The Experts below are selected from a list of 222 Experts worldwide ranked by ideXlab platform

Fengde Zhou - One of the best experts on this subject based on the ideXlab platform.

  • Uncertainty in estimation of Coalbed methane resources by geological modelling
    Journal of Natural Gas Science and Engineering, 2016
    Co-Authors: Fengde Zhou, Zhenliang Guan
    Abstract:

    This paper presents an uncertainty analysis of Coalbed methane resources estimation for a Coal seam gas field containing multiple Coal seams. Firstly, logs from nine wells and laboratory data from nine Coal seams were used to predict the Coal thickness, ash content and gas content at nine boreholes. Secondly, the structural models were determined using the well correlation, structural contour and Coal seam thicknesses maps from Coal mine data by the convergent gridder and sequential Gaussian simulation methods. Then, distributions of Coal Density and ash content were generated in 3D by using sequential Gaussian simulation based on the well log interpretations. Then, the distributions of gas content for each cell were built by two methods; one is multivariable regression analysis in 3D and the other is sequential Gaussian simulation based on the log interpreted gas content. Finally, Coalbed methane resources were estimated based on the cell volume, Coal Density, net to gross ratio and gas content. In Coalbed methane resources estimation, four different Density cutoffs were used to define the net to gross ratio of Coal in 3D. Results show that the gas contents decreases with increase in depth though with increases of vitrinite reflectance ratio, fixed carbon content and pressure. Calculated gas content from linear multivariate regression by using parameters of ash content, volatile matter content and fixed carbon content and sample's burial depth matches well with laboratory measured values. The total Coalbed methane resources estimated are similar by the two geological modelling processes, the multivariable regression analysis in 3D and the sequential Gaussian simulation. It has been found that the effects of Coal Density cutoffs on Coalbed methane resources for different Coal seams are different.

  • Impact of geological modeling processes on spatial Coalbed methane resource estimation
    International Journal of Coal Geology, 2015
    Co-Authors: Fengde Zhou, Stephen Tyson
    Abstract:

    Spatial Coalbed methane (CBM) resource estimation is based on spatial distributions of Coal, Coal adsorbed gas content and Coal Density. However, the spatial distribution of gas content can be generated via two different geological modeling processes: (1) The gas content distribution is generated by geological modeling based on the interpreted gas content at boreholes; (2) distributions of gas content related logs or Coal properties are generated firstly, then the gas content distribution is calculated based on the spatial distributions of logs or Coal properties by the relationship between the gas content and logs or Coal properties. This paper presents a study to compare the impact of these two processes on CBM resource estimation for Coal seam no. 3 (CS-3) in southeast Qinshui Basin, China. Well logs from 22 wells, laboratory data from five wells and well tops from 131 wells for CS-3 are used in log interpretation and geological modeling. The simple kriging (SK) is used to build the structural model and the Coal distribution. Weighted and unweighted omni-directional variograms for structural residual and Coal thickness are calculated using an in-house program. Logs of gamma-ray (GR) and Density (DEN or RHOB) are distributed in 3D by using sequential Gaussian simulation (SGS) with SK algorithm. Artificial neural network (ANN) is used to build the relationship of the measured raw gas content (RGC; gas content in raw Coal basis) with the logs of GR, DEN and measured depth (MD). Then the RGC is distributed in 3D by the two geological modeling processes. CBM resources are calculated in 3D based on the cells' volume, Coal Density and RGC. Results show that RGC increases with an increase in burial depth. Total CBM resources for the study area calculated by these two processes are similar for CS-3 but the distribution probability of high gas content is highly different which is important for locating wells.

  • Effects of Variogram Characteristics of Coal Permeability on CBM Production: A Case Study in Southeast Qinshui Basin, China:
    Energy Exploration & Exploitation, 2014
    Co-Authors: Fengde Zhou, Jianzhong Wang
    Abstract:

    The Coalbed methane (CBM) resources of China are located mainly in 9 basins, Ordos, Qinshui, Jungar, Diandongqianxi, Erlian, Tuha, Tarim, Tianshan and Hailaer. Qinshui Basin, one of the richest CBM basins in China, has boosted its annual CBM production to 3 × 109 m3 (106 Bcf). The Coal seams in Qinshui Basin are significant with high gas content but strong heterogeneous permeabilities ranging from 0.1 to 10 mD. This paper investigates the effects of spatial distribution characteristics of Coal permeability on CBM production. The study area is the South Shizhuang CBM district, Southeast of Qinshui Basin. The distributions of porosity, ash content, Coal Density and gas content of the Coal seam are generated using sequential Gaussian simulation (SGS) with only one realisation because this paper only justifies the effects of Coal permeability on CBM production. The permeabilities of 17 wells are determined by matching these wells' water and gas production with bottom-hole pressure as constraint. Then, the dis...

  • Stochastic modelling of Coalbed methane resources: a case study in Southeast Qinshui Basin, China
    International Journal of Coal Geology, 2012
    Co-Authors: Fengde Zhou, Jianzhong Wang, Guy Allinson, Dehua Xiong, Yildiray Cinar
    Abstract:

    This paper presents a stochastic analysis of Coalbed methane (CBM) resources for a Coal seam in southeast Qinshui Basin, China. Log and laboratory data are used to predict the Coal thickness, Coal ash content and Coal gas content. Using reservoir modelling, the distributions of the karst collapse column (KCC), Coal seam thickness, Coal quality and Coal gas content are generated. The convergent interpolation and sequential Gaussian simulation methods are used to model the surface and structure of the Coal seam. The structural models are determined by the surface structure and Coal seam thickness. Based on the structural models, the Coal and KCC are converted to two facies and their distributions are obtained using object modelling. The Coal Density distributions are simulated based on each facies model using the sequential Gaussian simulation. Finally, different realisations are used to study CBM resources. The results show that the heterogeneities in Coal seam thickness and Coal quality lead to significant uncertainty in estimating CBM resources. A model with lower heterogeneity gives a greater CBM resource. The distributions of KCC, Coal seam thickness, Coal quality and gas content are the main sources for uncertainty in CBM resource estimation. The Density variogram and top structure contribute less to the uncertainty in CBM resources.

Jacek Jarosz - One of the best experts on this subject based on the ideXlab platform.

  • economic valuation of Coal deposits the value of geological information in the resource recognition process
    Resources Policy, 2019
    Co-Authors: Michal Kopacz, Jaroslaw Kulpa, Dominik Galica, Artur Dyczko, Jacek Jarosz
    Abstract:

    Abstract Resource and economic assessment under uncertainty and risk is quite a difficult process. Many geological, mining, technical and economic aspects should be taken in to consideration simultaneously. The assessment process is also subject to errors, including measurement and interpretation errors and errors resulting from lack of knowledge or over-optimism. Uncertainty about the true value of the geological parameters is reduced as the recognition process and the knowledge about deposits improve. Especially at the first stages of mine development, the information available is insufficient and can lead to incorrect interpretation. In this paper, the impact of geological information on key economic parameters was examined, using the example of a hard Coal deposit in mine X in Poland. A scenario analysis was performed, based on a Monte Carlo simulation. The simulation was conducted using ogive distributions and copulas (empirical copulas) binding six geological parameters (Coal and waste rock thickness, Coal Density, Coal calorific value (LHV) and sulfur and ash content). To achieve the research purposes, the most popular economic measures (indicators), i.e., IRR, NPV and EBITDA, were used. Finally, differential distributions of these parameters were built and commented upon. The three analyzed scenarios were elaborated considering the data from the geological 3D model of Coal deposit X. A polygon (testing) area was defined in the Coal field, where complete geological information was available. The first data set (scenario 1) incorporated only information from deep surface boreholes (drillings), the second included data from surface drillings and channel samples and the third used only channel sampling. To interpolate the geological data between boreholes, the inverse distance interpolator was applied. Significant differences in the values of selected economic parameters were obtained; the largest in scenario 1. The average value of the IRR difference distribution (overestimation) was 6.4%; for NPV it was up to 213.7 million PLN and for EBITDA up to 678.6 million PLN in comparison to the reference scenario, or possibly even more. The results achieved are influenced by optimistic estimates of the thickness of Coal partings, the amount of Coal impurities and finally, lower costs of waste rock handling and Coal production, despite the unfavorable values of the Coal-quality parameters. The total cost of waste rock management underground and Coal processing should be of special interest in the assessment process. These observations led to the general conclusion that interpretation of the geological information at the early stages of the exploration based only on surface boreholes, can lead to overestimating the potential value of the Coal reserves and hence to significant errors. It is worth mentioning that the aspects of geological appraisals presented above are commented upon in the professional literature, but elaborations regarding the influence of geological information in the resource economic evaluation are very limited. To strengthen the results achieved, there is a need to continue the research and verify the results obtained on other examples of Coal mines.

  • Economic valuation of Coal deposits – The value of geological information in the resource recognition process
    Resources Policy, 2019
    Co-Authors: Michal Kopacz, Jaroslaw Kulpa, Dominik Galica, Artur Dyczko, Jacek Jarosz
    Abstract:

    Abstract Resource and economic assessment under uncertainty and risk is quite a difficult process. Many geological, mining, technical and economic aspects should be taken in to consideration simultaneously. The assessment process is also subject to errors, including measurement and interpretation errors and errors resulting from lack of knowledge or over-optimism. Uncertainty about the true value of the geological parameters is reduced as the recognition process and the knowledge about deposits improve. Especially at the first stages of mine development, the information available is insufficient and can lead to incorrect interpretation. In this paper, the impact of geological information on key economic parameters was examined, using the example of a hard Coal deposit in mine X in Poland. A scenario analysis was performed, based on a Monte Carlo simulation. The simulation was conducted using ogive distributions and copulas (empirical copulas) binding six geological parameters (Coal and waste rock thickness, Coal Density, Coal calorific value (LHV) and sulfur and ash content). To achieve the research purposes, the most popular economic measures (indicators), i.e., IRR, NPV and EBITDA, were used. Finally, differential distributions of these parameters were built and commented upon. The three analyzed scenarios were elaborated considering the data from the geological 3D model of Coal deposit X. A polygon (testing) area was defined in the Coal field, where complete geological information was available. The first data set (scenario 1) incorporated only information from deep surface boreholes (drillings), the second included data from surface drillings and channel samples and the third used only channel sampling. To interpolate the geological data between boreholes, the inverse distance interpolator was applied. Significant differences in the values of selected economic parameters were obtained; the largest in scenario 1. The average value of the IRR difference distribution (overestimation) was 6.4%; for NPV it was up to 213.7 million PLN and for EBITDA up to 678.6 million PLN in comparison to the reference scenario, or possibly even more. The results achieved are influenced by optimistic estimates of the thickness of Coal partings, the amount of Coal impurities and finally, lower costs of waste rock handling and Coal production, despite the unfavorable values of the Coal-quality parameters. The total cost of waste rock management underground and Coal processing should be of special interest in the assessment process. These observations led to the general conclusion that interpretation of the geological information at the early stages of the exploration based only on surface boreholes, can lead to overestimating the potential value of the Coal reserves and hence to significant errors. It is worth mentioning that the aspects of geological appraisals presented above are commented upon in the professional literature, but elaborations regarding the influence of geological information in the resource economic evaluation are very limited. To strengthen the results achieved, there is a need to continue the research and verify the results obtained on other examples of Coal mines.

Jianzhong Wang - One of the best experts on this subject based on the ideXlab platform.

  • Effects of Variogram Characteristics of Coal Permeability on CBM Production: A Case Study in Southeast Qinshui Basin, China:
    Energy Exploration & Exploitation, 2014
    Co-Authors: Fengde Zhou, Jianzhong Wang
    Abstract:

    The Coalbed methane (CBM) resources of China are located mainly in 9 basins, Ordos, Qinshui, Jungar, Diandongqianxi, Erlian, Tuha, Tarim, Tianshan and Hailaer. Qinshui Basin, one of the richest CBM basins in China, has boosted its annual CBM production to 3 × 109 m3 (106 Bcf). The Coal seams in Qinshui Basin are significant with high gas content but strong heterogeneous permeabilities ranging from 0.1 to 10 mD. This paper investigates the effects of spatial distribution characteristics of Coal permeability on CBM production. The study area is the South Shizhuang CBM district, Southeast of Qinshui Basin. The distributions of porosity, ash content, Coal Density and gas content of the Coal seam are generated using sequential Gaussian simulation (SGS) with only one realisation because this paper only justifies the effects of Coal permeability on CBM production. The permeabilities of 17 wells are determined by matching these wells' water and gas production with bottom-hole pressure as constraint. Then, the dis...

  • Stochastic modelling of Coalbed methane resources: a case study in Southeast Qinshui Basin, China
    International Journal of Coal Geology, 2012
    Co-Authors: Fengde Zhou, Jianzhong Wang, Guy Allinson, Dehua Xiong, Yildiray Cinar
    Abstract:

    This paper presents a stochastic analysis of Coalbed methane (CBM) resources for a Coal seam in southeast Qinshui Basin, China. Log and laboratory data are used to predict the Coal thickness, Coal ash content and Coal gas content. Using reservoir modelling, the distributions of the karst collapse column (KCC), Coal seam thickness, Coal quality and Coal gas content are generated. The convergent interpolation and sequential Gaussian simulation methods are used to model the surface and structure of the Coal seam. The structural models are determined by the surface structure and Coal seam thickness. Based on the structural models, the Coal and KCC are converted to two facies and their distributions are obtained using object modelling. The Coal Density distributions are simulated based on each facies model using the sequential Gaussian simulation. Finally, different realisations are used to study CBM resources. The results show that the heterogeneities in Coal seam thickness and Coal quality lead to significant uncertainty in estimating CBM resources. A model with lower heterogeneity gives a greater CBM resource. The distributions of KCC, Coal seam thickness, Coal quality and gas content are the main sources for uncertainty in CBM resource estimation. The Density variogram and top structure contribute less to the uncertainty in CBM resources.

Michal Kopacz - One of the best experts on this subject based on the ideXlab platform.

  • economic valuation of Coal deposits the value of geological information in the resource recognition process
    Resources Policy, 2019
    Co-Authors: Michal Kopacz, Jaroslaw Kulpa, Dominik Galica, Artur Dyczko, Jacek Jarosz
    Abstract:

    Abstract Resource and economic assessment under uncertainty and risk is quite a difficult process. Many geological, mining, technical and economic aspects should be taken in to consideration simultaneously. The assessment process is also subject to errors, including measurement and interpretation errors and errors resulting from lack of knowledge or over-optimism. Uncertainty about the true value of the geological parameters is reduced as the recognition process and the knowledge about deposits improve. Especially at the first stages of mine development, the information available is insufficient and can lead to incorrect interpretation. In this paper, the impact of geological information on key economic parameters was examined, using the example of a hard Coal deposit in mine X in Poland. A scenario analysis was performed, based on a Monte Carlo simulation. The simulation was conducted using ogive distributions and copulas (empirical copulas) binding six geological parameters (Coal and waste rock thickness, Coal Density, Coal calorific value (LHV) and sulfur and ash content). To achieve the research purposes, the most popular economic measures (indicators), i.e., IRR, NPV and EBITDA, were used. Finally, differential distributions of these parameters were built and commented upon. The three analyzed scenarios were elaborated considering the data from the geological 3D model of Coal deposit X. A polygon (testing) area was defined in the Coal field, where complete geological information was available. The first data set (scenario 1) incorporated only information from deep surface boreholes (drillings), the second included data from surface drillings and channel samples and the third used only channel sampling. To interpolate the geological data between boreholes, the inverse distance interpolator was applied. Significant differences in the values of selected economic parameters were obtained; the largest in scenario 1. The average value of the IRR difference distribution (overestimation) was 6.4%; for NPV it was up to 213.7 million PLN and for EBITDA up to 678.6 million PLN in comparison to the reference scenario, or possibly even more. The results achieved are influenced by optimistic estimates of the thickness of Coal partings, the amount of Coal impurities and finally, lower costs of waste rock handling and Coal production, despite the unfavorable values of the Coal-quality parameters. The total cost of waste rock management underground and Coal processing should be of special interest in the assessment process. These observations led to the general conclusion that interpretation of the geological information at the early stages of the exploration based only on surface boreholes, can lead to overestimating the potential value of the Coal reserves and hence to significant errors. It is worth mentioning that the aspects of geological appraisals presented above are commented upon in the professional literature, but elaborations regarding the influence of geological information in the resource economic evaluation are very limited. To strengthen the results achieved, there is a need to continue the research and verify the results obtained on other examples of Coal mines.

  • Economic valuation of Coal deposits – The value of geological information in the resource recognition process
    Resources Policy, 2019
    Co-Authors: Michal Kopacz, Jaroslaw Kulpa, Dominik Galica, Artur Dyczko, Jacek Jarosz
    Abstract:

    Abstract Resource and economic assessment under uncertainty and risk is quite a difficult process. Many geological, mining, technical and economic aspects should be taken in to consideration simultaneously. The assessment process is also subject to errors, including measurement and interpretation errors and errors resulting from lack of knowledge or over-optimism. Uncertainty about the true value of the geological parameters is reduced as the recognition process and the knowledge about deposits improve. Especially at the first stages of mine development, the information available is insufficient and can lead to incorrect interpretation. In this paper, the impact of geological information on key economic parameters was examined, using the example of a hard Coal deposit in mine X in Poland. A scenario analysis was performed, based on a Monte Carlo simulation. The simulation was conducted using ogive distributions and copulas (empirical copulas) binding six geological parameters (Coal and waste rock thickness, Coal Density, Coal calorific value (LHV) and sulfur and ash content). To achieve the research purposes, the most popular economic measures (indicators), i.e., IRR, NPV and EBITDA, were used. Finally, differential distributions of these parameters were built and commented upon. The three analyzed scenarios were elaborated considering the data from the geological 3D model of Coal deposit X. A polygon (testing) area was defined in the Coal field, where complete geological information was available. The first data set (scenario 1) incorporated only information from deep surface boreholes (drillings), the second included data from surface drillings and channel samples and the third used only channel sampling. To interpolate the geological data between boreholes, the inverse distance interpolator was applied. Significant differences in the values of selected economic parameters were obtained; the largest in scenario 1. The average value of the IRR difference distribution (overestimation) was 6.4%; for NPV it was up to 213.7 million PLN and for EBITDA up to 678.6 million PLN in comparison to the reference scenario, or possibly even more. The results achieved are influenced by optimistic estimates of the thickness of Coal partings, the amount of Coal impurities and finally, lower costs of waste rock handling and Coal production, despite the unfavorable values of the Coal-quality parameters. The total cost of waste rock management underground and Coal processing should be of special interest in the assessment process. These observations led to the general conclusion that interpretation of the geological information at the early stages of the exploration based only on surface boreholes, can lead to overestimating the potential value of the Coal reserves and hence to significant errors. It is worth mentioning that the aspects of geological appraisals presented above are commented upon in the professional literature, but elaborations regarding the influence of geological information in the resource economic evaluation are very limited. To strengthen the results achieved, there is a need to continue the research and verify the results obtained on other examples of Coal mines.

Weina Li - One of the best experts on this subject based on the ideXlab platform.

  • wetting process and surface free energy components of two fine liberated middling bituminous Coals and their flotation behaviors
    Powder Technology, 2013
    Co-Authors: Weina Li
    Abstract:

    Abstract The hydrophobicity of bituminous Coal surface has a significant effect on the separation of Coal and mineral matter during flotation process. In this paper, the thermodynamic characterization of the two kinds of liberated fine middling bituminous Coal measured by Washburn dynamic method and its influence on flotation have been investigated. Wetting rate and Lipophilic Hydrophilic Ratio (LHR) was calculated by linear regression analysis method according to the wetting process of different Density fractions of Coal samples wetted by n -hexane, α -bromonaphthalene, formamide and water. Washburn equation and Van Oss–Chaudhury–Good theory were used to estimate the surface free energy components of samples. The LHR value of Xiqu (XQ) Coal is higher than that of Qianjiaying (QJY) Coal, and it decreases with the Density level. Density fraction of 1.5–1.6 g·cm − 3 and 1.6–1.8 g·cm − 3 for XQ Coal is hydrophobic with similar wetting rate and LHR (7.18 and 6.58), which is consistent with its poor selectivity from the regressive release flotation test. Disperse part of surface free energy reduces slightly with the Coal Density increase; the base part of Coal is increased and all lower than that of the hydrophobic kaolinite (58.27 mN·m  −1 ). Furthermore, there is a corresponding relationship between the base part and LHR. Results of elemental analysis and FTIR coincide well with the Coal surface property.