Effects of variogram characteristics of coal permeability on CBM production: a case study in Southeast Qinshui Basin, China

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 distributions of coal permeabilities are generated using SGS with a commercial simulator. The history matched permeabilities range from 1.5 to 12 mD with average of 2.9 mD of the 17 wells. Eight variogram models are used to build the distributions of permeability. The cumulative gas productions of two different well-spacing cases, 300 m and 2000 m, are compared. There are 20 realisations of permeability for each of the eight models. The results show that historical matching can be used to obtain the porosity multipliers and the permeabilities in wells. The major direction of variogram has less effect on the uncertainty of field CBM production than variogram range. The effects of variogram range on the uncertainty of CBM production are positive for the case with short well spacing and vice versa for the case with long well spacing

Effect of cap rock thickness and permeability on geological storage of CO2: laboratory test and numerical simulation

Geological storage of CO2 is considered widely as an efficient method of mitigation of greenhouse gas emission. CO2 storage mechanism includes structural trapping, residual gas trapping, solubility trapping and mineral trapping. The shale cap rock acts as a seal for the storage when CO2 accumulates at the top of the reservoir. The injected CO2 may migrate through the cap rock under buoyancy force or pressure build-up which depends on the seal capacity of the cap rock. As a result, the effectiveness of containment of injected CO2 in the reservoir is largely dependent on the migration rate of CO2 through the cap rock. This paper investigates the effects of CO2 leakage through cap rock by a combination of experimental studies and numerical simulation. Firstly, experimental measurements on shale core samples collected from Australian cap rocks were conducted to determine properties, such as capillary pressure, pore size distribution and permeability. Based on the measured cap rock properties, the effect of thickness and permeability of cap rocks on CO2 leakage was studied using a commercial compositional simulator. Experimental results show that the permeabilities of the shale samples measured by transient pulse technique range from 60 to 300 nD; a non-Darcy calibration factor which equals the ratio of the measured permeability divided by 1000, is identified for samples with permeability lower than 1000 nD. Numerical simulation results show that the largest leakage of CO2 through the seal (cap cock) is about 7.0% with seal thickness of 3m and vertical permeability of 90 nD; both shale thickness and permeability affect the CO2 leakage significantly; with a given seal permeability, the leakage rate has a power relationship with shale thickness

Uncertainty quantification of coal seam gas production prediction using Polynomial Chaos

A surrogate model approximates a computationally expensive solver. Polynomial Chaos is a method to construct surrogate models by summing combinations of carefully chosen polynomials. The polynomials are chosen to respect the probability distributions of the uncertain input variables (parameters); this allows for both uncertainty quantification and global sensitivity analysis. In this paper we apply these techniques to a commercial solver for the estimation of peak gas rate and cumulative gas extraction from a coal seam gas well. The polynomial expansion is shown to honour the underlying geophysics with low error when compared to a much more complex and computationally slower commercial solver. We make use of advanced numerical integration techniques to achieve this accuracy using relatively small amounts of training data

Adsorption/desorption characteristics for methane, nitrogen and carbon dioxide of coal samples from Southeast Qinshui Basin, China

This paper presents an experimental and modelling study of the adsorption/desorption of pure gases CH4, CO and N and their binary and ternary mixtures on coal samples obtained from southeast Qinshui Basin, China. Results show that the adsorbed amounts of N, CH4 and CO have approximate ratios of 1.0:1.3:2.4, respectively. No significant hysteresis from adsorption to desorption is observed for pure N and CH4 whereas significant hysteresis is measured for CO in CO -CH4 and CO-CH4-N mixtures and CH4 in the N -CH4 mixture. The experimental observations are modelled using three different models, namely the extended Langmuir (EL), the Langmuir-based ideal adsorbed solution (L-IAS) and the Dubinlin- Radushkevich-based ideal adsorbed solution (D-R-IAS). The models predict well the experimental observations for desorption tests. But the measurements for the low adsorbate capacity in binary and ternary mixtures are overestimated by the prediction models. It is found that the EL model predicts the CO -CH4 desorption test better while the D-R-IAS model is the best model for the CO-CH4- N adsorption

Impact of geological modeling processes on spatial coalbed methane resource estimation

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

Comparison of sequential indicator simulation, object modelling and multiple-point statistics in reproducing channel geometries and continuity in 2D with two different spaced conditional datasets

Fluvial sediments with multi-scale channels are difficult to model using classical two-point statistical methods, e.g. sequential indicator simulation (SIS) or object-based modelling (ObjM). Multiple-point statistics (MPS) has been used to generate facies, fracture and porosity distributions based on pattern statistics derived from training datasets. However, the ability of these three methods to reproduce channel geometry and continuity is not clear, especially when using differently spaced conditional data. This paper presents a case study to compare the application of these three methods in reproducing channels from a section of Amazon River based on two differently spaced conditional data sets. Results show that: the reproduction accuracy is similar between MPS and SIS; MPS provides the most connected channel facies (or most channel continuity) as compared to SIS and ObjM; and using a hand-drawn facies based on the sampling points yield a similar accuracy to that achieved by using the reality facies distribution as the training image. Finally, we conclude that the application of MPS does not significantly increase the reproduction accuracy when compared to SIS channel models; however, MPS can generate realistic models with respect to channel geometry and continuity

Integrative analysis of physiology, biochemistry and transcriptome reveals the mechanism of leaf size formation in Chinese cabbage (Brassica rapa L. ssp. pekinensis)

IntroductionThe leaf, the main product organ, is an essential factor in determining the Chinese cabbage growth, yield and quality.MethodsTo explore the regulatory mechanism of leaf size development of Chinese cabbage, we investigated the leaf size difference between two high-generation inbred lines of Chinese cabbage, Y2 (large leaf) and Y7 (small leaf). Furtherly, the transcriptome and cis-acting elements analyses were conducted.Results and DiscussionAccording to our results, Y2 exhibited a higher growth rate than Y7 during the whole growth stage. In addition, the significant higher leaf number was observed in Y2 than in Y7. There was no significant difference in the number of epidermal cells and guard cells per square millimeter between Y2 and Y7 leaves. It indicated that cell numbers caused the difference in leaf size. The measurement of phytohormone content confirmed that GA1 and GA3 mainly play essential roles in the early stage of leaf growth, and IPA and ABA were in the whole leaf growth period in regulating the cell proliferation difference between Y2 and Y7. Transcriptome analysis revealed that cyclins BraA09g010980.3C (CYCB) and BraA10g027420.3C (CYCD) were mainly responsible for the leaf size difference between Y2 and Y7 Chinese cabbage. Further, we revealed that the transcription factors BraA09gMYB47 and BraA06gMYB88 played critical roles in the difference of leaf size between Y2 and Y7 through the regulation of cell proliferation.ConclusionThis observation not only offers essential insights into understanding the regulation mechanism of leaf development, also provides a promising breeding strategy to improve Chinese cabbage yield

History matching and production prediction of a horizontal coalbed methane well

This paper presents a numerical simulation study that demonstrates history matching and production prediction for an actual horizontal coalbed methane (CBM) well located in Australia. A brief analysis of limited core analysis and well log data is presented. Numerical reservoir simulation is used to carry out a manual history matching to the field data of gas and water production rates and well bottomhole pressure. The matching parameters are porosity, relative permeability and skin factor. The reported field data show that there are sharp changes in the well bottomhole pressure and water and gas rates. This response of the reservoir is matched with a numerical model that has a varying skin factor along the horizontal well. This is deemed reasonable given that the drilling fluid has longer contact with the formation at the heel of the well, causing more formation damage. But the field data indicates that the formation damage is mitigated quickly with production. This is explained by the fact that the invaded mud is forced back during water and gas production and coal shrinkage. The production predictions show that skin factor and coal shrinkage have important effects on the CBM production of a horizontal well. However, the coal formation damage controls the gas rate more than the shrinkage for the examined case study with the assumed coal shrinkage parameters