The present research introduces a new methodology that combines simulated annealing (SA) and the quasi-Newton method (QNM) to enhance the prediction accuracy of impedance in the inter-well region. SA is employed to explore potential global optimum solutions with ample time and computational resources, while QNM serves as a local optimization technique focusing on refining solutions in the vicinity of the initial model. To address the limitations and leverage the strengths of both methods, the study integrates SA and QNM into a unified approach. The proposed technique involves applying a specific iteration of SA followed by QNM optimization, utilizing the initial model estimated by the former method. The effectiveness of this hybrid optimization approach is tested using synthetic data and real data from the F3-block in the Netherlands. Results indicate that the inverted impedance closely aligns with the modelled impedance under the hybrid optimization approach, outperforming SA, both with synthetic and real data. Statistical analysis reveal excellent performance within a reasonable computational time. Specifically, correlation coefficients for synthetic and real impedance cases are 0.99 and 0.88, respectively, while RMS errors are 0.11 and 0.26. Additionally, the hybrid optimization approach predicts impedance volume in the inter-well region, demonstrating superior subsurface information resolution compared to SA.

The objective of this research is to estimate the elastic properties of the CO2 plume in the Sleipner field and perform a comparative analysis of model-based inversion (MBI) and sparse layer reflectivity (SLR) inversion techniques. MBI is relatively old method, whereas SLR is relatively new method for seismic inversion. Model-based seismic inversion is a well-established deterministic inversion technique that iteratively minimizes the misfit between observed and modelled seismic data. In contrast, SLR inversion is designed to identify and analyse the reflectivity of thin subsurface layers by emphasizing sparsity in the reflectivity sequence. This study utilizes a set of time-lapse seismic angle stack data from the Sleipner field, comprising a 1994 pre-injection baseline and a 1999 post-injection monitor survey, following the injection of 2.35 million tons of CO2. These angle stacks were used to generate P-wave and S-wave reflectivity using the two-term Fatti amplitude versus offset (AVO) equation, which was then further utilized in the inversion process to estimate the elastic parameters. Acoustic and shear impedance (SI) were derived using MBI and SLR to evaluate their strengths, limitations, computational efficiency and adaptability to geological changes. In the CO2-injected zone, acoustic impedance values were observed between 2000 and 2400 m/s g/cm(3), whereas SI values ranged from 100 to 400 m/s g/cm(3). Our findings suggest that overall, MBI produces sharper and more reliable imaging across the entire seismic section. For P-impedance, MBI yielded correlation values of 0.980 with an error of 0.137 in 1994 and 0.989 with an error of 0.141 in 1999 datasets, whereas SLR showed higher correlation at the well location 0.997 with an error of 0.073 in 1994 and 0.998 with an error of 0.061 in 1999. For S-impedance, MBI achieved correlation values of 0.860 with an error of 0.650 in 1994 and 0.974 with an error of 0.265 in 1999 datasets. In comparison, SLR produced a correlation of 0.995 with an error of 0.072 in 1994 and 0.951 with an error of 0.370 in 1999 datasets at the well location. However, similar to the P-impedance case, whereas SLR performed well at the well location, its application to the full seismic volume resulted in reduced performance, characterized by noisier results and longer processing time. A comparative evaluation of MBI and SLR indicates that MBI offers greater efficiency, simpler implementation and faster computational performance. As a result, the impedance outputs obtained from MBI were subsequently converted into density, P-wave velocity and S-wave velocity using empirical relationships derived from well log data. In the seismic volumes, a significant change in the reservoir's elastic properties was observed in the CO2-saturated zone, compared to the Utsira Formation, which serves as the reservoir into which CO2 has been injected. Density decreased from 1.75 to 1.35 g/cm(3) (similar to 23%), P-wave velocity from 2000 to 1820 m/s (similar to 9%) and S-wave velocity from 1150 to 638 m/s (similar to 45%). These changes reflect the effects of CO2 replacing brine in the pore space, leading to a reduction in bulk density and stiffness and indicating overall reservoir softening due to gas injection. Integrating these inversion methods with multi-parameter elastic estimation enables effective CO2 plume monitoring and reservoir characterization, highlighting the role of seismic inversion in detecting fluid-induced changes and supporting improved monitoring strategies in carbon capture and storage (CCS) operations.

The development, management, and optimization of a reservoir depend on precise reservoir characterization. There are several methods for doing this, however in the current work, seismic inversion based on the hybrid particle swarm optimization (HPSO) methodology is used. In this method, a local optimization method called quasi-newton method (QNM), combined with a global optimization method called PSO to maximize their benefits and minimize their downsides are used. The global optimization method takes a lot of time to converge whereas, Quasi-Newton method is rapid, but heavily dependent on the initial model. The present study takes these two limitations into account. To characterize the reservoir, the hybrid PSO uses post-stack seismic data to predict acoustic impedance and porosity in the inter-well zone. The effectiveness of this newly devised method is first evaluated using synthetic data, and then it is applied to the real data from the Blackfoot area in Canada. The findings show that for both the synthetic and real data, the inverted outcomes closely match the observed data. The analysis anticipated that the inter-well acoustic impedance and porosity volume would vary from 6000 to 12000 m/s*g/cc and 5-22%, respectively. These volumes display extremely detailed subsurface data. The analysis of inverted findings reveals an abnormal zone inside the two-way transit time frame of 1045 to 1065 ms, ranging from low-impedance 6500-9000m/s*g/cc, and high porosity >15%. This unconventional area is classified as a reservoir. The method is particularly useful in nearby regions where detailed subsurface information needs to be estimated, even with limited prior data.

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.

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.

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.

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.

Seismic inversion is a computational technique used in geophysics to infer the properties of subsurface geological formations by analyzing seismic data. In order to lower the concentration of CO2 in the atmosphere, as a part of carbon capture and sequestration operation, these inversion techniques are essential for detecting CO2 leaks injected underground. While there are other ways to carry out this inversion, such as band-limited inversion or model-based seismic inversion, the current study used maximum likelihood (ML) inversion techniques. In this technique, a probabilistic framework is used to quantify uncertainties in both the seismic data and the model parameters. The inversion process involves iteratively adjusting the model parameters to minimize the misfit between the observed seismic data and the data predicted by the model. The misfit is typically quantified using a measure such as the difference between observed and predicted seismic waveforms or attributes. The ML method is faster and provide higher resolution subsurface information in comparison with other traditional methods. In this study, to increase accuracy and comprehension of the dynamic behavior of the injected CO(2 )plume, time-lapse inversion and analysis of 4D seismic data are being used. The analyses of 4D seismic data includes predictability, temporal shift, cross correlation, and other crucial elements to learn more about the reservoir's response to CO2 injection. For the analysis of Sleipner Gas Field, Norway's seismic data recorded in 1994 (pre-injection) and 2001 (post-injection) are used. The comparison of inverted impedance section from pre-injection and post-injection clearly shows the movement of CO2 plume in the subsurface. Also the use of ML inversion methods over traditional model based seismic inversion provides more accurate and less computation time and computation cost which is very important for any developing country to adopt CCS project.

In 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

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.

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.

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

To estimate acoustic impedance and porosity in inter-well regions, a seismic inversion based on Hooke and Jeeves’ methods has been developed. The Hooke and Jeeves algorithm is a local optimization method that can reach a local or global optimum solution depending on the starting model. To prevent convergence to a local optimum, in the present study, the solution is constrained by well-log data. To optimize parameters, the algorithms are first assessed on synthetic data followed by the coal coking and wedge model. The algorithm’s performance is very satisfactory, according to the error analysis between the inverted and anticipated outcomes. Further, a real data application from the Blackfoot field, Canada, has been performed in two steps: First, a composite seismic trace close to the well location is retrieved, inverted into an impedance, and compared with the well-log impedance. The analysis shows how well the well-log impedance corresponds to the inverted impedance. Finally, in the second step, the entire seismic reflection data is subjected to the Hooke and Jeeves–based inversion, and the volume of acoustic impedance and porosity in the inter-well region is predicted. The paper demonstrates that when local optimization is utilized and is constrained by well-log data, the algorithms yield higher-resolution subsurface information. A low impedance anomaly (ranging from 7000 to 9500 m/s g/cc) was detected between 1040 and 1060 ms of two-way travel time, according to the study of inverted impedance. Additionally, the same zone contains an estimated high porosity anomaly (> 12

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