Browsing by Author "Kant R."
Now showing 1 - 9 of 9
Results Per Page
Sort Options
Item Enhancement of CO2 monitoring in the sleipner field (north sea) using seismic inversion based on simulated annealing of time-lapse seismic data(Elsevier Ltd, 2024) Hema G.; Maurya S.P.; Kant R.; Singh A.P.; Verma N.; Singh R.; Singh K.H.The primary aim of this research is to enhance seismic data interpretation and CO2 monitoring by utilizing seismic inversion techniques based on the simulated annealing method. Simulated annealing is a global optimization technique employed for inverting seismic data and provides better results as compared with local optimization-based inversion. This methodology is implemented in the Utsira Formation, located at a depth of 1000 m within the Sleipner Field, Norway. The study encompasses the analysis of three sets of time-lapse seismic data, first from 1994 (pre-injection), followed by surveys in 1999 and 2001, corresponding to the injection of 2.35 million tonnes and 4.26 million tonnes of CO2, respectively. Firstly, synthetic data is used to check the reliability of the algorithm followed by real data application. This process starts by performing the inversion analysis on the synthetic data which shows a decrease in the impedance values observed at the injection site whereas the seismic amplitude increases. The qualitative as well as quantitative analysis depicts that the algorithm works satisfactorily. The same process is applied to the real data from the Sleipner field. Acoustic impedances are calculated using a simulated annealing-based inversion scheme for the pre-injection case in 1994 and post-injection scenarios in 1999 and 2001. Because of the presence of injected CO2 in the years 1999 and 2001, a low impedance zone that ranged from 2000 m/s*g/cc to 2400 m/s*g/cc appeared at the time interval of 0.85�1.10sec. The interpretation of the inverted impedance section and seismic attribute analysis show no signature of CO2 leakage. The results indicated that the inverted section which is derived from the SA optimization technique shows very clear CO2 information offering a more realistic representation with enhanced resolution of the CO2 plume and its migratory paths. � 2024 Elsevier LtdItem Exploring the utility of nonlinear hybrid optimization algorithms in seismic inversion: A comparative analysis(Elsevier Ltd, 2024) Kant R.; Kumar B.; Maurya S.P.; Singh R.; Tiwari A.K.The present study integrates various local and global optimization techniques together to estimate subsurface properties from post-stack seismic data and compare their efficacy qualitatively and quantitatively. Specifically, a local gradient-based optimization method, the quasi-newton method (QNM), is combined with global techniques such as simulated annealing (SA), genetic algorithms (GA), and particle swarm optimization (PSO). These are well-established methods in geophysics. The research compares three global optimization methods (SA, GA, and PSO), their hybrid variants, and QNM for estimating subsurface acoustic impedance. The goal is to assess the trade-offs between solution accuracy and convergence efficiency, offering insights into the strengths and weaknesses of each approach. The objective is to guide the selection of the most effective optimization technique for seismic inversion, balancing quality and computational performance. Both synthetic and real seismic datasets are used to validate the proposed methodology, demonstrating its robust performance across various geological scenarios. Comparative analyses with single global inversion approaches reveal that hybrid optimization methods offer greater accuracy and reliability, positioning them as versatile tools for subsurface characterization. The results indicate that while the hybrid PSO method does not provide significant improvements over single PSO, it extends the convergence time. On the other hand, SA and GA perform adequately, but their hybrid versions considerably enhance solution quality at the cost of longer convergence times. Among the methods, SA shows the fastest convergence to the global solution, followed by GA and PSO. Hybrid SA stands out, delivering superior resolution and faster convergence compared to hybrid PSO and GA. � 2024Item Identification of the reservoir using seismic inversion based on particle swarm optimization method: A case study(Springer, 2024) Kant R.; Kumar B.; Maurya S.P.; Verma N.; Singh A.P.; Hema G.; Singh R.; Singh K.H.; Sarkar P.Accurate reservoir characterization is a crucial step for developing, managing, and optimizing hydrocarbon production. In this current study, we employ particle swarm optimization techniques (PSO) to perform an inversion of post-stack seismic data, extracting information about subsurface acoustic impedance and porosity. Conventional seismic inversion methods predominantly employ local optimization strategies, which often rely on the availability of initial models, particularly in unfamiliar geological settings. In contrast, our approach is based on global search principles, consistently striving to converge towards a global optimal solution, independent of the initial model. To validate the developed technique, we initially subjected it to synthetic data and a wedge model, followed by its application to real data from the Blackfoot field in Canada. The investigation reveals that the inverted results, both for the synthetic and real data, closely align with the observed data. Statistical analysis indicates a high correlation of 0.99 for the synthetic data. For the real data, the correlation remains strong at 0.89. Finally, the PSO-based inversion algorithm is applied across the entire seismic volume, successfully yielding high-resolution subsurface information. This inversion reveals impedance variations ranging from 6500 to 13000 m/s*g/cc, with porosity levels spanning 5�24%, within the Blackfoot region. As per the findings of the investigation, it is evident that the upper section of the subsurface mainly comprises non-solid rock materials. The examination of inverted sections has disclosed an atypical region characterized by low impedance (<9000 m/s*g/cc) and remarkably high porosity (>18%) within the time interval of 1040�1060 ms two-way travel time. This distinctive zone is corroborated by well-log analysis at the same depth and is categorized as a reservoir. � Indian Academy of Sciences 2024.Item Implementing 4D seismic inversion based on Linear Programming techniques for CO2 monitoring at the Sleipner field CCS site in the North Sea, Norway(Springer Science and Business Media Deutschland GmbH, 2024) Singh A.P.; Maurya S.P.; Kant R.; Singh K.H.; Singh R.; Srivastava M.K.; Hema G.; Verma N.This article provides a comprehensive analysis of CO2 injection monitoring in the Sleipner Field. Ensuring the safe storage and containment of CO2 in geological formations or assigned storage sites, especially in the carbon capture and storage (CCS) projects. In this study, a seismic inversion method incorporating linear programming sparse spike inversion was employed to observe and analyze the CO2 plume in the Sleipner field, Norway. This approach enhances the understanding of the dynamics and behavior of the CO2 injection, providing valuable insights into the monitoring and assessment of CCS operations in the Sleipner field. The foundational dataset includes 3D post-stack seismic data from the year 1994, with special emphasis on the monitoring data collected in 1999, following four years of CO2 sequestration. The analysis utilized synthetic data to investigate alterations in seismic amplitude, highlighting that amplitude variations were more prominent compared to variations in velocity and density. The findings highlight noticeable shifts in P-wave velocity, signifying a significant 29% reduction, with the most substantial decrease occurring within the 0 to 30% CO2 saturation range. Correspondingly, density changes align with trace variations, demonstrating only a 2�3% reduction in density as gas saturation increases from 0 to 30%. Beyond 30% saturation, density exhibits a further decrease of 30%. The traces collectively reveal a consistent trend, showcasing a 32% reduction in impedance as CO2 saturation levels rise. Through the cross-equalization process, it was observed that the initial data repeatability was low, indicated by a normalized root mean square (NRMS) value of 0.6508. However, significant improvement was achieved, bringing the NRMS value to a more satisfactory level of 0.5581. This improvement underscored the alignment of features both above and below the reservoir, underscoring the efficacy of the cross-equalization technique. The outcomes of the 4D inversion provided insights into the distribution of CO2 within the reservoir, revealing upward migration. Importantly, the results confirmed the secure storage of CO2 within the reservoir, affirming the integrity of the overlying cap layer. � The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences 2024.Item Integrated thin layer classification and reservoir characterization using sparse layer reflectivity inversion and radial basis function neural network: a case study(Springer Science and Business Media B.V., 2024) Singh R.; Srivastava A.; Kant R.; Maurya S.P.; Mahadasu P.; Verma N.; Hema G.; Kushwaha P.K.; Richa; Singh K.H.; Singh A.P.; Srivastava M.K.; Sarkar P.Understanding subterranean reservoirs, geological characteristics, fluid composition, and hydrocarbon potential strongly relies on precise reservoir characterization. Seismic inversion is a key method in reservoir characterization to approximate the acoustic impedance and porosity of underlying rock formations using seismic and well-log data. A sparse layer reflectivity (SLR) post-stack inversion method approach is used in this study to make thin layers more visible. To generate an impedance volume, it uses a predetermined wavelet library, an objective function, and a regularization parameter, the regularization parameter is a tunable parameter used to control the balance between fitting the data closely (minimizing the misfit) and ensuring a smooth and stable model for and sparseness computed coefficients. This study uses Blackfoot data to estimate the density, velocity, impedance, and porosity of a particular region using the SLR and Radial Basis Function Neural Network (RBFNN). According to the interpretation of the impedance section, a low impedance anomaly zone with an impedance range of (8500�9000) m/s*g/cc is present at a time of (1040�1065) ms. The low impedance zone is classified as a clastic glauconitic sand channel (reservoir zone) based on the correlation between seismic and borehole data. Further, a Radial Basis Function Neural Network (RBFNN) has been applied to the data to estimate porosity volume and to conduct a more thorough examination of the reservoir zone and cross-validate inverted results. The research demonstrates that the high porosity zone, low velocity, and density zone are discovered by the RBFNN technique, and the low impedance zone interpreted in inversion findings are correlating, which confirms the existence of the glauconitic sand channel. This research is crucial for understanding how well SLR, RBFNN, and multi-attribute analysis work to define sand channels. � 2024, The Author(s), under exclusive licence to Springer Nature B.V.Item Looking beyond transactions: decoding the role of service innovation, relationship commitment and fairness in driving customer satisfaction in�retail banking(Emerald Publishing, 2024) Biswas A.; Kant R.; Jaiswal D.Purpose: A significant increase in the number of private sector banks has intensified the level of competition in the Indian banking industry (IBI). This increase in the number of banks has a considerable impact on the existing players, which calls for prioritizing customer satisfaction (CS) and enhancing bank reputation (BR). Our study seeks to investigate the enablers of CS and BR in the IBI. Design/methodology/approach: The study adopted a cross-sectional design for gathering responses from retail bank customers across the selected banks through a structured questionnaire. Structural equation modeling (SEM) was utilized to evaluate direct and indirect linkages among the identified constructs by examining mediating and moderating effects. Findings: The study puts forward crucial antecedents of CS and BR. The findings exhibit that perceived trust (PT) and relationship commitment (RC) magnify CS and BR, respectively, while CS amplifies repurchase intention (RI). The study advances that BR and CS partially mediate between the underlying constructs. In addition, fairness and risk exhibit moderating effects between CS and customer repurchase intention (CRI) and BR and CRI. Research limitations/implications: The study illustrates the crucial enablers of BR, CS and CRI that�may assist banking professionals in enriching customer experience and holding on to their customers. Originality/value: There is a shortage of research on RC, service innovation (SI) and BR in the IBI. Accordingly, our study builds on the prior studies by considering these constructs using a comprehensive conceptual framework by extending the application of signaling theory (ST) in the banking domain and scrutinizing the dual moderating effects of fairness and risk. � 2024, Emerald Publishing Limited.Item Qualitative and quantitative reservoir characterisation using seismic inversion based on global optimization: A comparative case study(Springer, 2024) Kumar B.; Kant R.; Maurya S.P.In this study, the focus is on predicting the properties of rocks beneath the Earth�s surface using global optimisation techniques such as genetic algorithms (GA), simulated annealing (SA) and particle swarm optimisation (PSO). The goal is to minimise the difference (error) between actual seismic data and synthetic (computed) seismic traces. Global optimisation is an approach that is independent of the initial model and aims to identify the global minimum of an objective function. In contrast, local optimisation relies on the accuracy of the initial model, and if an accurate initial model is not provided, it may become trapped in a local minimum, leading to an inaccurate representation of the subsurface model. What makes global optimisation powerful is that it does not get stuck in local minima (suboptimal solutions), but seeks the absolute best solution in the entire search space. This property is crucial in seismic inversion, where finding the most accurate representation of subsurface properties is of utmost importance for geophysical applications. The study includes one synthetic example and one real dataset, with a specific emphasis on evaluating acoustic impedance rock properties. While acoustic impedance is characteristic of rock layers, seismic data represents properties at the interfaces between these layers. Consequently, seismic data is highly valuable for gaining detailed insights into the subsurface. The results of the optimisation process provide exceptionally detailed views of the subsurface, aiding in the interpretation of seismic data. GA, SA and PSO algorithms perform well, both with synthetic data and real data. The inversion process identifies a zone with low acoustic impedance, corresponding to a prominent seismic anomaly. The evaluation of the inverted outcomes reveals that the impedance within the area ranges from 4300 to 4700�m/s*g/cc, situated within a specific time range of 900�950�ms in the seismic data of F3-block, Netherland. � Indian Academy of Sciences 2024.Item Qualitative and quantitative reservoir characterization using seismic inversion based on particle swarm optimization and genetic algorithm: a comparative case study(Nature Research, 2024) Kant R.; Maurya S.P.; Singh K.H.; Nisar K.S.; Tiwari A.K.Accurate reservoir characterization is necessary to effectively monitor, manage, and increase production. A seismic inversion methodology using a genetic algorithm (GA) and particle swarm optimization (PSO) technique is proposed in this study to characterize the reservoir both qualitatively and quantitatively. It is usually difficult and expensive to map deeper reservoirs in exploratory operations when using conventional approaches for reservoir characterization hence inversion based on advanced technique (GA and PSO) is proposed in this study. The main goal is to use GA and PSO to significantly lower the fitness (error) function between real seismic data and modeled synthetic data, which will allow us to estimate subsurface properties and accurately characterize the reservoir. Both techniques estimate subsurface properties in a comparable manner. Consequently, a qualitative and quantitative comparison is conducted between these two algorithms. Using two synthetic data and one real data from the Blackfoot field in Canada, the study examined subsurface acoustic impedance and porosity in the inter-well zone. Porosity and acoustic impedance are layer features, but seismic data is an interface property, hence these characteristics provide more useful and applicable reservoir information. The inverted results aid in the understanding of seismic data by providing incredibly high-resolution images of the subsurface. Both the GA and the PSO algorithms deliver outstanding results for both simulated and real data. The inverted section accurately delineated a high porosity zone (>20%) that supported the high seismic amplitude anomaly by having a low acoustic impedance (6000�8500 m/s? g/cc). This unusual zone is categorized as a reservoir (sand channel) and is located in the 1040�1065 ms time range. In this inversion process, after 400 iterations, the fitness error falls from 1 to 0.88 using GA optimization, compared to 1 to 0.25 using PSO. The convergence time for GA is 670,680 s, but the convergence time for PSO optimization is 356,400 s, showing that the former requires 88% more time than the latter. � The Author(s) 2024.Item Reservoir characterisation using hybrid optimisation of genetic algorithm and pattern search to estimate porosity and impedance volume from post-stack seismic data: A case study(Springer, 2024) Verma N.; Maurya S.P.; Kant R.; Singh K.H.; Singh R.; Singh A.P.; Hema G.; Srivastava M.K.; Tiwari A.K.; Kushwaha P.K.; RichaIn the current study, a seismic inversion based on a hybrid optimisation of genetic algorithm (GA) and pattern search (PS) is carried out. The GA is an approach to global optimisation technique that always converges to the global optimum solution but takes much time to converge. On the other hand, the PS is a local optimisation technique and can converge at local or global optimum solution depending on the starting model. If these two techniques are used together (here termed hybrid optimisation), they can enhance one's benefit and reduce the drawbacks of others. The present study developed a methodology to combine GA and PS in a single flowchart and utilise seismic reflection data exclusively to predict porosity and impedance volume in inter-well regions. The algorithms are initially tested on synthetically created data based on the wedge model, the coal coking model, and the 1D convolution model. The performance of the algorithm is remarkably acceptable, according to the error analysis and statistical analysis between the inverted and the anticipated results. After that, the field post-stack seismic data from the Blackfoot field, Canada, is transformed into impedance and porosity using a developed hybrid optimisation technique. The inverted/predicted sections show very high-resolution subsurface information with impedance varying from 6000 to 14000 m/s�g/cc and porosity varying from 5 to 40% in the region. The error decreases from 1.0 to 0.5 for impedance inversion, whereas it varies from 1.4 to 0.5 for porosity inversion within 3000 iterations, which cannot be achieved by a single optimisation technique. The findings also demonstrated a sand channel (reservoir) anomaly with low impedance (6000�9000 m/s�g/cc) and high porosity (12�20%) in between 1040 and 1060 ms time intervals. This study provides evidence that subsurface parameters like acoustic impedance or porosity may be promptly and affordably determined using seismic inversion based on hybrid optimisation. The developed methodology is very helpful in finding subsurface parameters in a limited time and cost, which cannot be achieved only by global or local optimisation. � Indian Academy of Sciences 2024.
