Pub Date : 2021-10-18DOI: 10.3997/2214-4609.202112724
G. Niu, S. Wang, C. Zhou
Summary Accurate near-surface velocity structure is the key to improve the precision of statics and seismic imaging. We propose a novel method for complex near-surface velocity modeling based on a modified U-net from pre-stack seismic data. The method makes use of waveform information rather than travel time only. We design a number of complex near-surface velocity models and simulate shot gathers using the finite difference scheme. During the forward stage, the network develops a nonlinear relationship between the multi-shot seismic data and the corresponding velocity models. During the inversion stage, the trained network can be used to predict velocity models from the new shot gathers in a few minutes. Supported by numerical experiments on synthetic models, this method achieve a promising performance in complex near-surface velocity inversion.
{"title":"Complex Near-surface Velocity Modeling via U-net","authors":"G. Niu, S. Wang, C. Zhou","doi":"10.3997/2214-4609.202112724","DOIUrl":"https://doi.org/10.3997/2214-4609.202112724","url":null,"abstract":"Summary Accurate near-surface velocity structure is the key to improve the precision of statics and seismic imaging. We propose a novel method for complex near-surface velocity modeling based on a modified U-net from pre-stack seismic data. The method makes use of waveform information rather than travel time only. We design a number of complex near-surface velocity models and simulate shot gathers using the finite difference scheme. During the forward stage, the network develops a nonlinear relationship between the multi-shot seismic data and the corresponding velocity models. During the inversion stage, the trained network can be used to predict velocity models from the new shot gathers in a few minutes. Supported by numerical experiments on synthetic models, this method achieve a promising performance in complex near-surface velocity inversion.","PeriodicalId":143998,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"23 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":"121115866","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: 10.3997/2214-4609.202112701
M. Farooqui, D. Carotti, M. Al-Jahdhami
Summary The geology of northern Oman presents significant challenges for land velocity model building. We show in this paper that these challenges can be overcome by using an integrated high-resolution velocity model workflow, through the combination of different types of waves, that allow resolving different parts of the velocity model. This dedicated workflow consists of Multi-Wave Inversion (MWI) for the near-surface, followed by Optimal Transport Full Waveform Inversion (OT-FWI) and then by ray-based joint reflected and diving wave tomography inversion. It resolves challenges imposed by complex shallow geology and allows for proper imaging of deeper structures. Compared to a conventional ray-based only model building flow, the integrated high-resolution workflow enabled generating a geologically plausible velocity model which minimizes depth positioning errors and greatly enhances structural and stratigraphic trends.
{"title":"Integrated high-resolution model building: a case study from the Sultanate of Oman","authors":"M. Farooqui, D. Carotti, M. Al-Jahdhami","doi":"10.3997/2214-4609.202112701","DOIUrl":"https://doi.org/10.3997/2214-4609.202112701","url":null,"abstract":"Summary The geology of northern Oman presents significant challenges for land velocity model building. We show in this paper that these challenges can be overcome by using an integrated high-resolution velocity model workflow, through the combination of different types of waves, that allow resolving different parts of the velocity model. This dedicated workflow consists of Multi-Wave Inversion (MWI) for the near-surface, followed by Optimal Transport Full Waveform Inversion (OT-FWI) and then by ray-based joint reflected and diving wave tomography inversion. It resolves challenges imposed by complex shallow geology and allows for proper imaging of deeper structures. Compared to a conventional ray-based only model building flow, the integrated high-resolution workflow enabled generating a geologically plausible velocity model which minimizes depth positioning errors and greatly enhances structural and stratigraphic trends.","PeriodicalId":143998,"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":"125850438","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: 10.3997/2214-4609.202112771
Y. Chen, L. Zhao, J. Pan, C. Li, K. Li, F. Zhang, J. Geng
Summary Seismic characterization of deep carbonate reservoir is challenging due to the heterogeneous reservoir properties caused by the complex diagenesis and deep buried physical conditions. We propose a variety of physical constraints (including spatial constraint, continuity constraint, gradient constraint and category constraint) to guide the machine learning (Random Forest method) for reservoir quality prediction using multi-seismic attributes. Taking the carbonate reservoirs in the Tarim Basin, Western China as an example, we demonstrate that, various physical constraints are effective in enhancing the prediction performance based on the well test. The combination of the four proposed physical constraints gives the best prediction performance in terms of identifying reservoir and non-reservoir as well as inferring reservoir quality. We also show that a two-step strategy gives higher F1 score for reservoir quality evaluation. Machine learning based seismic prediction of deep carbonate reservoir with physical constraints suggests that this approach can effectively delineate the heterogeneous reservoir distribution, laying the foundation for geological model building and sweet spot detection.
{"title":"Machine learning based deep carbonate reservoir characterization with physical constraints","authors":"Y. Chen, L. Zhao, J. Pan, C. Li, K. Li, F. Zhang, J. Geng","doi":"10.3997/2214-4609.202112771","DOIUrl":"https://doi.org/10.3997/2214-4609.202112771","url":null,"abstract":"Summary Seismic characterization of deep carbonate reservoir is challenging due to the heterogeneous reservoir properties caused by the complex diagenesis and deep buried physical conditions. We propose a variety of physical constraints (including spatial constraint, continuity constraint, gradient constraint and category constraint) to guide the machine learning (Random Forest method) for reservoir quality prediction using multi-seismic attributes. Taking the carbonate reservoirs in the Tarim Basin, Western China as an example, we demonstrate that, various physical constraints are effective in enhancing the prediction performance based on the well test. The combination of the four proposed physical constraints gives the best prediction performance in terms of identifying reservoir and non-reservoir as well as inferring reservoir quality. We also show that a two-step strategy gives higher F1 score for reservoir quality evaluation. Machine learning based seismic prediction of deep carbonate reservoir with physical constraints suggests that this approach can effectively delineate the heterogeneous reservoir distribution, laying the foundation for geological model building and sweet spot detection.","PeriodicalId":143998,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"42 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":"126414803","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: 10.3997/2214-4609.202112768
S. Rentsch, M. Silverberg
Summary In modern product development, assessment and maintenance the use of digital twins is gaining momentum. In its simplest form a digital twin is a virtual model replicating a potential or actual physical product, system, process and/or service in part or in its entirety. In this abstract we show how we created a digital twin for a towed streamer platform by mapping significant parts of the streamer platform elements from a physical platform onto a digital replica. We demonstrate how we integrated simulators for navigation, aimpoints, spread health and seismic data into the digital twin such that we can generate simulations representative of data streams without live streaming all the data from a physical twin. The digital twin facilitates the derivation of maintenance prediction models, compute resource models and acquisition scenario boundaries. We show an example of how the digital twin can give us options for safely reallocating computational resources in scenarios of increasingly challenging sea states or to improve productivity by acquiring a survey faster.
{"title":"Towards digital twinning for single sensor streamer platforms","authors":"S. Rentsch, M. Silverberg","doi":"10.3997/2214-4609.202112768","DOIUrl":"https://doi.org/10.3997/2214-4609.202112768","url":null,"abstract":"Summary In modern product development, assessment and maintenance the use of digital twins is gaining momentum. In its simplest form a digital twin is a virtual model replicating a potential or actual physical product, system, process and/or service in part or in its entirety. In this abstract we show how we created a digital twin for a towed streamer platform by mapping significant parts of the streamer platform elements from a physical platform onto a digital replica. We demonstrate how we integrated simulators for navigation, aimpoints, spread health and seismic data into the digital twin such that we can generate simulations representative of data streams without live streaming all the data from a physical twin. The digital twin facilitates the derivation of maintenance prediction models, compute resource models and acquisition scenario boundaries. We show an example of how the digital twin can give us options for safely reallocating computational resources in scenarios of increasingly challenging sea states or to improve productivity by acquiring a survey faster.","PeriodicalId":143998,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"41 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":"130555035","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: 10.3997/2214-4609.202112777
T. Shibayama, N. Mizuno, H. Kusano, A. Kinoshita, M. Minegishi, R. Sakamoto, K. Hasegawa, F. Kachi
Summary Deep learning has the potential to estimate velocity models directly from shot gathers, which would reduce the turn-around time of seismic inversion. Our study addresses two challenges in implementing deep learning techniques for seismic inversion: the practical generation of a large amount of training data and the search for the best neural network architecture. First, we propose a flexible system which parametrically generates velocity models to create a large-scale, complex and fully synthetic training dataset, without using a target subsurface model. Using this system, we created 300,000 synthetic velocity models for our experiments. Second, we employ neural architecture search techniques to design a suitable neural network using Optuna, an automatic hyperparameter optimisation framework. We incorporated the residual network into an encoder–decoder model and optimised its architecture. Thus, we obtained an optimal neural network model consisting of more than 100 hidden layers. We evaluated our model using the Marmousi2 model and the 1994 Amoco statics test dataset. The model demonstrated comprehensible estimations of the benchmark velocity models.
{"title":"Practical deep learning inversion using neural architecture search and a flexible training dataset generator","authors":"T. Shibayama, N. Mizuno, H. Kusano, A. Kinoshita, M. Minegishi, R. Sakamoto, K. Hasegawa, F. Kachi","doi":"10.3997/2214-4609.202112777","DOIUrl":"https://doi.org/10.3997/2214-4609.202112777","url":null,"abstract":"Summary Deep learning has the potential to estimate velocity models directly from shot gathers, which would reduce the turn-around time of seismic inversion. Our study addresses two challenges in implementing deep learning techniques for seismic inversion: the practical generation of a large amount of training data and the search for the best neural network architecture. First, we propose a flexible system which parametrically generates velocity models to create a large-scale, complex and fully synthetic training dataset, without using a target subsurface model. Using this system, we created 300,000 synthetic velocity models for our experiments. Second, we employ neural architecture search techniques to design a suitable neural network using Optuna, an automatic hyperparameter optimisation framework. We incorporated the residual network into an encoder–decoder model and optimised its architecture. Thus, we obtained an optimal neural network model consisting of more than 100 hidden layers. We evaluated our model using the Marmousi2 model and the 1994 Amoco statics test dataset. The model demonstrated comprehensible estimations of the benchmark velocity models.","PeriodicalId":143998,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"22 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":"129495586","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: 10.3997/2214-4609.202112700
T. Tangkijwanichakul, Sergey Fomel
Summary We approximate the inverse Hessian operator by a chain of weights in time/space and frequency domains. Tests on synthetic data show that this approach provides an effective approximation while having the minimal cost of forward and inverse FFTs (Fast FourierTransforms). The method can be applied either for compensating migrated images or in the form of a preconditioner inside iterative least-squares reverse-time migration (LSRTM). As demonstrated by experiments with synthetic data, the latter significantly accelerates the convergence of LSRTM and achieves high-quality imaging results in fewer iterations.
{"title":"Inverse Hessian estimation in least-squares migration using chains of operators","authors":"T. Tangkijwanichakul, Sergey Fomel","doi":"10.3997/2214-4609.202112700","DOIUrl":"https://doi.org/10.3997/2214-4609.202112700","url":null,"abstract":"Summary We approximate the inverse Hessian operator by a chain of weights in time/space and frequency domains. Tests on synthetic data show that this approach provides an effective approximation while having the minimal cost of forward and inverse FFTs (Fast FourierTransforms). The method can be applied either for compensating migrated images or in the form of a preconditioner inside iterative least-squares reverse-time migration (LSRTM). As demonstrated by experiments with synthetic data, the latter significantly accelerates the convergence of LSRTM and achieves high-quality imaging results in fewer iterations.","PeriodicalId":143998,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"111 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":"126005089","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: 10.3997/2214-4609.202112776
A. Górszczyk, S. Sambolian, S. Operto
Summary We present an extension of the 2004 BP velocity model which is suitable for the assessment of cutting-edge seismic imaging methods as FWI applied to ultra long-offset ocean-bottom node (OBN) acquisitions. The 2004 BP model is routinely utilized to benchmark various velocity-model building approaches - in particular those developed to tackle the challenges encountered in geological settings comprising salt structures. Those challenges are typically related to the correct reconstruction of the subsalt structures or the sharp velocity contrasts between the salt bodies and the surrounding sediments. To make this model suitable for testing the emerging long-offset OBN acquisitions, we embed the original 2004 BP model within a crustal-scale velocity model inspired by the structural interpretation of the tomographic results from the GUMBO experiment (Gulf of Mexico). The resulting model allows for wavefield propagation within the rifted continental crust and the upper mantle and therefore for the undershooting of the salt and subsalt structures. Consequently, there is no need for extrapolation of the original BP model boundaries or resizing/resampling of its spatial dimensions. The GOMCRUST can therefore be seen as a geologically consistent evolution of the 2004 BP model, which allows to benchmark various seismic imaging workflow with long-offset OBN surveys.
{"title":"GOMCRUST - The crustal-scale extension of the 2004 BP velocity model for long-offset OBN acquisition setting","authors":"A. Górszczyk, S. Sambolian, S. Operto","doi":"10.3997/2214-4609.202112776","DOIUrl":"https://doi.org/10.3997/2214-4609.202112776","url":null,"abstract":"Summary We present an extension of the 2004 BP velocity model which is suitable for the assessment of cutting-edge seismic imaging methods as FWI applied to ultra long-offset ocean-bottom node (OBN) acquisitions. The 2004 BP model is routinely utilized to benchmark various velocity-model building approaches - in particular those developed to tackle the challenges encountered in geological settings comprising salt structures. Those challenges are typically related to the correct reconstruction of the subsalt structures or the sharp velocity contrasts between the salt bodies and the surrounding sediments. To make this model suitable for testing the emerging long-offset OBN acquisitions, we embed the original 2004 BP model within a crustal-scale velocity model inspired by the structural interpretation of the tomographic results from the GUMBO experiment (Gulf of Mexico). The resulting model allows for wavefield propagation within the rifted continental crust and the upper mantle and therefore for the undershooting of the salt and subsalt structures. Consequently, there is no need for extrapolation of the original BP model boundaries or resizing/resampling of its spatial dimensions. The GOMCRUST can therefore be seen as a geologically consistent evolution of the 2004 BP model, which allows to benchmark various seismic imaging workflow with long-offset OBN surveys.","PeriodicalId":143998,"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":"114364658","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: 10.3997/2214-4609.202112741
W. Cheng, S. Wang, C. Zhou, G. Pang
Summary Due to the absorption of subsurface media, seismic waves experience energy attenuation and waveform distortion, which seriously decreases the resolution of seismic data. For prestack seismic data, since the effect of absorption attenuation varies with the propagation path, the amplitude variation with angle (AVA) trend will be distorted. Therefore, we propose a novel prestack attenuation compensation method based on inversion considering the influence of ray paths on the absorption attenuation. We first derive the frequency domain forward formula of the prestack gather in the attenuation media, then reduce the attenuation compensation to an inverse problem, and utilize Tikhonov regularization for stability processing to achieve compensation. Numerical tests, comparative analysis of different compensation methods and noise immunity experiment demonstrate that our method has higher accuracy and can perform attenuation compensation for prestack gather more stably and effectively.
{"title":"Prestack data attenuation compensation based on inversion","authors":"W. Cheng, S. Wang, C. Zhou, G. Pang","doi":"10.3997/2214-4609.202112741","DOIUrl":"https://doi.org/10.3997/2214-4609.202112741","url":null,"abstract":"Summary Due to the absorption of subsurface media, seismic waves experience energy attenuation and waveform distortion, which seriously decreases the resolution of seismic data. For prestack seismic data, since the effect of absorption attenuation varies with the propagation path, the amplitude variation with angle (AVA) trend will be distorted. Therefore, we propose a novel prestack attenuation compensation method based on inversion considering the influence of ray paths on the absorption attenuation. We first derive the frequency domain forward formula of the prestack gather in the attenuation media, then reduce the attenuation compensation to an inverse problem, and utilize Tikhonov regularization for stability processing to achieve compensation. Numerical tests, comparative analysis of different compensation methods and noise immunity experiment demonstrate that our method has higher accuracy and can perform attenuation compensation for prestack gather more stably and effectively.","PeriodicalId":143998,"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":"114562904","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: 10.3997/2214-4609.202112728
H. Miyamoto, G. Cambois
Summary Seismic data processing by pre-stack depth migration (PrSDM) requires a reliable initial velocity model. An accurate velocity model secures pre-stack gather flatness by short offset spread; however, a vertical transverse isotropy (VTI) model, for characterizing horizontal layering, should be sufficiently considered to extend offset usage and maximize image quality. This study sought a robust workflow of Thomsen VTI parameters, e and δ, estimation to stabilize anisotropic tomography analysis. Vertical and deviated wells offered the opportunity to derive the target parameters in a rather simple and elegant way. Anisotropic Backus averaging combined intrinsic and apparent anisotropy at seismic scale. In our case study, the calculated anisotropic parameters profiles were validated by WAVSPs and by the surface seismic data, which could be flattened effectively all the way to the largest offsets. In particular, steps like refraction FWI need an accurate anisotropic starting model to converge effectively. Cross-spread 3D seismic surveys are particularly ill suited for deriving shallow anisotropic velocity models and the vertical and deviated wells method provides a welcome alternative.
{"title":"Estimation of Thomsen VTI parameters for seismic imaging using vertical and deviated wells","authors":"H. Miyamoto, G. Cambois","doi":"10.3997/2214-4609.202112728","DOIUrl":"https://doi.org/10.3997/2214-4609.202112728","url":null,"abstract":"Summary Seismic data processing by pre-stack depth migration (PrSDM) requires a reliable initial velocity model. An accurate velocity model secures pre-stack gather flatness by short offset spread; however, a vertical transverse isotropy (VTI) model, for characterizing horizontal layering, should be sufficiently considered to extend offset usage and maximize image quality. This study sought a robust workflow of Thomsen VTI parameters, e and δ, estimation to stabilize anisotropic tomography analysis. Vertical and deviated wells offered the opportunity to derive the target parameters in a rather simple and elegant way. Anisotropic Backus averaging combined intrinsic and apparent anisotropy at seismic scale. In our case study, the calculated anisotropic parameters profiles were validated by WAVSPs and by the surface seismic data, which could be flattened effectively all the way to the largest offsets. In particular, steps like refraction FWI need an accurate anisotropic starting model to converge effectively. Cross-spread 3D seismic surveys are particularly ill suited for deriving shallow anisotropic velocity models and the vertical and deviated wells method provides a welcome alternative.","PeriodicalId":143998,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"85 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":"128967917","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: 10.3997/2214-4609.202112772
N. Salaun, M. Pouget, Z. Yu, C. Beigbeder, A. Rivet, S. Dega, M. Peiro, A. Lafram, A. Grandi, E. Jungo
Summary Time-lapse seismic is now being used more frequently to assist reservoir development, prevent infrastructure damage or monitor geological storage. To better reveal true 4D signals while suppressing acquisition-related noise as a result of, for example, water velocity changes, source positioning errors etc., a new processing flow which focusses on correcting each noise-contributing factor based on its physical characteristics, has been developed to replace the conventional non-deterministic correction approach based on cross-survey matching. Our proposed flow is based on using common water bottom and the water-bottom travel time to invert each factor and correct for it, which allows for processing of each monitor survey independently and the possible acceleration of standard 4D processing timelines. We applied this workflow on two fields offshore Angola, one with strong subsidence and one without, and showed the superiority of this new approach to reveal the true 4D information. The subsidence effect, observable from the reservoir up to the water bottom, now better matches with the model of pressure changes in the new 4D results compared to legacy results. Even for field experiencing no subsidence effect, the time shift and NRMS maps obtained at the reservoir level are cleaner and easier to interpret from new flow.
{"title":"A deterministic 4D processing flow to suppress acquisition-related noise at Dalia and Rosa fields","authors":"N. Salaun, M. Pouget, Z. Yu, C. Beigbeder, A. Rivet, S. Dega, M. Peiro, A. Lafram, A. Grandi, E. Jungo","doi":"10.3997/2214-4609.202112772","DOIUrl":"https://doi.org/10.3997/2214-4609.202112772","url":null,"abstract":"Summary Time-lapse seismic is now being used more frequently to assist reservoir development, prevent infrastructure damage or monitor geological storage. To better reveal true 4D signals while suppressing acquisition-related noise as a result of, for example, water velocity changes, source positioning errors etc., a new processing flow which focusses on correcting each noise-contributing factor based on its physical characteristics, has been developed to replace the conventional non-deterministic correction approach based on cross-survey matching. Our proposed flow is based on using common water bottom and the water-bottom travel time to invert each factor and correct for it, which allows for processing of each monitor survey independently and the possible acceleration of standard 4D processing timelines. We applied this workflow on two fields offshore Angola, one with strong subsidence and one without, and showed the superiority of this new approach to reveal the true 4D information. The subsidence effect, observable from the reservoir up to the water bottom, now better matches with the model of pressure changes in the new 4D results compared to legacy results. Even for field experiencing no subsidence effect, the time shift and NRMS maps obtained at the reservoir level are cleaner and easier to interpret from new flow.","PeriodicalId":143998,"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":"130933436","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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