Pub Date : 2021-10-18DOI: 10.3997/2214-4609.202113187
A. R. Dalkhani, X. Zhang, C. Weemstra
Summary Seismic surface wave tomography is an effective tool for 3D crustal imaging. Conventionally, a two-step inversion algorithm is used to recover a three-dimensional model of surface wave velocity. That is, starting from surface wave dispersion data (frequency-dependent phase velocities), an initial inversion resulting in a series of (2D) maps of frequency-dependent surface-wave velocity is followed by a separate (1D) depth inversion. A single-step 3D non-linear algorithm has recently been proposed in a Bayesian framework. The algorithm involves a reversible jump Markov chain Monte Carlo approach and is referred to transdimensional tomography. Here, we investigate the feasibility of this transdimensional algorithm for the purpose of recovering the 3D surface wave velocity structure below the Reykjanes Peninsula, southwest Iceland. In particular, we investigate this for the specific receiver configuration for which we have obtained year-long recordings of ambient seismic noise. To that end, we designed a number of synthetic tests using receiver-receiver travel times associated with that station configuration. We find that the transdimensional algorithm successfully recovers the 3D velocity structure of the area. In particular, the algorithm successfully adapts its resolution to the density of rays and the level of data noise. Moreover, quantified solution uncertainty makes the result better interpretable.
{"title":"3D transdimensional ambient noise surface wave tomography of the Reykjanes Peninsula – a feasibility study","authors":"A. R. Dalkhani, X. Zhang, C. Weemstra","doi":"10.3997/2214-4609.202113187","DOIUrl":"https://doi.org/10.3997/2214-4609.202113187","url":null,"abstract":"Summary Seismic surface wave tomography is an effective tool for 3D crustal imaging. Conventionally, a two-step inversion algorithm is used to recover a three-dimensional model of surface wave velocity. That is, starting from surface wave dispersion data (frequency-dependent phase velocities), an initial inversion resulting in a series of (2D) maps of frequency-dependent surface-wave velocity is followed by a separate (1D) depth inversion. A single-step 3D non-linear algorithm has recently been proposed in a Bayesian framework. The algorithm involves a reversible jump Markov chain Monte Carlo approach and is referred to transdimensional tomography. Here, we investigate the feasibility of this transdimensional algorithm for the purpose of recovering the 3D surface wave velocity structure below the Reykjanes Peninsula, southwest Iceland. In particular, we investigate this for the specific receiver configuration for which we have obtained year-long recordings of ambient seismic noise. To that end, we designed a number of synthetic tests using receiver-receiver travel times associated with that station configuration. We find that the transdimensional algorithm successfully recovers the 3D velocity structure of the area. In particular, the algorithm successfully adapts its resolution to the density of rays and the level of data noise. Moreover, quantified solution uncertainty makes the result better interpretable.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127458334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-18DOI: 10.3997/2214-4609.202113327
L. Cui, K. Wu
Summary To further improve the quality and efficiency of subsurface fault zone image and study its geometry. Herein we adopted post-stack seismic data conditioning and a combination of seismic multi-attribute for producing a new hybrid attribute through a supervised multilayer perceptron (MLP) neural network in the Jurassic formation of Cai36 3D prospect located in the eastern part of the Junggar Basin. We first conditioned original seismic data by using the dip-steering cube extracted from the original seismic data. Secondly, we extracted conventional seismic attributes from the conditioned data sensitive to fault zone signatures. Thirdly, we selected a set of “picks” at a time slice representing the presence or absence of fault zones. Then we adopted the supervised MLP neural network to train over the selected seismic attributes extracted at the fault zone and non-fault zone positions. We obtained a new fault probability cube as new attributes. Finally, we analyzed a typical strike-slip fault zone using the new attributes. This study provides an effective way of fault zone imaging from seismic data and adds new insights into its geometry. Therefore, the workflows used here could be widely applied to other 3D surveys.
{"title":"Characterizing Subsurface Damage Zones From 3D Seismic Data Using Artificial Neural Network Approach","authors":"L. Cui, K. Wu","doi":"10.3997/2214-4609.202113327","DOIUrl":"https://doi.org/10.3997/2214-4609.202113327","url":null,"abstract":"Summary To further improve the quality and efficiency of subsurface fault zone image and study its geometry. Herein we adopted post-stack seismic data conditioning and a combination of seismic multi-attribute for producing a new hybrid attribute through a supervised multilayer perceptron (MLP) neural network in the Jurassic formation of Cai36 3D prospect located in the eastern part of the Junggar Basin. We first conditioned original seismic data by using the dip-steering cube extracted from the original seismic data. Secondly, we extracted conventional seismic attributes from the conditioned data sensitive to fault zone signatures. Thirdly, we selected a set of “picks” at a time slice representing the presence or absence of fault zones. Then we adopted the supervised MLP neural network to train over the selected seismic attributes extracted at the fault zone and non-fault zone positions. We obtained a new fault probability cube as new attributes. Finally, we analyzed a typical strike-slip fault zone using the new attributes. This study provides an effective way of fault zone imaging from seismic data and adds new insights into its geometry. Therefore, the workflows used here could be widely applied to other 3D surveys.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129076744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-18DOI: 10.3997/2214-4609.202113162
B. Du, J. Gao, X. Li, G. Zhang, X. Guo, R. Jiang, R. Yang
Summary Shale gas reservoir in deep burial has the features of developed fracture and complex in-situ stress property. To improve the accuracy of stress prediction for shale gas reservoir, we proposed that Differential Horizontal Stress Ratio (DHSR) evaluate the in-situ stress property for shale gas reservoir in function of Poisson’s ratio and fracture density. First of all, new rock physics model for shale gas reservoir is established considering the influence of Total Organic Content (TOC), fracture and anisotropy. Then, pre-stack angle gathers of different azimuthal angles is obtained by Offset Vector Tile (OVT) processing. Finally, pre-stack seismic anisotropy inversion was executed to obtain the Poisson’s ratio and fracture density. DHSR can be estimated by above two parameters. The real data test demonstrated that the predicted DHSR is consisting with prior geology information. The stress evaluation can offer useful geophysical evidence for hydraulic fracturing and well track design.
{"title":"The Application of in-situ Stress Prediction in Shale Gas Reservoir Through Pre-stack Seismic Anisotropy Inversion – A Case Study from Sichuan Basin, SW China.","authors":"B. Du, J. Gao, X. Li, G. Zhang, X. Guo, R. Jiang, R. Yang","doi":"10.3997/2214-4609.202113162","DOIUrl":"https://doi.org/10.3997/2214-4609.202113162","url":null,"abstract":"Summary Shale gas reservoir in deep burial has the features of developed fracture and complex in-situ stress property. To improve the accuracy of stress prediction for shale gas reservoir, we proposed that Differential Horizontal Stress Ratio (DHSR) evaluate the in-situ stress property for shale gas reservoir in function of Poisson’s ratio and fracture density. First of all, new rock physics model for shale gas reservoir is established considering the influence of Total Organic Content (TOC), fracture and anisotropy. Then, pre-stack angle gathers of different azimuthal angles is obtained by Offset Vector Tile (OVT) processing. Finally, pre-stack seismic anisotropy inversion was executed to obtain the Poisson’s ratio and fracture density. DHSR can be estimated by above two parameters. The real data test demonstrated that the predicted DHSR is consisting with prior geology information. The stress evaluation can offer useful geophysical evidence for hydraulic fracturing and well track design.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127994817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-18DOI: 10.3997/2214-4609.202112541
U. Waheed, T. Alkhalifah, B. Li, E. Haghighat, A. Stovas, J. Virieux
Summary Traveltimes corresponding to both compressional and shear waves are needed for many applications in seismology ranging from seismic imaging to earthquake localization. Since the behavior of shear waves in anisotropic media is considerably more complicated than the isotropic case, accurate traveltime computation for shear waves in anisotropic media remains a challenge. Ray tracing methods are often used to compute qSV wave traveltimes but they become unstable around triplication points and, therefore, we often use the weak anisotropy approximation. Here, we employ the emerging paradigm of physics-informed neural networks to solve transversely isotropic eikonal equation for the qSV wave that otherwise are not easily solvable using conventional finite difference methods. By minimizing a loss function formed by imposing the validity of eikonal equation, we train a neural network to produce traveltime solutions that are consistent with the underlying equation. Through tests on synthetic models, we show that the method is capable of producing accurate qSV wave traveltimes even at triplication points and works for arbitrary strength of medium anisotropy.
{"title":"Traveltime Computation for qSV Waves in TI Media Using Physics-Informed Neural Networks","authors":"U. Waheed, T. Alkhalifah, B. Li, E. Haghighat, A. Stovas, J. Virieux","doi":"10.3997/2214-4609.202112541","DOIUrl":"https://doi.org/10.3997/2214-4609.202112541","url":null,"abstract":"Summary Traveltimes corresponding to both compressional and shear waves are needed for many applications in seismology ranging from seismic imaging to earthquake localization. Since the behavior of shear waves in anisotropic media is considerably more complicated than the isotropic case, accurate traveltime computation for shear waves in anisotropic media remains a challenge. Ray tracing methods are often used to compute qSV wave traveltimes but they become unstable around triplication points and, therefore, we often use the weak anisotropy approximation. Here, we employ the emerging paradigm of physics-informed neural networks to solve transversely isotropic eikonal equation for the qSV wave that otherwise are not easily solvable using conventional finite difference methods. By minimizing a loss function formed by imposing the validity of eikonal equation, we train a neural network to produce traveltime solutions that are consistent with the underlying equation. Through tests on synthetic models, we show that the method is capable of producing accurate qSV wave traveltimes even at triplication points and works for arbitrary strength of medium anisotropy.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126040985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-18DOI: 10.3997/2214-4609.202112547
P. Pereira, J. Carneiro, C. Ribeiro, J. M. Martins
Summary The relevance of technological solutions as Carbon Capture, Utilization and Storage (CCUS) have been increasing with the expectation to play a fundamental role in the next decades to mitigate CO2 emissions in Europe and worldwide, essentially those associated to the industrial sectors. Under the scope of the ongoing STRATEGY CCUS project, this work comprises a feasibility study in the Lusitanian basin, a Portuguese promising region with a large potential for CO2 storage, applied to seventeen storage units: thirteen offshore and four onshore. Different methods are presented for a storage resource assessment, including the Boston Square Analysis (BSA) and a four-tiered storage capacity pyramid. The last method aimed to determine critical CO2 storage parameters, namely injectivity and storage capacity, under a stochastic framework with the application of Monte Carlo simulations and a sensitivity analysis of reservoir petrophysical properties. The results from BSA allowed the identification of main gaps and strengths for all storage units in the Lusitanian basin. In addition, the total storage capacity of this basin is about 3.12 Gt CO2, based on stochastic modelling approach, although most the deep saline aquifers were classified as theoretical resources and therefore further characterisation studies must be conducted to increase their maturation level.
{"title":"Resource Maturity and Sensitivity Analysis of CO2 Storage Capacity in the Lusitanian Basin, Portugal","authors":"P. Pereira, J. Carneiro, C. Ribeiro, J. M. Martins","doi":"10.3997/2214-4609.202112547","DOIUrl":"https://doi.org/10.3997/2214-4609.202112547","url":null,"abstract":"Summary The relevance of technological solutions as Carbon Capture, Utilization and Storage (CCUS) have been increasing with the expectation to play a fundamental role in the next decades to mitigate CO2 emissions in Europe and worldwide, essentially those associated to the industrial sectors. Under the scope of the ongoing STRATEGY CCUS project, this work comprises a feasibility study in the Lusitanian basin, a Portuguese promising region with a large potential for CO2 storage, applied to seventeen storage units: thirteen offshore and four onshore. Different methods are presented for a storage resource assessment, including the Boston Square Analysis (BSA) and a four-tiered storage capacity pyramid. The last method aimed to determine critical CO2 storage parameters, namely injectivity and storage capacity, under a stochastic framework with the application of Monte Carlo simulations and a sensitivity analysis of reservoir petrophysical properties. The results from BSA allowed the identification of main gaps and strengths for all storage units in the Lusitanian basin. In addition, the total storage capacity of this basin is about 3.12 Gt CO2, based on stochastic modelling approach, although most the deep saline aquifers were classified as theoretical resources and therefore further characterisation studies must be conducted to increase their maturation level.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"94 1-2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116608460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-18DOI: 10.3997/2214-4609.202113256
B. Wang, F. Zhang
Summary present a SH-SH wave inversion method for the transversely isotropic media with vertical axis of symmetry (VTI media) based on a modified approximation of the SH-SH wave reflection coefficient.
{"title":"Inversion of SH-SH wave anisotropy parameters in VTI media","authors":"B. Wang, F. Zhang","doi":"10.3997/2214-4609.202113256","DOIUrl":"https://doi.org/10.3997/2214-4609.202113256","url":null,"abstract":"Summary present a SH-SH wave inversion method for the transversely isotropic media with vertical axis of symmetry (VTI media) based on a modified approximation of the SH-SH wave reflection coefficient.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"158 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121355760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-18DOI: 10.3997/2214-4609.202113339
J. Lin, E. Haber
Summary This paper avoids the difficulties in using conventional methods in lithology segmentation task by putting the tasks in the frame of computer vision. First, we setup a lithology dataset which contains paired topology, satellite and lithology images; Second, two heated neural networks HyperNet and UNet are introduced and applied in lithology segmentation task. The experiments show that both HyperNet and UNet are efficient and promising for the application in lithology segmentation. % Neural networks can increase the predicted accuracy three times than random guess, that greatly reduce the workload of professional lithology geologist.
{"title":"Lithology segmentation using deep neural network","authors":"J. Lin, E. Haber","doi":"10.3997/2214-4609.202113339","DOIUrl":"https://doi.org/10.3997/2214-4609.202113339","url":null,"abstract":"Summary This paper avoids the difficulties in using conventional methods in lithology segmentation task by putting the tasks in the frame of computer vision. First, we setup a lithology dataset which contains paired topology, satellite and lithology images; Second, two heated neural networks HyperNet and UNet are introduced and applied in lithology segmentation task. The experiments show that both HyperNet and UNet are efficient and promising for the application in lithology segmentation. % Neural networks can increase the predicted accuracy three times than random guess, that greatly reduce the workload of professional lithology geologist.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131099845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-18DOI: 10.3997/2214-4609.202113138
P. Tempone, S. Merten, S. V. Puijvelde, M. Shkrob, F. V. D. Broek
Summary Knowing which overpressure mechanisms are likely to occur in basins can improve the ability to predict abnormal pressures and can thus provide vital information to better manage exploration and drilling risks. Overpressure formation has been studied extensively in the scientific literature, however narrowing down publications to relevant results and linking these publications to a position on the globe in a systematic way can be difficult and time-consuming. In this study, we used semantic search and advanced analytics to screen over 120,000 reviewed and georeferenced publications for mentions of overpressure formation to a basket of ~1100 publications and subsequently analysed results for temporal and spatial trends, allowing the pore pressure specialist to focus on studying data to assess the implications and risk instead of spending time searching for relevant information and data.
{"title":"Leveraging semantic search and advanced analytics to map studies on overpressure mechanisms across the globe","authors":"P. Tempone, S. Merten, S. V. Puijvelde, M. Shkrob, F. V. D. Broek","doi":"10.3997/2214-4609.202113138","DOIUrl":"https://doi.org/10.3997/2214-4609.202113138","url":null,"abstract":"Summary Knowing which overpressure mechanisms are likely to occur in basins can improve the ability to predict abnormal pressures and can thus provide vital information to better manage exploration and drilling risks. Overpressure formation has been studied extensively in the scientific literature, however narrowing down publications to relevant results and linking these publications to a position on the globe in a systematic way can be difficult and time-consuming. In this study, we used semantic search and advanced analytics to screen over 120,000 reviewed and georeferenced publications for mentions of overpressure formation to a basket of ~1100 publications and subsequently analysed results for temporal and spatial trends, allowing the pore pressure specialist to focus on studying data to assess the implications and risk instead of spending time searching for relevant information and data.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133279120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-18DOI: 10.3997/2214-4609.202113229
H. Song, F. Zhang, J. Fan, Q. Chen, F. Qiu
Summary In this paper, a gamma detection system consisting of a cesium-137 gamma source and seven gamma detectors is used to evaluate the azimuthal density of cement sheath in horizontal wells. In order to eliminate the impact to gamma detection information caused by casing eccentricity and formation density changes in horizontal wells and obtain correct density results, this paper give the theoratical response of gamma detectors at different source distances and azimuths and raise an inverse method based on Newton iteration and truncated singular values Regularization.Simulation examples with different conditions are designed to verify the inversion results
{"title":"An Inversion Method to Determine Azimuthal Density for Cement Evaluation in Horizontal Well","authors":"H. Song, F. Zhang, J. Fan, Q. Chen, F. Qiu","doi":"10.3997/2214-4609.202113229","DOIUrl":"https://doi.org/10.3997/2214-4609.202113229","url":null,"abstract":"Summary In this paper, a gamma detection system consisting of a cesium-137 gamma source and seven gamma detectors is used to evaluate the azimuthal density of cement sheath in horizontal wells. In order to eliminate the impact to gamma detection information caused by casing eccentricity and formation density changes in horizontal wells and obtain correct density results, this paper give the theoratical response of gamma detectors at different source distances and azimuths and raise an inverse method based on Newton iteration and truncated singular values Regularization.Simulation examples with different conditions are designed to verify the inversion results","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115729876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-18DOI: 10.3997/2214-4609.202113121
J. Paffenholz, A. Goertz
Summary In order to limit the global warming of the planet to below 2o C, models show that net-zero release of anthropomorphic CO2 must be achieved by the mid-century. Since for the foreseeable future the most of the world’s energy will still be provided by fossil fuels, other methods besides expanding the contribution of renewable energy are called for. According to the Intergovernmental Panel on Climate Change (IPCC), Carbon (short for carbon dioxide) Capture and Sequestration (CCS) is one such method. To achieve this climate goal current CCS efforts must increase by approximately 100-fold within the next 20 years. Geophysical simulations on suitable geologic models will provide an important tool to streamline and accelerate the vast expansion of site characterization and long term monitoring tasks to ensure the success of such large-scale CCS application.
{"title":"The Critical Role of Geophysical Simulations in Enhanced Carbon Storage","authors":"J. Paffenholz, A. Goertz","doi":"10.3997/2214-4609.202113121","DOIUrl":"https://doi.org/10.3997/2214-4609.202113121","url":null,"abstract":"Summary In order to limit the global warming of the planet to below 2o C, models show that net-zero release of anthropomorphic CO2 must be achieved by the mid-century. Since for the foreseeable future the most of the world’s energy will still be provided by fossil fuels, other methods besides expanding the contribution of renewable energy are called for. According to the Intergovernmental Panel on Climate Change (IPCC), Carbon (short for carbon dioxide) Capture and Sequestration (CCS) is one such method. To achieve this climate goal current CCS efforts must increase by approximately 100-fold within the next 20 years. Geophysical simulations on suitable geologic models will provide an important tool to streamline and accelerate the vast expansion of site characterization and long term monitoring tasks to ensure the success of such large-scale CCS application.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114503079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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