A dynamic evaluation technique for assessing gas output from coal seams during commingling production within a coalbed methane well: a case study from the Qinshui Basin

Gas drainage is carried out based on output from each coal bed throughout commingling production of coalbed methane(CBM).A reasonable drainage process should therefore initially guarantee main coal bed production and then enhance gas output from other beds.Permanent damage can result if this is not the case,especially with regard to fracture development in the main gas-producing coal bed and can greatly reduce single well output.Current theoretical models and measuring devices are inapplicable to commingled CBM drainage,however,and so large errors in predictive models cannot always be avoided.The most effective currently available method involves directly measuring gas output from each coal bed as well as determining the dominant gas-producing unit.A dynamic evaluation technique for gas output from each coal bed during commingling CBM production is therefore proposed in this study.This technique comprises a downhole measurement system combined with a theoretical calculation model.Gas output parameters(i.e.,gas-phase flow rate,temperature,pressure)are measured in this approach via a downhole measurement system;substituting these parameters into a deduced theoretical calculation model then means that gas output from each seam can be calculated to determine the main gas-producing unit.Trends in gas output from a single well or each seam can therefore be predicted.The laboratory and field test results presented here demonstrate that calculation errors in CBM outputs can be controlled within a margin of 15%and therefore conform with field use requirements.

Estimation of chlorophyll content in maize canopy using wavelet denoising and SVR method

In order to estimate the chlorophyll content of maize plant non-destructively and rapidly,the research was conducted on maize at the heading stage using spectroscopy technology.The spectral reflectance of maize canopy was measured and processed following wavelet denoising and multivariate scatter correction(MSC)to reduce the noise influence.Firstly,the signal to noise ratio(SNR)and curve smoothness(CS)were used to evaluate the denoising effect of different wavelet functions and decomposition levels.As a result,the Sym6 wavelet basis function and the 5th level decomposition were determined to denoise the original signal.The MSC method was used to eliminate the scattering effect after denoising.Then three spectral ranges were extracted by interval partial least squares(IPLS)including the 525-549 nm,675-749 nm and 850-874 nm.Finally,the chlorophyll content estimation model was developed by using support vector regression(SVR)method.The calibration Rc2 of the SVR model was 0.831,the RMSEC was 1.3852 mg/L;the validation Rv2 was 0.809,the RMSEP was 0.8664 mg/L.The results show that the SNR and CS indicators can be used to select the parameters for wavelet denoising and model can be used to estimate the chlorophyll content of maize canopy in the field.

A novel wavelength selection strategy for chlorophyll prediction by MWPLS and GA

The research proposed a novel wavelength selection strategy by the combination of moving window partial least squares(MWPLS)and genetic algorithm(GA)for the chlorophyll content detection of winter wheat canopy using spectroscopy technology.Firstly,the original spectral dataset was pre-processed by wavelet denosing,multiple scatter correction.Then,abnormal data samples were removed by Pauta Criterion and the dataset was divided into modeling set and validation set by SPXY.Finally,the sensitive wavebands were selected using MWPLS method and MWPLS+GA respectively and partial least squares(PLS)models were established for chlorophyll content prediction.For the model established by using all the wavebands in the region of 400-900 nm,its R_(c)^(2) and R_(v)^(2) were 0.4468 and 0.3821 respectively;its modeling root mean square error(RMSEM)and verification root mean square error(RMSEV)were 2.9057 and 1.7589 respectively.For the model established by using 151 wavebands selected by MWPLS,its R_(c)^(2) and R_(v)^(2) were 0.6210 and 0.5901 respectively;its RMSEM and RMSEV were 2.4007 and 1.6408 respectively.For the model established by using 36 wavebands selected by MWPLS+GA,its R_(c)^(2) and R_(v)^(2) were 0.7805 and 0.7497 respectively;its RMSEM and RMSEV were 1.8504 and 1.1315 respectively.The results show that wavelength selection can remove redundant information and improve model performance.The strategy of combining MWPLS with GA has also been proved to work well in selecting sensitive wavebands for chlorophyll content prediction.