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Well log analysis and production testing in coal are the initial requirements to judge the prospectivity of a coalbed methane (CBM) reservoir. The process of prospect identification through laboratory studies is accurate although it is time-consuming and expensive. Therefore, we developed a methodology to identify prospective coal seam by establishing multiple regression models of geophysical well log parameters vs. organic/inorganic contents from laboratory-tested core samples for one seam. The Langmuir's equation and methane adsorption isotherm were used to estimation of gas and saturation content by developing a regression model from organic content. Gas and coal contents (ash, moisture, fixed carbon, and volatile matter) were obtained from the subsequent propagation of the established equations to other wells. Gas saturation increased with depth from 60 to 69%. Mapped seam thickness and gas content were in the ranges of 10.0–54.0 m and 6.1–28.2 cc/g, respectively. Overlaying of seam thickness and gas content identified the sweet spots in releasing potential future well locations. Errors within the permissible limit between the predicted and observed values indicate the gas estimation to be reliable. Another application for electro-facies classification was demonstrated by applying multi-resolution graph-based clustering architecture to capture texture parameters from histogram and auto-covariance function in resistivity image log. Determination of lithology by correlation of resistivity image and geophysical well log corroborated with the depositional environment having fining upward formational sequence. Thus, this study helps in estimating proximate components, gas content, and saturation with depth in coal seam for production optimization to better understand its implications on the dewatering and gas production periods in the Bokaro CBM reservoir situated in India. [ABSTRACT FROM AUTHOR]

Road feature extraction from the remote sensing images is an arduous task and has a significant role in various applications of urban planning, updating the maps, traffic management, etc. In this paper, a new band combination (8652) to form a road index (RJ) from OLI multispectral bands based on the spectral reflectance of asphalt, is presented for road feature extraction. The 8652 is converted to road index by normalization. The morphological operators (Top-hat or Bottom-hat) uses on RI to enhance the roads. To sharpen the edges and for better discrimination of features, shock square tilter (SSF), is proposed. Then, an iterative adaptive threshold (lAT) based online search with variational min-max and markov random fields (MRF) model are used on the SSF image to segment the roads and non-roads. The roads are extracting by using the rules based on the connected component analysis. IAT and MRF model segmentation methods prove the proposed index (RJ) able to extract road features productively. The proposed methodology is a combination of saturation based adaptive thresholding and morphology (SATM), and saturation based MRF (SMRF), applied to OLI images of several urban cities of India, producing the satisfactory results. The experimental results with the quantitative analysis presented in the paper. [ABSTRACT FROM AUTHOR]

We have characterized the gas hydrate reservoir at Sites NGHP-02-17 and NGHP-02-19 based on isotropic and anisotropic velocity modeling by utilizing both the wireline (WL) and logging-while-drilling (LWD) data independently. The isotropic study is carried out using the simplified three phase Biot equation (STPBE) with density derived porosity and calculated bulk density of sediments as inputs. The anisotropic study uses the transverse isotropic theory for laminated media by considering a layered reservoir model composed of two components: (i) fractures filled with 100% gas hydrates and (ii) isotropic medium composed of water-saturated sediments. In the anisotropic velocity modeling, the reservoir is modeled by incorporating fractures with varying dip angles from 0° to 90°. We estimated gas hydrate saturations using isotropic and anisotropic velocity analysis at both Sites NGHP-02-17 and NGHP-02-19. In both the cases the LWD data derived isotropic model yielded, what appears to be the most accurate values, an average saturation of 45.8% in the high velocity interval between 269 and 285.5 mbsf at Site NGHP-02-17 and 33.8% in the high velocity interval between 306 and 312 mbsf in NGHP-02-19. • Identified the base of gas hydrate bearing sediments at Sites NGHP-02-17 and NGHP-02-19. • Estimated saturation of gas hydrate using isotropic and anisotropic velocity modeling. • Compared the saturation with pressure core and chlorinity data. [ABSTRACT FROM AUTHOR]

Millions of rural households in India lack access to clean basic lighting which hampers the ability of children to study during dark hours. Over 81 million students are likely to be dependent on kerosene lamps for basic lighting. Despite programmes focusing on disseminating solar lanterns since the last three decades, penetration is limited owing to three main barriers: affordability, absence of after-sales services and unavailability in rural markets. This paper presents the localization-affordability-saturation (LAS) model that addresses these main barriers by bringing together three institutional spheres - government, corporate and NGOs for speedy upscaling. This paper illustrates experiences and challenges that have emerged from field testing of the model through Million Solar Urja Lamp Programme while disseminating 1,000,000 solar study lamps to rural school children residing in more than 10,000 villages in India. Though implemented successfully, applicability of LAS model can be hindered due to dependence on subsidies, bottlenecks in local manufacturing infrastructure, absence of market mechanism and its relevance in providing complete lighting solutions. [ABSTRACT FROM AUTHOR]

Abstract: A reservoir of gas hydrates filling fractures was discovered in the Krishna–Godavari (KG) Basin during the Indian National Gas Hydrate Program (NGHP) Expedition 01 at site NGHP01-10. The existing methods for estimating gas hydrate saturation from seismic data rely on establishing an empirical relation between acoustic impedance and density porosity from well logs assuming isotropic pore-filling gas hydrate. This method, however, yields a misleading saturation for fractured clay-dominated sediments. Here we present a methodology to estimate gas hydrate saturation from seismic data based on geostatistical inversion. It integrates the verticals detail of well log data with the lateral details from seismic data to produce highly detailed estimates of gas hydrate saturation. First, gas hydrate saturation is calculated from P-wave velocities assuming anisotropic distribution at the well site. Then, probability density functions (PDFs) between acoustic impedance and the calculated gas hydrate saturation at the well site are analyzed. A Markov Chain Monte Carlo method is employed to integrate well logs with the seismic data to produce acoustic impedance. Finally, crossplots and histograms at the well site are used to estimate gas hydrate saturations along the seismic line from inverted acoustic impedance. The spatial distribution of gas hydrate varies both laterally and vertically along the line with an average saturation of 22.5%. The estimate matches reasonably with the value at the wells. [Copyright &y& Elsevier]

Highlights: [•] Gas hydrate reported from the Andaman Sea, India after coring and drilling. [•] Geothermal modeling of base of gas hydrate stability zone. [•] Gas hydrate stability thickness map of Andaman Sea. [•] Gas hydrate saturation using rock physics modeling. [•] Seismic attributes (i.e. reflection strength and instantaneous frequency etc.). [ABSTRACT FROM AUTHOR]

Accurate estimates of spatial above ground biomass (AGB) in tropical forests are important in understanding the global carbon cycle. Microwave and optical remote sensing datasets have been used extensively for AGB estimation, but their uses are restricted due to saturation in high biomass region. To overcome saturation issues of single sensor based models, the current paper uses a non-parametric Random Forest based approach to spatially estimate biomass over Indian forests using field inventory data in combination with optical (MODIS) and Microwave (L-band ALOS-PALSAR) images along with other bio-climatic parameters (e.g., rainfall, temperature) which significantly influence the biomass accumulation in an ecosystem. Plot level biomass estimates for 6678 sample plots (0.1 ha size), inventoried as part of robustly designed National Forest Inventory (NFI), are computed using volumetric equations, wood density and biomass expansion factors. Spatial above ground biomass estimates were generated using random forest model over two scenarios. Firstly, a single nation-wide model using all the available plot data and secondly, physiographic zone (14 zones over India) wise models with plot data over respective zones. AGB stored in Indian forest is estimated as 7952.3 million tonnes (Carbon equivalent: 3737.58 TgC) with a root mean square error (RMSE) of 31.2% using national level model. Physiographic zone level models estimated the country's biomass as 7597.45 million tonnes (Carbon equivalent: 3570.8 TgC). The above ground biomass estimates from our study indicates that the estimation error in physiographic zone model varies from 25.24% to 54.15% depending upon the sample size and biomass range. We observed that L-band microwave saturated in 140–160 Mg ha−1 range and currently available microwave wavelengths alone is not sufficient to predict entire range of biomass over Indian forests. Inclusion of multisource data using random forest regression model increase the saturation range to 350 Mg ha−1 which is a significant improvement as 94.7% of Indian forests are covered in this range. Model estimation error reduces to 25.6% in AGB range up to 350 Mg ha−1. • L-band ALOS PALSAR backscatter saturates in biomass range140–160 Mg ha−1. • Inclusion of multi source data improved the saturation point to ~350 Mg ha−1. • 94.7% of Indian forest cover is covered in AGB range below 350 Mg ha−1. • Biomass stored in Indian forest estimated as 7952.3 Mt. (carbon equivalent: 3737.58 TgC). [ABSTRACT FROM AUTHOR]