Showing papers by "Anirbid Sircar published in 2021"

Production of biodiesel from high free fatty acid feedstock using heterogeneous acid catalyst derived from palm-fruit-bunch

Abstract: A novel solid acid catalyst was prepared from the palm-fruit-bunch by in-situ incomplete sulphonation carbonization method. The prepared catalyst was characterized to determine its physical and che...

Geothermal energy provinces in India: A renewable heritage

Abstract: Increasing demand of energy leads India to switch from fossil fuel to renewable energy sources. Geothermal energy is one such source of energy which is getting explored with time. Literature survey show that geothermal energy is nascent stage in India. Exploration of geothermal survey started long back but exploitation of the same is limited. However, with at the preliminary stage Geological Survey of India has found around 340 hot springs across India. These hot springs are categorised into five major classes based on their tectonic features. This paper reviews a complete review of the works carried out in India for geothermal energy by various organizations. It highlights the different geothermal potential zones in India. It also talks about the temperature conditions of these potential zones along with geochemistry. The government organisation like GSI, NGRI and MNRE are the leading bodies for exploration and exploitation of geothermal in India. The aim of this paper is to promote geothermal energy in India for societal benefits. The paper discusses about the local development and use of geothermal water in various regions of India. It also narrates that how the geothermal water can be used for balneology, power generation, space cooling and heating, crop drying, honey processing etc. for societal development.

Feedforward Neural Network for joint inversion of geophysical data to identify geothermal sweet spots in Gandhar, Gujarat, India

Abstract: Artificial Neural Networks (ANNs) are used in numerous engineering and scientific disciplines as an automated approach to resolve a number of problems. However, to build an artificial neural network that is prudent enough to rely on, vast quantities of relevant data have to be fed. In this study, we analysed the scope of artificial neural networks in geothermal reservoir architecture. In particular, we attempted to solve joint inversion problem through Feedforward Neural Network (FNN) technique. In order to identify geothermal sweet spots in the subsurface, an extensive geophysical studies were conducted in Gandhar area of Gujarat, India. The data were acquired along six profile lines for gravity, magnetics and magnetotellurics. Initially low velocity zone was identified using refraction seismic technique in order to set a common datum level for other potential data. The depth of low velocity zone in Gandhar was identified at 11 m. The FNN backpropagation method was applied to gain the global minima of the data space and model space as desired. The input dataset fed to the inversion algorithm in the form of gravity, magnetic susceptibility and resistivity helped to predict the suitable model after network training in multiple steps. The joint inversion of data is conducive to understanding the subsurface geological and lithological features along with probable geothermal sweet spots. The results of this study show the geothermal sweet spots at depth ranging from 200 m to 300 m. The results from our study can be used for targeted zones for geothermal water exploitation.

Analysis of wellbore stability by pore pressure prediction using seismic velocity

Abstract: Borehole problems result in the wastage of huge expenditures in petroleum industry. One of the major challenges is to keep the borehole intact at a particular location and prevent it from falling in reactive shale in high- or low-pressure zones. To convert seismic velocity to pore pressure, the modified Eaton's equation is widely used. A guideline was prepared using both stochastic and non-uniform parameter distributions. Pore pressure and fracture pressure were calculated using a modified version of Eaton's and Mathew and Kelly's methods. To validate the results, geomechanical models were developed based on good correlation. Different regions in the eastern state of India at Tripura were taken into consideration and pore pressure was estimated for the regions of Kathalchari and Ambassa by plotting pressures. The actual pore pressure predicted in Tripura was calculated using seis P and seis P3, the indigenous software which is a modified version of Eaton's method and Mathew and Kelly's method, and the pore pressure from our method matches the calculated Repeat Formation Test (RFT) data from wells.

Geochemical characterization of geothermal spring waters occurring in Gujarat, India

Abstract: Geothermal waters are valuable natural resources for direct uses, societal benefits, and industrial uses. Geochemical classification of physicochemical parameters of seven geothermal sources at Gujarat was evaluated in order to improve and identify their possible applications. The analysis of geothermal spring water was performed using standard methods. Metal elements were determined by atomic absorption spectroscopy (AAS). The waters were categorized as Na + K and Cl + SO4 based on results obtained from Piper, Durov, and Stiff analysis. The Tuwa, Dholera, and Lasundra thermal waters had relatively high salinity values exceeding 5000 mg/l. This study gave an insight into the origin of water which could be due to the interaction of underground water with granitic basement/plutons. Due to high total dissolved solvent (TDS) values in places like Dholera, Tuwa, and Lasundra, the water cannot be used for daily life purposes like bathing, drinking, irrigation, etc. while in places like Tulsishyam, Savarkundala, Unai and Lalpur the TDS content was such that it can be used for daily uses.

Stochastic modeling of earth’s hydrocarbon resources using artificial neural network

Abstract: Modeling of earth’s hydrocarbon resource and reserve is a complex phenomenon. Resource/reserve can be estimated using both deterministic and stochastic methods. However, according to the guidelines prescribed by Project resource management system (PRMS), stochastic method is a better approach. In this paper, a stochastic approach has been described which uses the power of two-layered Artificial neural network (ANN) technique. One layer is the input layer and the other one is a hidden layer. In resource estimation, six such input layers were considered, namely, area of hydrocarbon pool, pay thickness of hydrocarbon reservoir, saturation of hydrocarbon reservoir, Formation Volume Factor and Recovery Factor. The output is the model describing the stochastic range of hydrocarbon resource/reserve. Training of the network is performed with 100 random data value of each input reservoir parameter. 90% of the data have been used for training and remaining 10% for validating the network. The deterministic calculation acts as a target for stochastic inversion of data. The model performs best when quality data are fed during training. Mean square error was calculated which is the average of squared difference between normalized outputs and targets. Any value of error over 0.6667 signifies high error. Probability was assigned to the output layer ranges. Two pay zones were considered to demonstrate the efficacy of the system. Prospective recoverable resource with minimum (1P), most likely (2P) and maximum (3P) were determined and presented in this paper.

Genetic-based Monte Carlo Modeling of geothermal prospects: Indian examples

Abstract: Modeling reserves of geothermal reservoirs in Gujarat and Maharashtra in India are in nascent stage. Prognosis of reserve estimate for these shallow prospects is attempted for the first time. Genetic-based Monte Carlo Modeling was used to carry out the systematic investigation. Input parameters required for the model are area of the thermal reservoir, specific heat of the rock, specific heat of reservoir water and average annual surface temperature. Range of values for each parameter were collected using geophysical and logging surveys. Gravity and Magnetic surveys were utilized to have a range of values for prospect closure area, porosity logs were utilized for porosity ranges for reservoirs. Temperature probes were carried out in 19 locations, 17 in Gujarat and 2 in Maharashtra. Specific heat of water was collected from the reports of abandoned hydrocarbon wells. The distribution of values for each parameter was found to be triangular or log normal. The modeling algorithm then defined a relationship between input and output parameters. Genetic-based steps such as random population creation, encoding, crossover, mutations and decoding were performed to assign probability to likely outcome of reserve. The last step of the model involved analyzing the results of the calculation and providing a probability distribution function for each result. Heat capacities of places like Shirdi, Saputara and Harsani were found to be highly encouraging.

A comprehensive study on modeling methods for gauging of resources in a geothermal reservoirs

Abstract: Resource assessment and reserve estimation plays a critical role in the economic development of the geothermal field. Various methods are available for resource assessment. Most studies of resource assessment highlight the statistical residual value, showing the inaccuracy of the implemented procedure in estimation. These shortcomings are due to various reservoir parameters such as hot fluid volume in place, recharging rate of the natural fluid in the reservoir, economical rate of extraction, and program to develop the field during this beginning stage. The present study discusses the applicability of various geothermal resource estimation methods and compares various methods for assessing geothermal resources. The study brings out different techniques used for resource estimation and also categorizes them in the context of reliable applicability in geothermal resource estimation. Based on the comparative analysis, suitable applications for several methods have been identified.

Modelling of earth’s geothermal subtle traps using gravity Euler deconvolution

Abstract: Euler deconvolution technique is one of the methods that predicts the subsurface features and structures with help of gravity modelling. The present study was performed to identify the earth’s geothermal potentials in Dholera, Unai, and Gandhar regions of Gujarat, India using gravity technique. The structures and layers of the subsurface were determined by performing the gravity survey and the interpretation of data was carried out using Euler Deconvolution. The survey was conducted along six profile lines: five horizontal and one perpendicular to others in the study areas. After acquiring gravity data, various corrections were applied to convert raw gravity data to corrected Bouguer gravity data. In this paper density of the subsurface formation has been determined using Nettleton and Parasnis methods, which suggests that the subsurface of Dholera, Unai, and Gandhar have densities close to sedimentary rocks. After density determination, regional and residual separation was performed on the Bouguer gravity data to get information on geothermal causative bodies. In this paper, the Euler Deconvolution method was applied to interoperate the spatial position and depth of the subtle geothermal bodies. The Euler solutions for depth in Dholera, Unai, and Gandhar range between 1324–4300 m, 1877–4813 m, and 2345–5536 m. The results of gravity Euler Deconvolution suggests the presence of geothermal potential in these regions.

Geothermal Energy and Climate Change Mitigation

Abstract: The energy sector in the world needs major changes for reduction in greenhouse gas emission and climate change mitigation. It has been believed that with the current rate in increase of greenhouse gas emissions a rise in sea levels will occur with changes in global climate patterns. These effects hinder adaptation for climate change mitigation, so it is important to incorporate and understand the models implemented in the energy sector for decarbonisation. According to this perspective, the geothermal resource is contemplated as being one of the most productive energy sources that can be adopted for control of greenhouse gas emissions. The purpose of this study is to understand the global status of geothermal energy and its important role in climate change mitigation. In this chapter it is seen how geothermal energy has been used for centuries for heat extraction and power generation in geothermal countries such as the USA, New Zealand, Iceland, Kenya, and the Philippines, with minimal greenhouse gas emission with respect to fossil fuels. In the present study, the GeoAdam method of geothermal climate change mitigation is also discussed and modified in terms of scenario setup, assessment stage, and implementation stage. This method can be used for the climate change mitigation process in India with geothermal energy. The life cycle assessment of a system, which is an important factor to understand and control greenhouse gas emission, is reviewed in detail here. The present study provides a complete guide for climate change mitigation with geothermal energy.

A heuristic modeling of solar-assisted earth’s geothermal power production capacity

Abstract: Geothermal energy is in nascent stage in India. Extracting energy from earth’s interior and converting it into power are a general practice in countries like Kenya, New Zealand, Tanzania, Mexico, Iceland, etc. India has low enthalpy subsurface regime. Power generation from earth’s internal heat is limited in India. The present study deals with a heuristic modeling technique where solar energy is hybridized with geothermal energy. To enhance enthalpy and in turn elevate power generation concentrated solar trough (CST) device is utilized. Thermodynamic models are prepared for CST-geothermal setup. CST is hybridized with geothermal energy both in series and parallel modes. The study also suggests thermal energy storage methods for uninterrupted power supply in night. The cost of the hybrid plant is 25–50% lesser compared to photovoltaic plants. The economic analysis of the proposed plant is evaluated based on energy and exergy models.