Browsing by Author "S.P. Maurya"

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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) Raghav Singh; S.P. Maurya; Brijesh Kumar; Nitin Verma; Alok Kumar Tiwari; Ravikant Tiwari; G. Hema; Ajay P. Singh
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.
Application of LP and ML sparse spike inversion with probabilistic neural network to classify reservoir facies distribution - A case study from the Blackfoot field, Canada
(Elsevier B.V., 2018) S.P. Maurya; N.P. Singh
Sparse-Spike inversion techniques are used to estimate distribution of acoustic impedance in inter well region, the important parameters for characterizing the reservoir facies from seismic and well log data. The purpose of sparse spike impedance inversion is to obtain high-resolution impedance profile of the subsurface from the low resolution seismic data with the integration of well log data and enhance the interpretation of the prospective zone. In the present study, two types of sparse spike inversion techniques, namely, Linear Programming (LP) sparse spike inversion and Maximum Likelihood (ML) sparse spike inversion are applied to estimate acoustic impedance from seismic data of the Blackfoot region, Alberta, Canada. The principle objective of the study is assessing the relative performance of these techniques for estimation of petrophysical parameters to identification of prospective zones in the area. Initially, the inversion methods are applied to the composite trace near to well locations and inverted for acoustic impedance and compared with the actual impedance derived from the well log data. The result demonstrates that both curves are matching with each other very well. The correlation is estimated to be 0.97 and 0.93, Synthetic relative error (SRE) 0.23 and 0.34 and Root mean square (RMS) errors are 1125 m/s*g/cc and 1205 m/s*g/cc for Linear Programming sparse spike inversion (LPSSI) and Maximum Likelihood sparse spike inversion (MLSSI), respectively. The analysis for composite traces depicts the robustness and performance of the algorithm. Thereafter, the techniques are applied to seismic volume to estimate variation of acoustic impedance in inter well regions. The analysis of inverted impedance shows a low impedance anomaly in between 1060 and 1075 ms time intervals which may be due to presence of reservoir facies (sand channel). The analyses of the inverted results reiterate that both methods work satisfactory and show variation of reservoir facies in similar way. The results found by LPSSI shows slightly higher resolution compared to the MLSSI results. Thereafter, to enhance reservoir facies more clearly, porosity is predicted in inter well region by using probabilistic neural network (PNN) technique. The result shows very high porosity (> 15%) in between 1060 and 1075 ms time interval which corroborated with the low impedance zone and confirms the presence of sand channel. The qualitatively and quantitatively analysis of inversion results suggest that the LPSSI along with PNN provides better reservoir characterization than MLSSI and PNN combination for the Blackfoot field, Alberta, Canada. © 2018 Elsevier B.V.
Application of maximum likelihood and model-based seismic inversion techniques: a case study from K-G basin, India
(Springer Science and Business Media Deutschland GmbH, 2022) Richa; S.P. Maurya; Kumar H. Singh; Raghav Singh; Rohtash Kumar; Prabodh Kumar Kushwaha
Seismic inversion is a geophysical technique used to estimate subsurface rock properties from seismic reflection data. Seismic data has band-limited nature and contains generally 10–80 Hz frequency hence seismic inversion combines well log information along with seismic data to extract high-resolution subsurface acoustic impedance which contains low as well as high frequencies. This rock property is used to extract qualitative as well as quantitative information of subsurface that can be analyzed to enhance geological as well as geophysical interpretation. The interpretations of extracted properties are more meaningful and provide more detailed information of the subsurface as compared to the traditional seismic data interpretation. The present study focused on the analysis of well log data as well as seismic data of the KG basin to find the prospective zone. Petrophysical parameters such as effective porosity, water saturation, hydrocarbon saturation, and several other parameters were calculated using the available well log data. Low Gamma-ray value, high resistivity, and cross-over between neutron and density logs indicated the presence of gas-bearing zones in the KG basin. Three main hydrocarbon-bearing zones are identified with an average Gamma-ray value of 50 API units at the depth range of (1918–1960 m), 58 API units (2116–2136 m), and 66 API units (2221–2245 m). The average resistivity is found to be 17 Ohm-m, 10 Ohm-m, and 12 Ohm-m and average porosity is 15%, 15%, and 14% of zone 1, zone 2, and zone 3 respectively. The analysis of petrophysical parameters and different cross-plots showed that the reservoir rock is of sandstone with shale as a seal rock. On the other hand, two types of seismic inversion namely Maximum Likelihood and Model-based seismic inversion are used to estimate subsurface acoustic impedance. The inverted section is interpreted as two anomalous zones with very low impedance ranging from 1800 m/s*g/cc to 6000 m/s*g/cc which is quite low and indicates the presence of loose formation. © 2021, The Author(s).
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) Nitin Verma; S.P. Maurya; Ravi kant; K.H. Singh; Raghav Singh; A.P. Singh; G. Hema; M.K. Srivastava; Alok K. Tiwari; P.K. Kushwaha; Richa Singh
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.
Coulomb Stress Change of the 2012 Indian Ocean Doublet Earthquake
(Springer International Publishing, 2024) Pankhudi Thakur; Rohtash Kumar; Ranjit Das; Amritansh Rai; Raghav Singh; Ankit Singh; S.P. Maurya
The 2012 Sumatra (Mw 8.6) earthquake, which falls into the largest and rarest group of the great intraplate earthquakes, continues to awe many brilliant minds. An enormous aftershock (Mw 8.2) was felt two hours after the Indian Ocean earthquake along the triple intersection of the Indian, Australian, and Sunda plates in the northwest. Over the past 20 years, there have been numerous earthquakes in the Sumatran subduction zone, including the 2004 earthquake (Mw 9.2) of Sumatra-Andaman, the 2005 earthquake (Mw 8.6) of Nias-Simeulue, the 2007 earthquake (Mw 8.4) of Bengkulu, the 2010 earthquake (Mw 7.8) of Mentawai tsunami, and a large number of other minor to moderate-sized events. It often takes a few seconds to a few minutes for the stress brought on by an earthquake to dissipate. This massive discharge disrupts the lithosphere and asthenosphere, which causes more earthquakes to occur nearby. A comprehensive comprehension of stress variations along a fault and its neighboring faults is essential for effectiv ly predicting and mitigating seismic risks. Drawing inspiration from the earthquake finite fault model pioneered by Guangfu Shao, Xiangyu Li, and Chen Ji from UCSB, we have formulated Coulomb stress models tailored to the Sumatran subduction zone and the Sumatran fault. It was discovered that the primary shock’s related coulomb stress change exceeded the stress-triggering threshold. The aftershock struck a place where there was a lot of stress from the mainshock. Therefore, the Coulomb failure stress change brought on by the mainshock is likely what caused the Sumatra aftershock to occur. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Covering assisted intuitionistic fuzzy bi-selection technique for data reduction and its applications
(Nature Research, 2024) Rajat Saini; Anoop Kumar Tiwari; Abhigyan Nath; Phool Singh; S.P. Maurya; Mohd Asif Shah
The dimension and size of data is growing rapidly with the extensive applications of computer science and lab based engineering in daily life. Due to availability of vagueness, later uncertainty, redundancy, irrelevancy, and noise, which imposes concerns in building effective learning models. Fuzzy rough set and its extensions have been applied to deal with these issues by various data reduction approaches. However, construction of a model that can cope with all these issues simultaneously is always a challenging task. None of the studies till date has addressed all these issues simultaneously. This paper investigates a method based on the notions of intuitionistic fuzzy (IF) and rough sets to avoid these obstacles simultaneously by putting forward an interesting data reduction technique. To accomplish this task, firstly, a novel IF similarity relation is addressed. Secondly, we establish an IF rough set model on the basis of this similarity relation. Thirdly, an IF granular structure is presented by using the established similarity relation and the lower approximation. Next, the mathematical theorems are used to validate the proposed notions. Then, the importance-degree of the IF granules is employed for redundant size elimination. Further, significance-degree-preserved dimensionality reduction is discussed. Hence, simultaneous instance and feature selection for large volume of high-dimensional datasets can be performed to eliminate redundancy and irrelevancy in both dimension and size, where vagueness and later uncertainty are handled with rough and IF sets respectively, whilst noise is tackled with IF granular structure. Thereafter, a comprehensive experiment is carried out over the benchmark datasets to demonstrate the effectiveness of simultaneous feature and data point selection methods. Finally, our proposed methodology aided framework is discussed to enhance the regression performance for IC50 of Antiviral Peptides. © The Author(s) 2024.
Data-Driven Spatiotemporal Assessment of Seismicity in the Philippine Region
(Springer International Publishing, 2024) Amritansh Rai; Rohtash Kumar; S.P. Maurya; Ankit Singh; Pankhudi Thakur; Raghav Singh; Ranjit Das
The present study provides a retrospective analysis of the geographical and chronological fluctuations of three basic statistical characteristics of seismicity using a big dataset of events that occurred between 1940 and 2022 in the Philippine region. For determining the spatial-time changes in a-value (seismic activity), b-value (recurrence graph slope), and z-value, the contemporary expanded software package ZMAP with numerous sophisticated seismological functions for earthquake catalog analysis is employed (parameter of the relative seismic quiescence). For the various statistical interpretations, catalog data from the United States Geological Survey (USGS) occurred in spatial windows 0° N – 20° N and 118°E – 130oN are used. The overall conclusion is that unusually low b-values and high z-values, which define zones of comparatively seismic quiescence, may be a signal of the approaching release of more severe stress in areas near zones of relatively high a-values. Thus, the suggested joint interpretation of the spatial-time fluctuations of these three statistical characteristics of seismicity may be seen as a form of the predictor of the more powerful recent seismic occurrences in the region. Furthermore, the occurrence probability of a seven magnitude event is near about one with a return period of 2 years. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Effect of row arrangement on root growth of linseed (Linum usitatissimum L.) + dwarf field pea (Pisum sativum L.) intercropping association under irrigated condition
(Enviro Media, 2016) Shiv Bahadur; S.P. Maurya; Lala Ram; R.N. Maurya; K.K. Maurya
The field experiment was conducted at Agricultural Research Farm, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, during winter season (rabi) of 2013-2014 with three replications by keeping fourteen treatments in randomized block design, to evaluate the effect of intercropping association on root nodulation of dwarf field pea and root growth of (Linum usitatissimum L.) + dwarf field pea (Pisum sativum L.). The highest number of nodules plant-1 (13.92), nodules dry weight plant-1 (49.95 mg) and root dry weight of linseed (0.80 g) and dwarf field pea (0.360 g) were recorded under sole planting as compared to other treatments. Among the row arrangements, highest number of root nodules plant-1 (13.81), dry weight of root nodules plant-1 (49.54 mg) and root dry weight of dwarf field pea (0.341 g) recorded under row ratio of 1:4 with 20% linseed + 80% dwarf field pea and in case of linseed (0.78 g) recorded under, row ratio of 4:1 with 80% linseed + 20% dwarf field pea. Copyright © EM International.
Effect of row arrangements on quality and nutrient dynamics of linseed (Linum usitatissimum l.) + dwarf field pea (Pisum sativum l.) intercropping association in irrigated condition
(Journal of Pure and Applied Microbiology, 2016) Shiv Bahadur; S.K. Verma; Surajyoti Pradhan; Lala Ram; R.N. Maurya; S.P. Maurya
Field experiments were conducted during winter season 2013-14 to study the effect of various row arrangements on quality and nutrient dynamics of linseed + dwarf field pea were investigated. The highest grain and straw yield, protein and oil content were recorded in T8 followed by T9 whereas highest protein and NKP content were recorded in T11 followed by T5. In case of dwarf field pea highest grain, straw and protein yield was recorded in T11 followed by T5 whereas highest protein and NPK content were recorded in T8. None of the row arrangement was show superiority over sole crop of either linseed or field pea.
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) G. Hema; S.P. Maurya; Ravi Kant; Ajay P. Singh; Nitin Verma; Raghav Singh; K.H. Singh
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 Ltd
Estimating elastic impedance from seismic inversion method: A study from Nova Scotia field, Canada
(Indian Academy of Sciences, 2019) S.P. Maurya
In the present study, elastic impedance (EI) inversion is performed to estimate subsurface elastic properties in inter-well regions. These elastic properties are helpful to discriminate gas-bearing formation from gasfree formation, as well as overpressure zone. Seismic reflection data from the Penobscot Scotia shelf, Canada are used for the analysis which is performed in two steps. First, the method is tested with noise-free synthetic data, as well as with addition of 5%, 10%, 20% and 30% Gaussian noise. The analysis shows that efficacy of EI inversion decreases by 3.4% with addition of 30% noise in the data compared to noise-free data. In the second step, EI inversion is applied to the real data and variation of EI is estimated for near- and far-angle stack gathers. The analysis demonstrates that the inverted results follow the well-log curve satisfactorily. The results also show higher resolution images for the far-stack data compared to the nearstack data. Incidentally, it is noticed that the area of study does not contain any major gas or overpressure zones. As of now, the analysis has been performed for small datasets of the region. Robustness of the method needs to be tested with more data from other parts of the region as well. © 2019 Current Science Association, Bengaluru.
Estimation of Petrophysical Properties Using Linear Programming Sparse Spike Inversion and Deep Feed-Forward Neural Network Techniques Over F3 Block, Netherlands: A Case Study
(Birkhauser, 2024) Raghav Singh; Prabodh Kumar Kushwaha; S.P. Maurya; Piyush Rai
In this study, acoustic impedance (P-impedance) distribution in the subsurface of the F3 block, Netherlands is determined using the linear programming (l1-norm) sparse spike inversion (LPSSI) method. The objectives of the study are to characterize the sand channel and extract high-resolution subsurface rock features from the low-resolution seismic data. To estimate rock properties from seismic data, a variety of conventional post-stack seismic inversion techniques are available. However, the LPSSI technique is a reasonably quick and easy-to-compute subsurface model that can be employed for both quantitative and qualitative interpretation. The method is employed in two steps: first, composite traces close to well locations are retrieved and inverted for acoustic P-impedance, and then optimization of the LPSSI parameters is done using comparison with well log impedance. According to the analysis of the composite traces, the algorithm performs well and has a high average correlation (0.98). The F3 block seismic data are utilized in the second stage to estimate the distribution of acoustic impedance in the subsurface by using the LPSSI method. A sand channel-like low impedance anomaly with a range of 3800–7400 m/s g/cc is evident in the inverted acoustic impedance analysis at the 1380–1400 ms time interval. Then, using a deep feed-forward neural network (DFNN), many other crucial rock parameters, including porosity, density, and P-wave velocity, were estimated in the inter-well region to corroborate the sand channel. Following the analysis of these petrophysical properties, a high porosity zone (24–40%), low-density zone (1.9–2.02 g/cc), and low P-wave velocity zone (1700–2300 m/s) are present in the 1380–1400 ms time interval, which aligns with the low impedance zone and validates the presence of the sand channel. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.
Exploring the Concept of Self-Similarity and High-Frequency Decay Kappa-Model and fmax-Model Using Strong-Motion Surface and Borehole Data of Japan: A Statistical Approach
(Springer International Publishing, 2024) Rohtash Kumar; Raghav Singh; Amritansh Rai; Sandeep; S.P. Singh; S.P. Maurya; Prashant Kumar Singh
We statistically analyzed the fmax-model, κ-model and stress drop (Δσ) using surface and borehole data of the KIK-NET Japan seismological network. The statistical tests show no contribution of source in the fmax-model and κ-model. The ‘fmax’ values obtained in the present study are 4.2–11.0 Hz and 5–11.0 Hz for surface and borehole data, respectively. The impact of local heterogeneities and wave propagation path is clearly visible on both surface and borehole fmax-models. The same is confirmed by the p-value ‘t-test’. The multivariate linear regression (MVLR) has been applied for the analysis of dependent variables ‘κ(s)’ and ‘κ(w)’ w.r.t. independent variables epicentral distance and magnitude. The p-value calculated by t-test indicates the strong dependence of κ(s) and κ(w) on near-surface geology and the physical state of the wave travelling media but almost no contribution of magnitude. The contribution of near-surface geology in kappa values is also confirmed by the ‘κ0’ (kappa at epicentral distance = 0). The relationships between the fmax-model and the κ-model have been developed for the study region. The stress drop (Δσ(s)) assessed from surface data is 44.16-65.86 bars with an average value of 53.19 bars and borehole derived stress drop (Δσ(w)) is 46.38-68.13 bars with an average value of 54.16 bars. This study discards the effect of depth; type of earthquake, i.e. normal, reverse and strike-slip; and signal to noise ratio (SNR) on stress drop as there is no huge variation in both Δσ(s) and Δσ(w) with the seismic moment and source radius. Therefore, the study supports the concept of self-similarity. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Exploring the utility of nonlinear hybrid optimization algorithms in seismic inversion: A comparative analysis
(Elsevier Ltd, 2024) Ravi Kant; Brijesh Kumar; S.P. Maurya; Raghav Singh; Anoop Kumar Tiwari
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. © 2024
Identification of the reservoir using seismic inversion based on particle swarm optimization method: A case study
(Springer, 2024) Ravi Kant; Brijesh Kumar; S.P. Maurya; Nitin Verma; Ajay P Singh; G. Hema; Raghav Singh; K.H. Singh; Piyush Sarkar
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.
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) Raghav Singh; Aditya Srivastava; Ravi Kant; S.P. Maurya; P. Mahadasu; Nitin Verma; G. Hema; P.K. Kushwaha; Richa; K.H. Singh; Ajay P. Singh; M.K. Srivastava; Piyush Sarkar
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.
Lapse-Time Dependence of Coda Quality Factor Within the Lithosphere of Northern Ecuador
(Springer International Publishing, 2024) Amritansh Rai; Rohtash Kumar; Ankit Singh; Raghav Singh; Indrajit Das; S.P. Maurya
In the current study, a frequency-dependent attenuation relationship based on coda waves is developed for the Ecuador region. The single backscattering model developed by Aki and Chouet (J Geophys Res 80:3322–3342, 1975) is used to investigate the dependence of Coda-Q on lapse time frames. The waveforms of 49 local earthquakes recorded by a one-station local seismological network are used for the analysis. The frequency-dependent Coda-Q relations estimated for the region are: Qc = (65.75) f0.6302 (10 sec lapse time), Qc = (92.48) f0.6868 (20 sec lapse time), Qc = (18.17) f1.3337 (30 sec lapse time), Qc = (30.95) f1.2303 (40 sec lapse time), Qc = (88.22) f0.9117, and Qc = (125.19) f0.9342. Given that greater areas would be sampled by longer lapse time windows, the rise in Qc values with lapse time demonstrates the depth dependency of Qc. The increase in value of Q0 (QC at 1 Hz) with time depicts that the heterogeneities decrease with depth. The observed quality factor is highly variable with the lapse time and frequency. The more significant value of n shows that the area is seismically active. The observed Qc relation is found equivalent to other similar seismically active regions. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Moho Mapping of Northern Chile Region Using Receiver Function Analysis and HK Stacking
(Springer International Publishing, 2024) Amritansh Rai; Rohtash Kumar; Dipankan Srivastava; Raghav Singh; Ankit Singh; S.P. Maurya
In this study, Rfpy software is utilized to compute the receiver functions to map Moho in the Northern Chile region. To obtain the teleseismic waveforms within an epicentral distance of 30° to 90°, the Package makes use of the IRIS station database. The receiver functions over stations AC04 and AC05 indicate a low-velocity layer possible area at a shallower depth. Additionally, the delay periods of the Moho Ps phase at various different back azimuths are used to infer the anisotropy or dipping Moho. With the aid of H-K Stacking the receiver functions for Poisson’s ratio and Moho depth were also inverted. Under the AC07 station, which is situated in Caldera, Atacama, a higher Moho depth of 46 km is discovered. This results in a Poisson’s ratio of 0.24. Below the CO10 station, which is in Coquimbo, an extraordinary poisson ratio is found. Accordingly, a Moho depth of 25 km is discovered. Inverting the acquired P-wave receiver functions may effectively determine the S-wave velocity structure below each station. This would aid in enhancing the region’s crustal imaging. Additionally, harmonic decompositions of the receiver functions might be performed to examine the behavior of anisotropy. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Monitoring of CO2 Sequestration through Seismic Inversion using Simulated Annealing: Insight from Synthetic Data Analysis
(European Association of Geoscientists and Engineers, EAGE, 2023) G. Hema; S.P. Maurya
The long-term safety of geologic carbon storage can be monitored using a variety of geophysical approaches. Monitoring CO2 migration in a geologic carbon storage site is generally carried out using the seismic method. In the present study, a seismic inversion based on simulated annealing is used to monitor CO2 migration, providing more detailed information. Seismic modeling can be used to image and monitor the fluid flow effects and leak detection in the reservoir. The Gassman fluid substitution was carried out to calculate the volume of the CO2 plume for the post-injection case. A 2D seismic model was generated to simulate the CO2 injection scenario. In the present study, synthetic data was used to check the reliability of the algorithm used in the case of the global optimization inversion technique. After performing the inversion analysis, a decrease in the impedance values is observed at the injection site. The inverted section shows very clear CO2 information which cannot be estimated from the interpretation of seismic data alone. © 2023 2nd EAGE/Aqua Foundation Indian Near Surface Geophysics Conference and Exhibition. All rights reserved.
Porosity prediction from offshore seismic data of F3 Block, the Netherlands using multi-layer feed-forward neural network
(Indian Academy of Sciences, 2020) Prabodh Kumar Kushwaha; S.P. Maurya; Piyush Rai; N.P. Singh
In the present study, seismic and well log information is incorporated with a multi-layer feed-forward neural network (MLFN) to predict porosity in the inter-well region. The aim of this study is to estimate a relationship between porosity and impedance to characterize the reservoir, if any, in the offshore F3 block, the Netherlands. MLFN is used to generate a connection between porosity logs and a set of seismic attributes, which are further used for porosity prediction. Modelbased inversion is employed to produce an acoustic impedance volume, which is a reliable technique for quantitative estimation of reservoir characteristics and acoustic impedance. The model-based inversion results indicate that the acoustic impedance (AI) in the region varies from 2500 to 6200 m/s*g/cm3 which is comparatively low and indicates loose formation. Thereafter, AI along with other attributes estimated from seismic data, is used as an input in MLFN, and porosity is predicted. The technique is first implemented on the traces close to well locations, and the findings are correlated with well log information, and after appropriate matching, the entire seismic segment is inverted for porosity. The results indicate that the porosity varies from 0.07 to 0.40. Further, a relationship between predicted porosity and inverted impedance is derived to represent the connection between these two parameters in the region. Moreover, based on this study, it is concluded that there is no significant reservoir in the region. However, as the analyses are performed for a specific range of data, it is possible that other parts of the area may have a different stratigraphy and possibility of the primary reservoir in the area. © 2020. All Rights Reserved.