Browsing by Author "Brijesh M.N. Kumar"

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Advancing post-stack seismic inversion through music-inspired harmony search optimization technique. A case study
(Elsevier B.V., 2025) Ravi Kant; Brijesh M.N. Kumar; Satya Prakash Maurya; Satya Narayan; Ajay Pratap Singh; Gopal Hema
A novel post-stack seismic inversion algorithm has been developed to estimate acoustic impedances using P-wave reflection seismic data, employing the music-inspired harmony search global optimization (HSO) technique. This optimization seeks to find the global minimum of the objective function, which measures the misfit between synthetic and observed post-stack seismic data. During the iterative inversion process, acoustic impedance models are randomly perturbed, and synthetic seismic data are recalculated to match observed data. To enhance stability, the algorithm uses constraints from a well-log-derived low-frequency impedance model. The proposed algorithm was tested on synthetic and real data to demonstrate its effectiveness in post-stack seismic data inversion. On synthetic test, we found high accuracy of the HSO-generated traces, with average correlations of 0.99, 0.99, 0.97, and 0.96, and RMS errors of 0.12, 0.40, 0.50, and 0.62, for noise levels of 0 %, 10 %, 20 %, and 30 %, respectively. For real data from the Blackfoot Field, Alberta, Canada, the algorithm achieved a 0.93 correlation and 0.22 RMS error, enabling seismic data inversion for acoustic impedance estimation. The inverted section identified low acoustic impedance (8000–9000 m/s∗g/cc), matching the high seismic amplitude anomaly, suggesting a sand channel reservoir between 1040 and 1065 ms two-way travel time. While, high acoustic impedance (9000–12000 m/s∗g/cc) indicating background shale facies. This study explores potential hydrocarbon reservoirs in the Blackfoot Field, Alberta, using HSO-based advanced global optimization for efficient and accurate seismic data inversion. © 2025
CO2 characterization using seismic inversion based on global optimization techniques for enhanced reservoir understanding: a comparative study
(Springer Science and Business Media Deutschland GmbH, 2025) Ajay Pratap Singh; Ravi Kant; Satya Prakash Maurya; Brijesh M.N. Kumar; Nitin Verma; Raghav S. Singh; Kumar Hemant Singh; Manoj Kumar Srivastava; Gopal Hema
Characterization of CO2 in subsurface reservoirs is an important aspect of ensuring the effectiveness and safety of storage operations. Seismic inversion technique, widely applied in the petroleum industry for tasks such as quantitative reservoir characterization and improved oil recovery, is now finding potential application in estimating the extension of CO2 plumes within an underground reservoir. Seismic inversion, coupled with global optimization techniques, offers a powerful approach to enhance reservoir understanding in CCS projects. This paper presents a comprehensive study on the application of a global optimization workflow to increase subsurface resolution in the CO2 storage. Global optimization techniques including simulated annealing and particle swarm optimization are employed to optimize the subsurface model and estimate the P-wave impedance. We used the Sleipner field in the Norwegian North Sea which is extracting gas with high CO2 content, and for environmental reasons, they have been injecting more than 11 million tons of CO2 into the Utsira sand saline aquifer above the hydrocarbon reserves since 1996. To monitor the spread of this CO2 plume and ensure the safety of the upper layers, a series of seven 3D seismic surveys have been conducted. Our study concentrated on vintage data from 1994 (before CO2 injection) and 1999 and 2006 (after an 8.4 Mt CO2 injection). The workflow incorporates prior information from well logs, facilitating faster convergence and detailed subsurface representations. The findings suggest that the application of global optimization techniques is advantageous for optimizing earth’s subsurface models, particularly in the context of CO2 storage initiatives. Although we faced challenges due to the absence of time-lapse well-log data in the specific area of interest, we successfully applied our inverse workflow to generate acoustic impedance data, to the best of our knowledge. These findings offer valuable insights for enhancing the understanding of CO2 dispersion within a reservoir. © The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences 2025.
Enhancing porosity prediction: Integrating seismic inversion utilizing sparse layer reflectivity, and particle swarm optimization with radial basis function neural networks
(John Wiley and Sons Inc, 2025) Ravi Kant; Brijesh M.N. Kumar; Ajay Pratap Singh; Gopal Hema; Satya Prakash Maurya; Raghav S. Singh; Kumar Hemant Singh; Piyush Sarkar
Seismic inversion, a crucial process in reservoir characterization, gains prominence in overcoming challenges associated with traditional methods, particularly in exploring deeper reservoirs. In this present study, we propose an inversion approach based on modern techniques like sparse layer reflectivity and particle swarm optimization to obtain inverted impedance. The proposed sparse layer reflectivity and particle swarm optimization techniques effectively minimize the error between recorded seismic reflection data and synthetic seismic data. This reduction in error facilitates accurate prediction of subsurface parameters, enabling comprehensive reservoir characterization. The inverted impedance obtained from both methods serves as a foundation for predicting porosity, utilizing a radial basis function neural network across the entire seismic volume. The study identifies a significant porosity zone (>20%) with a lower acoustic impedance of 6000–8500 m/s g cm3, interpreted as a sand channel or reservoir zone. This anomaly, between 1045 and 1065 ms two-way travel time, provides high-resolution insights into the subsurface. The particle swarm optimization algorithm shows higher correlation results, with 0.98 for impedance and 0.73 for porosity, compared to sparse layer reflectivity's 0.81 for impedance and 0.65 for porosity at well locations. Additionally, particle swarm optimization provides high-resolution subsurface insights near well location and across a broader spatial range. This suggests particle swarm optimization's superior potential for delivering higher resolution outcomes compared to sparse layer reflectivity. © 2024 European Association of Geoscientists & Engineers.
Machine learning-based comparative assessment of seismic inversion approaches for porosity estimation
(Springer, 2025) Brijesh M.N. Kumar; Ravi Kant; Shushant Singh; Nitin Verma; Satya Prakash Maurya; Ajay Pratap Singh; Gopal Hema; Sanjay K. Sharma
Seismic inversion is a fundamental technique for extracting quantitative subsurface properties from seismic data. This study confidently presents a thorough comparative assessment of five seismic inversion methods: Coloured Inversion (CI), Band-Limited Inversion (BLI), Model-Based Inversion (MBI), Maximum Likelihood Inversion (MLI), and Sparse Layer Reflectivity (SLR), all enhanced by a Probabilistic Neural Network (PNN) for porosity estimation. Using seismic data from the Blackfoot field in Canada, we rigorously evaluate the effectiveness of these techniques in delineating reservoir zones and accurately predicting porosity. Our compelling results demonstrate that MBI, BLI, and CI significantly outperform the other methods in identifying reservoir boundaries and aligning closely with well-log data. When integrated with PNN, MBI and BLI excel in mapping reservoir porosity, providing reliable predictions ranging from 5 to 20% and impedance values between 7000 and 18,000 m/s*g/cc. These integrated approaches deliver an unparalleled understanding of reservoir characteristics, far surpassing the precision and reliability of traditional methods. This research underscores the immense potential of combining advanced seismic inversion techniques with neural network models to revolutionize reservoir characterization. Our findings firmly establish the effectiveness of MBI and BLI, particularly when paired with PNN, in delivering accurate and high-resolution insights into subsurface porosity and reservoir boundaries. © Indian Academy of Sciences 2025.
Monitoring long-term storage of CO2 in a gas and condensate field in the North Sea off the coast of Norway using seismic methods
(Society of Exploration Geophysicists, 2025) Ajay Pratap Singh; Satya Prakash Maurya; Ravi Kant; Brijesh M.N. Kumar; Gopal Hema; Manoj Kumar Srivastava; Abhay Kumar; Anjali; Shoharat; Rohit Chaurasia; Anupama Sharma; Ankita Devi; Swarnima Pandey
The rising concentration of CO2 in the atmosphere drives climate change, prompting the development of various mitigation strategies. One approach involves injecting CO2 into hydrocarbon reservoirs for long-term storage. For long-term storage, one needs to monitor the injected CO2 to see the CO2 storage location and detect any leakage. This study focuses on the monitoring of injected CO2 in the Utsira Formation from a gas and condensate field in the Sleipner Field off the coast of Norway. The monitoring of injected CO2 is carried out using seismic inversion techniques of time-lapse data acquired over the injected zone at different time intervals. Seismic inversion techniques transform seismic reflection data into quantitative acoustic impedance models of the subsurface. The objective of the present study is to compare model-based inversion, band-limited inversion (BLI), maximum likelihood inversion, linear programming inversion (LPI) and sparse layer reflectivity inversion (SLRI) approaches to monitor CO2. All inversion techniques show consistent results, with low impedance values ranging from 2115 to 5275 m/s*g/cm3 in the Utsira Formation. Among these techniques, SLRI and LPI outperform traditional methods by offering high-resolution imaging of CO2 migration pathways, making them particularly effective for early leak detection and reducing uncertainties in reservoir modeling. By enhancing storage security and predictive modeling, these methodologies significantly contribute to the scalability and reliability of carbon capture and storage as a critical tool in combating climate change. This research not only strengthens the scientific foundation of seismic monitoring techniques but also provides practical recommendations for optimizing subsurface CO2 storage assessment methods. © 2025 Society of Exploration Geophysicists. All rights reserved.
Reservoir characterization using simultaneous inversion of pre-stack seismic data based on traditional conjugate gradient methods and particle swarm optimization: A comparative case study
(Springer Science and Business Media Deutschland GmbH, 2025) Brijesh M.N. Kumar; Ravi Kant; Satya Prakash Maurya; Ajay Pratap Singh; Gopal Hema; Raghav S. Singh; Piyush Sarkar; Kumar Hemant Singh; Sanjay K. Sharma
Seismic inversion is a geophysical method that converts seismic reflection data into a quantitative representation of a reservoir's geological properties. These parameters are crucial for predicting reservoir rocks and the fluids present in the subsurface. Seismic inversion has been categorized into two ways: post-stack and pre-stack inversion. Pre-stack seismic inversion provides more detailed properties of the subsurface as compared to post-stack inversion. This study focuses on pre-stack seismic inversion using the traditional conjugate gradient methods and a novel methodology based on particle swarm optimization (PSO) techniques. Pre-stack inversion inherently utilizes amplitude variation with offset (AVO), which provides critical information about the elastic properties of the subsurface. The conjugate gradient method is a local optimization technique that can converge at local optima, potentially leading to false solutions to the inverse problem. To overcome these drawbacks, PSO, a global optimization technique with a tendency to converge at global optima, was employed. These methods were employed in the Penobscot field in Canada in two phases. Initially, the composite trace was inverted and then compared to the original well-log data. The full seismic volume was then inverted to calculate P-velocity, S-velocity, and density. The inverted results from both methods provided high-resolution subsurface information, but the PSO-based seismic inversion showed significantly better results compared to traditional methods. The conjugate gradient method attained a correlation of 0.89 with a RMS error of 0.33, while the PSO-based inversion attained a correlation of 0.95 with RMS error of 0.23. Additional statistical parameters also demonstrated that the PSO-based seismic inversion offered more detailed and higher-resolution subsurface information compared to the traditional pre-stack seismic inversion utilizing conjugate gradient methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.
Seismic inversion based on principal component analysis and probabilistic neural network for prediction of porosity from post-stack seismic data
(Springer Science and Business Media Deutschland GmbH, 2025) Nitin Verma; Ravi Kant; Satya Prakash Maurya; Brijesh M.N. Kumar; Ajay Pratap Singh; Gopal Hema; Raghav S. Singh; Kumar Hemant Singh; Piyush Sarkar
This research delves into the utilization of Principal Component Analysis (PCA) and Probabilistic Neural Network (PNN) techniques for predicting porosity values based on seismic inversion data, which provides crucial insights into subsurface properties essential for reservoir characterization. The study integrates Principal Component Analysis (PCA) with Probabilistic Neural Networks (PNN), to streamline the interpretation of seismic data by condensing numerous attributes into a smaller set with a probabilistic classification approach. This integration provides a novel methodology for processing and interpreting complex seismic data. Additionally, model-based inversion is conducted to obtain extra attributes (inverted impedance 7000–18000 m/s*g/cc) for use in training the PNN model. Subsequently, the PNN model is utilized to make accurate porosity predictions by leveraging the reduced-dimensional seismic features. The study aims to compare PNN performance with single attributes against PNN with Principal Component(PC), highlighting their differences and similarities for the data from the Blackfoot field in Canada.Initially, various single attributes are generated and then subjected to PCA techniques to convert them into three principal components. Both PNN models, one with single attributes and the other with PC, are trained and validated to reveal detailed subsurface information. The inverted results show a strong correlation with well-log parameters, with an average correlation coefficient of 0.84 for PNN using single attributes and 0.78 for PNN using PC.It was observed that PNN with single attributes performed better on the training data set but Performs below expectations on validation dataset compared to PNN with PC.Furthermore, these algorithms are applied to whole seismic data to predict porosity within the inter-well region. The inverted volumes depict consistent porosity ranging from 5 to 11% across the region for PNN with PC. Furthermore, the interpretation of the inverted results for PNN with PC highlights anomalous zones characterized by low impedance and high porosity. These zones closely match well-log data and are identified as likely sand channels. The combination of PCA and PNN represents an advancement in machine learning techniques applied to geoscience. This research contributes to the growing field of machine learning in geoscience by exploring new methodologies and their applications. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
Seismic inversion for CO2 volume monitoring and comprehensive evaluation of pore fluid properties: a case study
(Springer Science and Business Media Deutschland GmbH, 2025) Gopal Hema; Satya Prakash Maurya; Ravi Kant; Ajay Pratap Singh; Nitin Verma; Brijesh M.N. Kumar; Raghav S. Singh; Kumar Hemant Singh
A comprehensive evaluation of pore fluid properties, involves detailed analysis of various characteristics and behaviours relevant to its storage and management in subsurface reservoirs. The assessment includes variations in CO2 density, bulk modulus, temperature, pressure, velocities, and interactions with reservoir fluids and rocks. The seismic response of porous rocks hosting pore fluids is influenced by these physical properties, crucial for understanding CO2 behaviour in carbon capture and storage (CCS) initiatives. In this study, we first utilize the Batzle–Wang model to predict the behavior of common pore fluids, such as brine and gas, which are key to understanding the seismic response of the reservoir. This initial analysis provides the foundation for the next step: monitoring the behavior of injected CO2 at the Sleipner field in Norway. To accurately track changes in the subsurface related to CO2 injection, we employ seismic inversion using the simulated annealing (SA) technique. This global optimization approach offers significant advantages over traditional local optimization methods, yielding more reliable and near-optimal solutions for estimating the changes in acoustic impedance caused by CO2 saturation. The study examines five sets of time-lapse seismic data from the Sleipner field, from 1994 to 2006. Acoustic impedances are computed for the pre-injection period and post-injection years, revealing a low impedance zone spanning from 2000 to 2500 m/s/g/cc. This inversion result predicts the injected CO2 volume by calculating the CO2 area from the uppermost time slice of different years, based on acoustic impedance seismic sections. To address inherent non-uniqueness in time-lapse analysis, the estimated volume is compared with the original production volume. The results indicate that the estimated volume closely resembles the original injected volume for different time-lapse seismic data. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.