numerical simulations of methane production from the selected coal seams with hydraulic fracturing were conducted by means of Schlumberger ECLIPSE reservoir simulator. Based on the simulations, it was concluded that, in the first six months of the simulations, water is produced from the seams, which is connected with the decrease in the rock mass pressure. The process prompts methane desorption from the coal matrix, which in turn results in a total methane production of 76.2 million of Nm3 within the five-year period of the simulations, which constitutes about 17% of total methane resources (GIP). The paper also presents a detailed analysis of Polish legislation concerning the activities aimed at prospecting, exploring, and extracting the deposits of hydrocarbons.

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Section: Institution Cbm Resources (Million Nm 3 )mentioning

confidence: 99%

Resource Assessment and Numerical Modeling of CBM Extraction in the Upper Silesian Coal Basin, Poland

et al. 2020

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“…Numerous researchers have reported the use of Monte Carlo simulations for modeling methane gas emission, with the results compared to those of stochastic modeling to re ect the acceptability of the simulation results (Zhou, 2015). In these studies, gas methane emission prediction has been based on the data on the coal structure, coal thickness, coal quality, and gas content (Zhou, 2015).…”

Section: Introductionmentioning

confidence: 99%

Model of Prediction of Gas Emission based on Gas Content Uncertainty in Coal Mine using Geostatistical Simulation and Monte Carlo Method

Rahimi

Ataee-pour

Madani

2021

Preprint

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It is very difficult to predict the emission of coal gas before the extraction, because it depends on various geological, geographical and operational factors. Gas content is a very important parameter for assessing gas emission in the coal seam during and after the extraction. Large amounts of gas released during the mining cause concern about adequate airflow for the ventilation and worker safety. Hence, the performance of the ventilation system is very important in an underground mine. In this paper, the gas content uncertainty in a coal seam is first investigated using the central data of 64 exploratory boreholes. After identifying the important coal seams in terms of gas emission, the variogram modeling for gas content was performed to define the distribution. Consecutive simulations were run for the random evaluation of gas content. Then, a method was proposed to predict gas emission based on the Monte Carlo random simulation method. In order to improve the reliability and precision of gas emission prediction, various factors affecting the gas emission were investigated and the main factors determining the gas emission were identified based on a sensitivity analysis on the mine data. This method produced relative and average errors of 2% and 0.57%, respectively. The results showed that the proposed model is accurate enough to determine the amount of emitted gas and ventilation. In addition, the predicted value was basically consistent with the actual value and the gas emission prediction method based on the uncertainty theory is reliable.

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“…Zhou et al built regression equations for gas content vs depth and gas content vs depth plus ash, and they found that the latter had a high correlation coefficient, which they then used to calculate the gas content . Based on the relationship of the measured gas content and the logs of gamma-ray, density, and depth, Zhou et al created an artificial neural network model to simulate the spatial distribution of the gas content . Zhou and Guan calculated the gas content from a regression equation using ash, volatile matter content, fixed carbon content, and depth as parameters .…”

Section: Introductionmentioning

confidence: 99%

Stochastic Modeling for Estimating Coalbed Methane Resources

Duan

Xia

et al. 2019

Energy Fuels

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In this paper, an integrated workflow was developed to estimate coalbed methane (CBM) probabilistic resources. This workflow captures all of the uncertainty parameters in CBM modeling, including the structural surface and coal thickness in the structural modeling and the coal facies, logging density, etc. in the property modeling. These uncertainties were statistically analyzed and quantified. Sensitivity analysis was carried out to rank the impact of these parameters on the resources, and six sensitive parameters were chosen. Then, multiple stochastic models were generated using the aforementioned six parameters to determine the resource distribution, from which the P90, P50, and P10 resources were chosen. Finally, the low, middle, and high probabilistic models corresponding to these three probabilistic resources were attained. This workflow was applied to a CBM field in Australia, and the simulated results show that for the low, middle, and high probabilistic resource models the resources are always high in the central and western part of the study area.

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Impact of geological modeling processes on spatial coalbed methane resource estimation

Cited by 31 publications

References 32 publications

Resource Assessment and Numerical Modeling of CBM Extraction in the Upper Silesian Coal Basin, Poland

Resource Assessment and Numerical Modeling of CBM Extraction in the Upper Silesian Coal Basin, Poland

Model of Prediction of Gas Emission based on Gas Content Uncertainty in Coal Mine using Geostatistical Simulation and Monte Carlo Method

Stochastic Modeling for Estimating Coalbed Methane Resources

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