Browsing by Author "Hema G."
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Item A flowchart for porosity and acoustic impedance mapping using seismic inversion with semi hybrid optimization combining simulated annealing and pattern search techniques(Springer Science and Business Media B.V., 2024) Singh R.; Maurya S.P.; Kumar B.; Verma N.; Tiwari A.K.; Tiwari R.; Hema G.; Singh A.P.Porosity and acoustic impedance are important in the study of subsurface properties of rocks and soil. Porosity is influenced by the type of minerals, and fluids, and their distribution within the subsurface material. Acoustic impedance is a key parameter in seismic inversion because it governs the reflection and transmission of seismic waves at interfaces between different rock layers. Mapping porosity and acoustic impedance using seismic inversion poses several challenges such as low resolution, longer convergence times compared to other optimization techniques, and handling large datasets. To address these challenges, our current study has employed a semi-hybrid optimization approach by incorporating a pattern search (PS) method into the globally recognized simulated annealing (SA) technique. In our devised methodology, seismic data is meticulously inverted, trace by trace, initially utilizing the simulated annealing process and subsequently integrating the pattern search which further reduces computational Complexity. The output from SA serves as the foundation for the PS optimization, preventing it from getting trapped in local minima or maxima. To evaluate the algorithm, we initiated a systematic analysis using synthetic data. The hybrid optimization method performed well, yielding highly accurate inversion results with a remarkable high resolution and correlation between original and inverted impedance. We then applied this approach to actual seismic reflection data from the Blackfoot field in Alberta, Canada. Notably, the inversion identified a sand channel between 1055 and 1070�ms two-way travel time, characterized by low impedance and high porosity, suggesting the potential presence of hydrocarbon reservoirs. The level of performance demonstrated in this context may not be anticipated when utilizing SA or PS optimization alone. Hence, the newly devised semi-hybrid optimization approach emerges as a highly recommended solution, offering the potential to address the constraints of individual optimization methods and deliver thorough subsurface insights. � The Author(s), under exclusive licence to Springer Nature B.V. 2024.Item Comparison of neural networks techniques to predict subsurface parameters based on seismic inversion: a machine learning approach(Springer Science and Business Media Deutschland GmbH, 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.; Singh R.Seismic inversion, complemented by machine learning algorithms, significantly improves the accuracy and efficiency of subsurface parameter estimation from seismic data. In this comprehensive study, a comparative analysis of machine learning techniques is conducted to predict subsurface parameters within the inter-well region. The objective involves employing three separate machine learning algorithms namely Probabilistic Neural Network (PNN), multilayer feedforward neural network (MLFNN), and Radial Basis Function Neural Network (RBFNN). The study commences by generating synthetic data, which is then subjected to machine learning techniques for inversion into subsurface parameters. The results unveil exceptionally detailed subsurface information across various methods. Subsequently, these algorithms are applied to real data from the Blackfoot field in Canada to predict porosity, density, and P-wave velocity within the inter-well region. The inverted results exhibit a remarkable alignment with well-log parameters, achieving an average correlation of 0.75, 0.77, and 0.86 for MLFNN, RBFNN, and PNN algorithms, respectively. The inverted volumes portray a consistent pattern of impedance variations spanning 7000�18000�m/s*g/cc, porosity ranging from 5 to 20%, and density within the range of 1.9�2.9�g/cc across the region. Importantly, all these methods yield mutually corroborative results, with PNN displaying a slight edge in estimation precision. Additionally, the interpretation of the inverted findings highlights anomalous zones characterized by low impedance, low density, and high porosity, seamlessly aligning with well-log data and being identified as sand channel. This study underscores the potential for seismic inversion, driven by machine learning techniques, to swiftly and cost-effectively determine critical subsurface parameters like acoustic impedance and porosity. � The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.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 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 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.
