Pub Date : 2019-04-04DOI: 10.3997/2214-4609.201900034
J. Ramos-Martínez, A. Valenciano, N. Chemingui, T. Martin
Summary Full Waveform Inversion (FWI) can create on an inaccurate model as a result of cycle skipping, if the initial model is not close enough to the true one, or there is insufficient low frequencies in the data. Furthermore, FWI model updates can be affected by a reflectivity imprint prior to the resolution of long-wavelength features. Imaging with the resulting incorrect model will create structural uncertainty, and will hamper an evaluation of potential prospects. Cycle skipping can be mitigated by using a robust norm for measuring the data misfit (W2-norm), instead of a traditional L2-norm. Used with a velocity gradient that removes the imprint of the reflectivity, we demonstrate an application to data resolving a high-velocity layer that was not present in the inital model. Corroborated by well data, the resulting earth model accurately reflects the subsurface, which, in turn, reduces uncertainty in the final structural image.
{"title":"Use of a Robust Norm in Reducing FWI Uncertainty in the Presence of Cycle Skipping","authors":"J. Ramos-Martínez, A. Valenciano, N. Chemingui, T. Martin","doi":"10.3997/2214-4609.201900034","DOIUrl":"https://doi.org/10.3997/2214-4609.201900034","url":null,"abstract":"Summary Full Waveform Inversion (FWI) can create on an inaccurate model as a result of cycle skipping, if the initial model is not close enough to the true one, or there is insufficient low frequencies in the data. Furthermore, FWI model updates can be affected by a reflectivity imprint prior to the resolution of long-wavelength features. Imaging with the resulting incorrect model will create structural uncertainty, and will hamper an evaluation of potential prospects. Cycle skipping can be mitigated by using a robust norm for measuring the data misfit (W2-norm), instead of a traditional L2-norm. Used with a velocity gradient that removes the imprint of the reflectivity, we demonstrate an application to data resolving a high-velocity layer that was not present in the inital model. Corroborated by well data, the resulting earth model accurately reflects the subsurface, which, in turn, reduces uncertainty in the final structural image.","PeriodicalId":350524,"journal":{"name":"Second EAGE/PESGB Workshop on Velocities","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125910812","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 : 2019-04-04DOI: 10.3997/2214-4609.201900035
G. Eisenberg-Klein, E. Verschuur, S. Qu, E. Schünemann
Summary Data driven Velocity Model Building (VMB) based on Full Waveform Inversion requires very broad band, especially low frequnecy data content to overcome the cycle skipping problem. In this paper we demonstrate how the Joint Migration Inversion method introduced by the DELHPI consortium group applied in a cascaded workflow to preduce a hich quality velocity model to start and reduce efforts in Full Waveform Inversion.
{"title":"JMI-FWI: Cascading Workflow Using Joint Migration Inversion (JMI) and Full Waveform Inversion (FWI)","authors":"G. Eisenberg-Klein, E. Verschuur, S. Qu, E. Schünemann","doi":"10.3997/2214-4609.201900035","DOIUrl":"https://doi.org/10.3997/2214-4609.201900035","url":null,"abstract":"Summary Data driven Velocity Model Building (VMB) based on Full Waveform Inversion requires very broad band, especially low frequnecy data content to overcome the cycle skipping problem. In this paper we demonstrate how the Joint Migration Inversion method introduced by the DELHPI consortium group applied in a cascaded workflow to preduce a hich quality velocity model to start and reduce efforts in Full Waveform Inversion.","PeriodicalId":350524,"journal":{"name":"Second EAGE/PESGB Workshop on Velocities","volume":"295 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133848432","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 : 2019-04-04DOI: 10.3997/2214-4609.201900048
D. Rovetta, D. Colombo, A. Kontakis, E. S. Curiel
Summary Desert environments are often characterized by areas with complex geological structures affecting seismic imaging in geophysical exploration. A good velocity model building tool is needed to deal with these difficult systems where geophysical inversion is affected by high non uniqueness or variable sensitivity to the targets. We approach this problem by making use of a recently developed automatic and data-driven surface-consistent refraction method. The developed method is focusing on the analysis of phases (pQC) and amplitudes (aQC) of refracted arrivals. We successfully applied the methodology to many 3D land and marine seismic datasets. As a land example, we show the results for a prominent wadi. Another example is related to marine acquisitions characterized by salt and evaporitic sequences composed of evaporitic and clastic sediments.
{"title":"Application of an Automatic and Data-driven Surface-consistent Refraction Method to Complex Geology Scenarios in Desert Environment","authors":"D. Rovetta, D. Colombo, A. Kontakis, E. S. Curiel","doi":"10.3997/2214-4609.201900048","DOIUrl":"https://doi.org/10.3997/2214-4609.201900048","url":null,"abstract":"Summary Desert environments are often characterized by areas with complex geological structures affecting seismic imaging in geophysical exploration. A good velocity model building tool is needed to deal with these difficult systems where geophysical inversion is affected by high non uniqueness or variable sensitivity to the targets. We approach this problem by making use of a recently developed automatic and data-driven surface-consistent refraction method. The developed method is focusing on the analysis of phases (pQC) and amplitudes (aQC) of refracted arrivals. We successfully applied the methodology to many 3D land and marine seismic datasets. As a land example, we show the results for a prominent wadi. Another example is related to marine acquisitions characterized by salt and evaporitic sequences composed of evaporitic and clastic sediments.","PeriodicalId":350524,"journal":{"name":"Second EAGE/PESGB Workshop on Velocities","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117041891","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 : 2019-04-04DOI: 10.3997/2214-4609.201900046
J. Tatat, P. Hayes, G. Jones, M. Townsend
Summary The Central North Sea is a mature basin containing a large number of fields, some of which have been in production for decades. Advances in seismic acquisition and data processing over the life of these fields have brought about improvements in seismic image quality and therefore the understanding of the reservoirs. Here we apply some of the latest imaging techniques such as joint tomography using both reflection and refraction pick data and Q Full-Waveform Inversion (Q-FWI) in a challenging geological setting, to help overcome some prevalent subsurface issues. These include the imaging problems introduced by shallow channels and gas, which induce distortion at reservoir depth.
{"title":"Reducing Imaging Depth Distortions in the Central North Sea with High Resolution Velocity Model Building","authors":"J. Tatat, P. Hayes, G. Jones, M. Townsend","doi":"10.3997/2214-4609.201900046","DOIUrl":"https://doi.org/10.3997/2214-4609.201900046","url":null,"abstract":"Summary The Central North Sea is a mature basin containing a large number of fields, some of which have been in production for decades. Advances in seismic acquisition and data processing over the life of these fields have brought about improvements in seismic image quality and therefore the understanding of the reservoirs. Here we apply some of the latest imaging techniques such as joint tomography using both reflection and refraction pick data and Q Full-Waveform Inversion (Q-FWI) in a challenging geological setting, to help overcome some prevalent subsurface issues. These include the imaging problems introduced by shallow channels and gas, which induce distortion at reservoir depth.","PeriodicalId":350524,"journal":{"name":"Second EAGE/PESGB Workshop on Velocities","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121177796","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 : 2019-04-04DOI: 10.3997/2214-4609.201900029
O. Zdraveva, M. Hegazy, Z. Chen, M. O’Briain
Summary Over the last 10 years, full-waveform inversion (FWI) established itself as an integral part of modern Earth model building (EMB) workflows. Recently, the industry witnessed the introduction of many types of FWI, differing either by the portion of the wavefield used in the inversion or by the nature of the objective function. We discuss the importance of different types of FWI in EMB workflows designed to address specific imaging challenges and achieve given interpretation objectives. We demonstrate the effects on model quality and project turn-around time from the complementary use of FWI in complex media, together with common image point Tomography (with or without borehole seismic constraints), salt geometry scenarios and extensive use of geologic constraints.
{"title":"Complementary Use of FWI in Earth Model Building Workflows in Complex Media","authors":"O. Zdraveva, M. Hegazy, Z. Chen, M. O’Briain","doi":"10.3997/2214-4609.201900029","DOIUrl":"https://doi.org/10.3997/2214-4609.201900029","url":null,"abstract":"Summary Over the last 10 years, full-waveform inversion (FWI) established itself as an integral part of modern Earth model building (EMB) workflows. Recently, the industry witnessed the introduction of many types of FWI, differing either by the portion of the wavefield used in the inversion or by the nature of the objective function. We discuss the importance of different types of FWI in EMB workflows designed to address specific imaging challenges and achieve given interpretation objectives. We demonstrate the effects on model quality and project turn-around time from the complementary use of FWI in complex media, together with common image point Tomography (with or without borehole seismic constraints), salt geometry scenarios and extensive use of geologic constraints.","PeriodicalId":350524,"journal":{"name":"Second EAGE/PESGB Workshop on Velocities","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121586449","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 : 2019-04-04DOI: 10.3997/2214-4609.201900052
J. Chaloner, P. Esestime, B. Cox, H. Nicholls, L. Letki
{"title":"Reflection-refraction Tomography and Complex Salt Structure - A Case Study from Offshore North Gabon","authors":"J. Chaloner, P. Esestime, B. Cox, H. Nicholls, L. Letki","doi":"10.3997/2214-4609.201900052","DOIUrl":"https://doi.org/10.3997/2214-4609.201900052","url":null,"abstract":"","PeriodicalId":350524,"journal":{"name":"Second EAGE/PESGB Workshop on Velocities","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121865191","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 : 2019-04-04DOI: 10.3997/2214-4609.201900051
R. Dias, J. Fonseca, A. Bulcão, B. Dias, L. Teixeira, A. Maul, F. Borges
Summary This paper presents the benefits of combining geological velocity modelling with Least-Squares Migration to generate seismic images for the Brazilian presalt reservoirs. The geological velocity modelling focus on the evaporitic salts section characterization, regarding the stratification features presented on this layer, which were incorporated in the velocity model. Results are compared with seismic images created by the velocity model with tomography – without salt stratification – and Reverse Time Migration. In addition, a quantitative comparison is made with a modelled reference horizon to analyse the depth positioning and the migrated amplitude.
{"title":"Salt Stratification and Least Square Migration to Improve Pre-Salt Reservoir Images: Santos Basin, Brazilian Offshore Example","authors":"R. Dias, J. Fonseca, A. Bulcão, B. Dias, L. Teixeira, A. Maul, F. Borges","doi":"10.3997/2214-4609.201900051","DOIUrl":"https://doi.org/10.3997/2214-4609.201900051","url":null,"abstract":"Summary This paper presents the benefits of combining geological velocity modelling with Least-Squares Migration to generate seismic images for the Brazilian presalt reservoirs. The geological velocity modelling focus on the evaporitic salts section characterization, regarding the stratification features presented on this layer, which were incorporated in the velocity model. Results are compared with seismic images created by the velocity model with tomography – without salt stratification – and Reverse Time Migration. In addition, a quantitative comparison is made with a modelled reference horizon to analyse the depth positioning and the migrated amplitude.","PeriodicalId":350524,"journal":{"name":"Second EAGE/PESGB Workshop on Velocities","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126692872","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 : 2019-04-04DOI: 10.3997/2214-4609.201900049
D. Colombo, D. Rovetta
{"title":"Guide to Multi-physics Velocity Model Building: Joint Inversion Algorithms and Workflows for Real Data Applications","authors":"D. Colombo, D. Rovetta","doi":"10.3997/2214-4609.201900049","DOIUrl":"https://doi.org/10.3997/2214-4609.201900049","url":null,"abstract":"","PeriodicalId":350524,"journal":{"name":"Second EAGE/PESGB Workshop on Velocities","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128221728","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 : 2019-04-04DOI: 10.3997/2214-4609.201900053
S. Baldock, T. Kim, T. Feng, Z. Guo, C. Zeng, H. Bondeson, B. Kjølhamar, M. Hart
Summary Sedimentary basins with prospectivity potential beneath volcanic intrusions occur in many parts of the world. However, the rugosity and high-impedance contrast of the basalt create significant challenges in imaging sub-basalt structures. Two-way wave equation techniques may be employed to address the complex multipathing that occurs during propagation of the wavefield through basalt. This is illustrated by the successful application of common offset RTM (COR) tomography and least squares RTM to a 3D data set from northwest Europe. The use of these techniques has improved the imaging and the velocity model within and beneath the basalt.
{"title":"Imaging beneath Basalts in the Norwegian Sea Using RTM Tomography and Least Squares RTM","authors":"S. Baldock, T. Kim, T. Feng, Z. Guo, C. Zeng, H. Bondeson, B. Kjølhamar, M. Hart","doi":"10.3997/2214-4609.201900053","DOIUrl":"https://doi.org/10.3997/2214-4609.201900053","url":null,"abstract":"Summary Sedimentary basins with prospectivity potential beneath volcanic intrusions occur in many parts of the world. However, the rugosity and high-impedance contrast of the basalt create significant challenges in imaging sub-basalt structures. Two-way wave equation techniques may be employed to address the complex multipathing that occurs during propagation of the wavefield through basalt. This is illustrated by the successful application of common offset RTM (COR) tomography and least squares RTM to a 3D data set from northwest Europe. The use of these techniques has improved the imaging and the velocity model within and beneath the basalt.","PeriodicalId":350524,"journal":{"name":"Second EAGE/PESGB Workshop on Velocities","volume":"196 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122354768","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 : 2019-04-04DOI: 10.3997/2214-4609.201900039
O. Øye, E. Dahl
Summary We present a machine learning setup that can estimate a velocity model from raw seismic shot gathers without the need for an initial velocity model. Our setup is based on a convolutional neural network (CNN) trained on pairs of random generated synthetic velocity models and corresponding forward modelled synthetic shot gathers. The network is trained to predict the correct velocity model for a given input shot gather. We evaluate the performance of the trained network on both synthetic and real seismic data, and observe that the system is able to estimate background velocity trends directly from the raw shot gathers without need for preprocessing or preconditioning. Once trained, the network is very fast to run, and can deliver a velocity model in seconds running on a single GPU. The preciscion and resolution of the estimated velocity models is not on par with state of the art velocity model building techniques such as FWI and/or reflection tomography, but shows that machine learning can robustly extract meaningful velocity information from raw shot gathers, and that there might be potential in using such methods for velocity model building.
{"title":"Velocity model building from raw shot gathers using machine learning","authors":"O. Øye, E. Dahl","doi":"10.3997/2214-4609.201900039","DOIUrl":"https://doi.org/10.3997/2214-4609.201900039","url":null,"abstract":"Summary We present a machine learning setup that can estimate a velocity model from raw seismic shot gathers without the need for an initial velocity model. Our setup is based on a convolutional neural network (CNN) trained on pairs of random generated synthetic velocity models and corresponding forward modelled synthetic shot gathers. The network is trained to predict the correct velocity model for a given input shot gather. We evaluate the performance of the trained network on both synthetic and real seismic data, and observe that the system is able to estimate background velocity trends directly from the raw shot gathers without need for preprocessing or preconditioning. Once trained, the network is very fast to run, and can deliver a velocity model in seconds running on a single GPU. The preciscion and resolution of the estimated velocity models is not on par with state of the art velocity model building techniques such as FWI and/or reflection tomography, but shows that machine learning can robustly extract meaningful velocity information from raw shot gathers, and that there might be potential in using such methods for velocity model building.","PeriodicalId":350524,"journal":{"name":"Second EAGE/PESGB Workshop on Velocities","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129176920","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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