1,720,971 research outputs found
Numerical investigations and evaluation of a puga geothermal reservoir with horizontal wells using a fully coupled thermo-hydro-geomechanical model (THM) and EDAS associated with AHP
No full textPuga geothermal reservoir in India gives promising results for the extraction of geothermal energy. On the other hand, no systematic studies were conducted to analyze the Puga geothermal reservoir's thermo-hydro-geomechanical behavior while heat extraction. Most of the researchers concentrated more on the geological and geophysical aspects of the Puga hot spring and provided basic geological data. This information is now being examined as input towards the construction of geothermal reservoirs. In this study, the THM model is enhanced by including additional dynamic fluid, rock, and fracture features, and an integrated assessment approach is anticipated to improve the heat extraction possibilities of the Puga geothermal reservoir. The variation in rock temperature in the vicinity of the production well is influenced less compared to the vicinity of the injection well. The low-temperature region is initially befalling in the vicinity of hydraulic fractures with high conductivity, flow rate, and strong heat convection further it extends to the rock matrix. Twelve injection-production scenarios were proposed for the Puga geothermal reservoir to extract heat energy with single and dual production wells and single injection well. Scenario-2 and scenario-8 are significantly showing high production temperatures when compared to other proposed scenarios. A multi-index-based evaluation system that consists of geothermal reservoir life, thermal breakthrough time, reservoir impedance, heat power, and heat recovery is employed to examine the geothermal extraction from the Puga geothermal reservoir. An integrated evaluation technique was projected for geothermal extraction which is created by coupling the analytical hierarchy process (AHP) and evaluation based on distance from average solution (EDAS) techniques. The performance of twelve injection-production scenarios of the Puga geothermal reservoir are evaluated using the integrated evaluation technique. The results show that scenario-11 and scenario-9 were found to be the top two scenarios for the extraction of heat from the Puga geothermal reservoir. The results demonstrate that the proposed integrated evaluation system and improved THM model can be used for the evaluation of geothermal reservoirs effectively.Manojkumar Gudala, Zeeshan Tariq, and Bicheng Yan thanks for the Research Funding from King Abdullah University of Science and Technology (KAUST), Saudi Arabia through the grants BAS/1/1423-01-01 and FCC/1/4491-22-01; Manojkumar Gudala and Shuyu Sun thanks for the Research Funding from King Abdullah University of Science and Technology (KAUST), Saudi Arabia through the grants BAS/1/1351-01-01 and URF/1/4074-01-01; Manojkumar Gudala and Suresh Kumar Govindarajan gratefully acknowledge financial support from the Indian Institute of Technology–Madras
Robust optimization of geothermal recovery based on a generalized thermal decline model and deep learning
No full textGeothermal reservoir simulation often considers the coupled thermo-hydro-mechanical physics, so the computational cost is remarkably expensive, which brings challenges for rapid reservoir optimization for geothermal management. In this work, we developed a parsimonious thermal decline model with only 3 parameters, namely
model. It can accurately predict the produced fluid temperature behavior in geothermal recovery, which captures both the early thermal breakthrough and the later decline behavior. Further, a forward surrogate model based on deep neural network is developed to map the reservoir parameters to the
model parameters and the ultimate total net energy. The forward model is integrated with a multi-objective optimizer (MOO) based on Non-dominated Sorting-based Genetic Algorithm II (NSGA-II), which considers reservoir uncertainties of rock properties and subjects to nonlinear engineering constraints for robust reservoir optimization. The
model is validated through processes including enhanced geothermal recovery (EGS) and geothermal recovery from hot sedimentary aquifers (HSA) without fracturing. The mean relative error of the
model is less than 1%. We also examined the deep neural network to predict 4 parameters including the total energy and 3
model parameters in EGS, with decent
scores 0.998, 0.998, 1.000 and 0.946, respectively. The MOO converges well to achieve the optimum total energy, and solutions with different (low, median, high) risk levels are consistent with the results based on reservoir simulation. The decision variables including injection temperature and rate, extraction well pressure and well distance are provided based on the MOO framework. The number of forward model evaluations during optimization is 20000, and the average CPU time of MOO based on the forward surrogate model is 28.32 s, while the optimization based simulation is estimated to be around 600 min. Therefore, the newly proposed workflow is highly scalable and ready for field or regional scale geothermal optimization.Bicheng Yan, and Manojkumar Gudala thanks King Abdullah University of Science and Technology (KAUST), Saudi Arabia for the Research Funding through the grants FCC/1/4491-22-01 and BAS/1/1423-01-01; Shuyu Sun thanks for the Research Funding from King Abdullah University of Science and Technology (KAUST), Saudi Arabia through the grants BAS/1/1351-01-01 and URF/1/4074-01-01
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Physics-informed machine learning for noniterative optimization in geothermal energy recovery
No full textGeothermal energy is clean, renewable, and cost-effective and its efficient recovery management mandates optimizing engineering parameters while considering the underpinning physics, typically achieved through computationally intensive simulators. This study proposes a novel physics-informed machine learning (PIML) framework for geothermal reservoir optimization, integrating a data wrangler to process high-fidelity simulations, a forward network for forward predictions, and a control network to optimize engineering decision parameters while maximizing the objective function and satisfying various engineering constraints. The PIML incorporates an improved Hyperbolic-ReLU (HyperReLU) model to predict the produced geothermal fluid temperature robustly. The forward model uses a neural network to predict hyper-parameters of HyperReLU from reservoir model input and estimates the produced fluid temperature and energy. Further, the control network is trained with labels automatically generated by the forward model. During prediction, it can infer optimum decision parameters noniteratively by inputting uncertain reservoir parameters, ensuring it maximizes the objective function. Numerical experiments reveal that the HyperReLU enhances long-term predictive stability, and the forward network can achieve predictions of the produced temperature and energy within errors of 0.53 ± 0.46% and 0.60 ± 0.74%, respectively. We examine PIML to control the produced temperature drops or maximize the total energy recovery. Compared to the differential evolution (DE) optimizer, PIML closely matches DE with a 53.7% increase in total energy while running 5,465 times faster than DE. Moreover, PIML presents great efficiency and accuracy and is scalable for field-scale geothermal well-control design and other similar optimization problems.Bicheng Yan, and Manojkumar Gudala thanks the King Abdullah University of Science and Technology (KAUST) for the research funding through grants FCC/1/4491-22-01 and BAS/1/1423-01-01. Shuyu Sun, and Manojkumar Gudala appreciate the research funding from KAUST, Saudi Arabia through the grants BAS/1/1351-01-01 and URF/1/4074-01-01
Fully Connected Neural Network Model for Fractured Geothermal Reservoir using Supercritical-CO2 as Geofluid with THM Model
No full textIn the present work, a thermo-hydro-mechanical (THM) model was utilized to examine the behaviour of fractured geothermal reservoir with supercritical-CO2 (SCCO2) as geofluid. The impact of natural fractures, orientation, and their interaction with hydraulic fractures on the extraction of heat and extension of injection fluid is examined. The development of thermal strain occupied regions were recognized significantly in the vicinity of fracture and injection well. The comparison between water-enhanced geothermal system (EGS) and SCCO2-EGS on the production temperature, thermal strain, and mechanical strain are performed. Injection temperature, injection/production (inj/prod) velocity, aperture of hydraulic fracture (HF), and HF length in a fractured geothermal reservoir are considered as primary control parameters and used as the inputs for the hybrid neural networks and time series models to predict the temperature at the production well. The fully connected neural network (FCN) model shows better predictions based on the loss functions. A mathematical equation is developed using the FCN model to predict the production temperature. Thus, the proposed system of numerical investigations with integrated FCN model could be a benefit in studying the temporal behavior of production temperature.Manojkumar Gudala, Zeeshan Tariq, Zhen Xu and Bicheng Yan thanks for the Research Funding from King Abdullah University of Science and Technology (KAUST), Saudi Arabia through the grants BAS/1/1423-01-01 and FCC/1/4491-22-01; Manojkumar Gudala and Shuyu Sun thanks for the Research Funding from King Abdullah University of Science and Technology (KAUST), Saudi Arabia through the grants BAS/1/1351-01-01 and URF/1/4074-01-01;Manojkumar Gudala and Suresh Kumar Govindarajan gratefully acknowledge financial support from the Indian Institute of Technology–Madras
COMPARISON OF SUPERCRITICAL CO2 WITH WATER AS GEOFLUID IN GEOTHERMAL RESERVOIRS WITH NUMERICAL INVESTIGATION USING FULLY COUPLED THERMO-HYDRO-GEOMECHANICAL MODEL
No full textIn the present work, fully coupled dynamic thermo-hydro-mechanical (THM) model was employed to investigate the advantage and disadvantages of supercritical CO2 (SCCO2) over water as geofluids. Low-temperature zone was found in both SCCO2-EGS and water-EGS systems, but spatial expansion is higher in water-EGS. Although, the spatial expansion of SCCO2 into the rock matrix will help in the geo-sequestration. The expansion of stress and strain invaded zones were identified significantly in the vicinity of fracture and injection well. SCCO2-EGS system is giving better thermal breakthrough and geothermal life conditions compared to the water-EGS system. Reservoir flow impedance (RFI) and heat power are examined, and heat power are high in the water-EGS system. Minimum RFI is found in the SCCO2-EGS system at 45°C and 0.05 m/s. Maximum heat power for SCCO2-EGS was observed at 35°C, 20 MPa, and 0.15 m/s. Therefore, the developed dynamic THM model is having greater abilities to examine behaviour of SCCO2-EGS and water-EGS systems effectively. The variations occur in the rock matrix and the performance indicators are dependent on the type of fluid, injection/production velocities, initial reservoir pressure, injection temperature. The advantages of SCCO2-EGS system over the water-EGS system, providing a promising result to the geothermal industry as geofluid.Manojkumar Gudala and Suresh Kumar Govindarajan gratefully acknowledge financial support from the Indian Institute of Technology–Madras; Manojkumar Gudala and Bicheng Yan thanks for the Research Funding from King Abdullah University of Science and Technology (KAUST), Saudi Arabia through the grants BAS/1/1423-01-01; Manojkumar Gudala and Shuyu Sun thanks for the Research Funding from King Abdullah University of Science and Technology (KAUST), Saudi Arabia through the grants BAS/1/1351-01-01 and URF/1/4074-01-01
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Numerical investigations of the PUGA geothermal reservoir with multistage hydraulic fractures and well patterns using fully coupled thermo-hydro-geomechanical modeling
No full textThe Puga geothermal reservoir is located in the south-eastern part of Ladakh (Himalayan region, India), and it is providing encouraging results towards heat production. We proposed an improved mathematical model for the fully coupled thermo-hydro-geomechanical model to examine the variations in the Puga geothermal reservoir at between 4500 m from the surface with three, four, and seven hydraulic fractures in the reservoir along with four-spot, five-spot, seven-spot, and nine-spot well patterns. The distribution of low-temperature region is found in each fracture, and it is low in the reservoir with seven hydraulic fractures. The changes in the rock and fluid properties are examined effectively. Thermal strain is dominated in the fractures, and mechanical strain is impressive in the rock matrix; it is dependent on the number of hydraulic fractures and well patterns. The thermal performance of the Puga reservoir is examined with the geothermal life, reservoir impedance, and heat power and found that the number of hydraulic fractures and well patterns are influenced significantly in the multistage modeling of the Puga geothermal reservoir. Thus, the proposed mathematical model can effectively evaluate and predict the variations that occur in the Puga geothermal reservoir with dynamic rock, fracture, and fluid properties.Manojkumar Gudala and Suresh Kumar Govindarajan gratefully acknowledge financial support from the Indian Institute of Technology–Madras; Manojkumar Gudala and Bicheng Yan thanks for the Research Funding from King Abdullah University of Science and Technology (KAUST), Saudi Arabia through the grants BAS/1/1423-01-01; Manojkumar Gudala and Shuyu Sun thanks for the Research Funding from King Abdullah University of Science and Technology (KAUST), Saudi Arabia through the grants BAS/1/1351-01-01 and URF/1/4074-01-01
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
- …
