Pub Date : 2019-06-03DOI: 10.3997/2214-4609.201901181
P. Hayes, 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 processing and imaging techniques in a challenging geological setting to help overcome some prevalent subsurface issues and identify opportunities to add significant potential reserves. These include improving the bandwidth of the data, the multiple attenuation and addressing the imaging problems introduced by shallow channels and gas. The processing sequence was established via an evolution style workflow, whereby fully imaged seismic volumes were created at stages during the life of the project. These products provide the opportunity for end user feedback, based upon detailed, reservoir focussed QC.
{"title":"Implementing New Technology to Revitalize Central North Sea Seismic Via Evolutionary Processing","authors":"P. Hayes, M. Townsend","doi":"10.3997/2214-4609.201901181","DOIUrl":"https://doi.org/10.3997/2214-4609.201901181","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 processing and imaging techniques in a challenging geological setting to help overcome some prevalent subsurface issues and identify opportunities to add significant potential reserves. These include improving the bandwidth of the data, the multiple attenuation and addressing the imaging problems introduced by shallow channels and gas. The processing sequence was established via an evolution style workflow, whereby fully imaged seismic volumes were created at stages during the life of the project. These products provide the opportunity for end user feedback, based upon detailed, reservoir focussed QC.","PeriodicalId":6840,"journal":{"name":"81st EAGE Conference and Exhibition 2019","volume":"233 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77471180","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 : 2019-06-03DOI: 10.3997/2214-4609.201900991
R. Cova, K. Innanen, M. Rauch-Davies
{"title":"Pre-Processing a Land Walkaway VSP Dataset for Elastic FWI: Effects of Deconvolution Operations","authors":"R. Cova, K. Innanen, M. Rauch-Davies","doi":"10.3997/2214-4609.201900991","DOIUrl":"https://doi.org/10.3997/2214-4609.201900991","url":null,"abstract":"","PeriodicalId":6840,"journal":{"name":"81st EAGE Conference and Exhibition 2019","volume":"246 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77590146","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 : 2019-06-03DOI: 10.3997/2214-4609.201901624
A. Guadagnini, A. Russian, M. Riva, E. Russo, M. Chiaramonte
Summary This study provides rigorous quantification of uncertainties associated with fracture permeability estimation obtained through stochastic inverse modeling of mud losses recorded while drilling. Fracture characterization is performed in terms of fracture width estimation and is grounded on a stochastic inverse modeling technique. Implementation of the latter rests on a well-defined set of parameters, including drilling fluid, rheological properties, flow rates, pore and dynamic drilling fluid pressure, wellbore geometry. These quantities are generally affected by diverse sources of uncertainty. Drilling mud is modeled as a Herschel–Bulkley fluid. Open fractures are treated as horizontal planes intersecting the wellbore and a simple analytical solution is employed to express mud flow advancement in the fracture as a function of drilling fluid properties and operational conditions. A modern global sensitivity analysis approach is employed to quantify the way uncertain model parameters affect fracture aperture (hence permeability) and extent. Uncertainty propagation from input parameters to model outputs is investigated and quantified through a workflow implemented within a Monte Carlo framework. It is then employed in the context of stochastic inverse modeling of field cases to evaluate posterior probability densities of fracture aperture and to simulate drilling fluid invasion in fractures in quasi-real time during drilling.
{"title":"Quantification of Uncertainties of Fracture Permeability Via Mud Loss Information and Inverse Stochastic Modeling","authors":"A. Guadagnini, A. Russian, M. Riva, E. Russo, M. Chiaramonte","doi":"10.3997/2214-4609.201901624","DOIUrl":"https://doi.org/10.3997/2214-4609.201901624","url":null,"abstract":"Summary This study provides rigorous quantification of uncertainties associated with fracture permeability estimation obtained through stochastic inverse modeling of mud losses recorded while drilling. Fracture characterization is performed in terms of fracture width estimation and is grounded on a stochastic inverse modeling technique. Implementation of the latter rests on a well-defined set of parameters, including drilling fluid, rheological properties, flow rates, pore and dynamic drilling fluid pressure, wellbore geometry. These quantities are generally affected by diverse sources of uncertainty. Drilling mud is modeled as a Herschel–Bulkley fluid. Open fractures are treated as horizontal planes intersecting the wellbore and a simple analytical solution is employed to express mud flow advancement in the fracture as a function of drilling fluid properties and operational conditions. A modern global sensitivity analysis approach is employed to quantify the way uncertain model parameters affect fracture aperture (hence permeability) and extent. Uncertainty propagation from input parameters to model outputs is investigated and quantified through a workflow implemented within a Monte Carlo framework. It is then employed in the context of stochastic inverse modeling of field cases to evaluate posterior probability densities of fracture aperture and to simulate drilling fluid invasion in fractures in quasi-real time during drilling.","PeriodicalId":6840,"journal":{"name":"81st EAGE Conference and Exhibition 2019","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79769652","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 : 2019-06-03DOI: 10.3997/2214-4609.201901182
P. Tillotson, D. Davies, M. Ball, L. Smith
Summary In 2017 BP acquired its densest ever full field marine ocean bottom node (OBN) survey over the Clair field. With a source sampling of 25m × 25m and receivers spaced at 50m × 100m the ultra-high density OBN (UHDOBN) sampling was an order of magnitude higher than the previous 2010 Clair Ridge HDOBC. The primary goal for the survey was a 4D baseline for the Clair Ridge area of the field however there were several 3D static imaging aspirations that the data also hoped to address. These included understanding the resolution limit of the data through interpolation, improved 3D imaging of key reservoir intervals on PP and PS data and to utilise the data density and rich azimuth distribution for robust fracture characterisation via azimuthal velocity analysis. The velocity model was rebuilt from scratch and then updated successfully using FWI using the legacy HDOBC data ahead of the survey starting. The final processed UHDOBN PP and PS images were completed within 12 months of the field data being delivered to the processing contractor and provided a step change improvement in imaging and attribute quality.
{"title":"Clair Ridge: Learnings From Processing the Densest OBN Survey in the UKCS","authors":"P. Tillotson, D. Davies, M. Ball, L. Smith","doi":"10.3997/2214-4609.201901182","DOIUrl":"https://doi.org/10.3997/2214-4609.201901182","url":null,"abstract":"Summary In 2017 BP acquired its densest ever full field marine ocean bottom node (OBN) survey over the Clair field. With a source sampling of 25m × 25m and receivers spaced at 50m × 100m the ultra-high density OBN (UHDOBN) sampling was an order of magnitude higher than the previous 2010 Clair Ridge HDOBC. The primary goal for the survey was a 4D baseline for the Clair Ridge area of the field however there were several 3D static imaging aspirations that the data also hoped to address. These included understanding the resolution limit of the data through interpolation, improved 3D imaging of key reservoir intervals on PP and PS data and to utilise the data density and rich azimuth distribution for robust fracture characterisation via azimuthal velocity analysis. The velocity model was rebuilt from scratch and then updated successfully using FWI using the legacy HDOBC data ahead of the survey starting. The final processed UHDOBN PP and PS images were completed within 12 months of the field data being delivered to the processing contractor and provided a step change improvement in imaging and attribute quality.","PeriodicalId":6840,"journal":{"name":"81st EAGE Conference and Exhibition 2019","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81623608","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 : 2019-06-03DOI: 10.3997/2214-4609.201900691
M. Cyz, L. Azevedo, M. Malinowski
Summary In this study we present an application of geostatistical AVA seismic inversion method for characterization of a unconventional Lower Paleozoic shale reservoir in Northern Poland. The target formations are of a small thickness (up tp 25 meters) and deeply buried (ca. 3 km) what makes their delineation and characterization especially difficult. An application of the iterative geostatistical AVA inversion method allowed for obtaining the high-resolution density, P-wave and S-wave velocity models together with the assessment of the uncertainty on the predictions. The obtained elastic property models were compared with the results of the deterministic simultaneous Amplitude-versus-Offset inversion proving that the application of a such sophisticated (geostatistical) inversion technique is a must while dealing with the thin and highly variable layers. The inverted elastic models where further used to improve the prediction of a spatial distribution of the brittleness index with a machine learning (PSVM) algorithm by integrating well-log data and seismic rock property volumes.
{"title":"Application of Geostatistical Seismic AVA Inversion for Shale Reservoir Characterization and Brittleness Prediction with Machine Learning","authors":"M. Cyz, L. Azevedo, M. Malinowski","doi":"10.3997/2214-4609.201900691","DOIUrl":"https://doi.org/10.3997/2214-4609.201900691","url":null,"abstract":"Summary In this study we present an application of geostatistical AVA seismic inversion method for characterization of a unconventional Lower Paleozoic shale reservoir in Northern Poland. The target formations are of a small thickness (up tp 25 meters) and deeply buried (ca. 3 km) what makes their delineation and characterization especially difficult. An application of the iterative geostatistical AVA inversion method allowed for obtaining the high-resolution density, P-wave and S-wave velocity models together with the assessment of the uncertainty on the predictions. The obtained elastic property models were compared with the results of the deterministic simultaneous Amplitude-versus-Offset inversion proving that the application of a such sophisticated (geostatistical) inversion technique is a must while dealing with the thin and highly variable layers. The inverted elastic models where further used to improve the prediction of a spatial distribution of the brittleness index with a machine learning (PSVM) algorithm by integrating well-log data and seismic rock property volumes.","PeriodicalId":6840,"journal":{"name":"81st EAGE Conference and Exhibition 2019","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81733321","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 : 2019-06-03DOI: 10.3997/2214-4609.201901641
J. Liu, Y. Fan, X. Ge, D. Xing, T. Qiu
Summary We conducted the T2 and T1–T2 low field NMR experiment to detect the formation and dissociation process of the methane gas hydrate in sedimentary rocks and artificial cores. Based on our investigations. (1)The gas hydrate forms preferentially in large pores and it is easier for the gas hydrate to reach the equilibrium state than for samples with small pore diameter and grain size. (2)The gas hydrate volume is positively correlated with the porosity, but there is no obvious relationship between the gas hydrate saturation and the porosity. (3)The spectrum distributions move towards the fast relaxation domain with the growth of gas hydrate, because the generated gas hydrate occupies the large pore and accelerate the relaxation rate. (4) It is easier for the gas hydrate in the sample of high porosity and large pore size to dissociate than that in the sample of low porosity and small pore size. (5) T1–T2 spectrum gives new perspective of the porous media, which helps us to discriminate complex components that cannot be interpreted only by the T2 spectrum.
{"title":"Experimental Research on the Gas Hydrate Based on T2 and T1–T2 Low Field NMR Technique","authors":"J. Liu, Y. Fan, X. Ge, D. Xing, T. Qiu","doi":"10.3997/2214-4609.201901641","DOIUrl":"https://doi.org/10.3997/2214-4609.201901641","url":null,"abstract":"Summary We conducted the T2 and T1–T2 low field NMR experiment to detect the formation and dissociation process of the methane gas hydrate in sedimentary rocks and artificial cores. Based on our investigations. (1)The gas hydrate forms preferentially in large pores and it is easier for the gas hydrate to reach the equilibrium state than for samples with small pore diameter and grain size. (2)The gas hydrate volume is positively correlated with the porosity, but there is no obvious relationship between the gas hydrate saturation and the porosity. (3)The spectrum distributions move towards the fast relaxation domain with the growth of gas hydrate, because the generated gas hydrate occupies the large pore and accelerate the relaxation rate. (4) It is easier for the gas hydrate in the sample of high porosity and large pore size to dissociate than that in the sample of low porosity and small pore size. (5) T1–T2 spectrum gives new perspective of the porous media, which helps us to discriminate complex components that cannot be interpreted only by the T2 spectrum.","PeriodicalId":6840,"journal":{"name":"81st EAGE Conference and Exhibition 2019","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84384974","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 : 2019-06-03DOI: 10.3997/2214-4609.201901353
S. Wang, Y. Li, H. Lin
{"title":"Desert Seismic Noise Attenuation Based on Bayesian Mathematical Morphology Filtering","authors":"S. Wang, Y. Li, H. Lin","doi":"10.3997/2214-4609.201901353","DOIUrl":"https://doi.org/10.3997/2214-4609.201901353","url":null,"abstract":"","PeriodicalId":6840,"journal":{"name":"81st EAGE Conference and Exhibition 2019","volume":"C-35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84455146","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 : 2019-06-03DOI: 10.3997/2214-4609.201900804
A. Bakk, R. Holt, A. Bauer, B. Dupuy, M. Romdhane
Depletion or injection into a reservoir implies stress and strain changes in the reservoir and its surroundings. This may lead to measurable time-shifts for seismic waves propagating in the subsurface. We have measured multi-directional ultrasonic P-wave velocity changes for three different field shale cores, each probed with four different stress paths (i.e. different ratios between the horizontal and the vertical stress change), to systematically quantify the time-shifts for overburden shales with respect to ray angle (offset). The laboratory data show that for a given offset, the time-shifts are stress path dependent, where the isotropic stress path is associated with larger time-shifts as compared to the constant mean stress path or the triaxial stress path. Generally, the time-shifts are largest for zero offset (propagation normal to the bedding) and are decreasing for increasing offsets. The constant mean stress path has the most significant decrease of time-shifts with offset. By utilizing pre-stack seismic offset data, such controlled laboratory experiments can be used to constrain the inversion of 4D seismic data to quantify the stress and strain changes due to production. This may have important implications for improved recovery and safety, particularly in mature fields.
{"title":"Offset-Dependent Overburden Time-Shifts from Ultrasonic Data","authors":"A. Bakk, R. Holt, A. Bauer, B. Dupuy, M. Romdhane","doi":"10.3997/2214-4609.201900804","DOIUrl":"https://doi.org/10.3997/2214-4609.201900804","url":null,"abstract":"Depletion or injection into a reservoir implies stress and strain changes in the reservoir and its surroundings. This may lead to measurable time-shifts for seismic waves propagating in the subsurface. We have measured multi-directional ultrasonic P-wave velocity changes for three different field shale cores, each probed with four different stress paths (i.e. different ratios between the horizontal and the vertical stress change), to systematically quantify the time-shifts for overburden shales with respect to ray angle (offset). The laboratory data show that for a given offset, the time-shifts are stress path dependent, where the isotropic stress path is associated with larger time-shifts as compared to the constant mean stress path or the triaxial stress path. Generally, the time-shifts are largest for zero offset (propagation normal to the bedding) and are decreasing for increasing offsets. The constant mean stress path has the most significant decrease of time-shifts with offset. By utilizing pre-stack seismic offset data, such controlled laboratory experiments can be used to constrain the inversion of 4D seismic data to quantify the stress and strain changes due to production. This may have important implications for improved recovery and safety, particularly in mature fields.","PeriodicalId":6840,"journal":{"name":"81st EAGE Conference and Exhibition 2019","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84895015","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 : 2019-06-03DOI: 10.3997/2214-4609.201901052
S. Nakayama, G. Blacquière, T. Ishiyama
The application of blended acquisition along with irregular acquisition geometries contributes to the economic perspective of a seismic survey. The joint migration inversion scheme is capable of directly processing the data acquired in this way, i.e., without deblending or data reconstruction, and of subsequently estimating both reflectively and velocity models. The workflow proposed in this study aims to design the source blending operator as well as detector and source sampling operators. The approach iteratively computes these parameters in such a way that the quality of reflectivity and velocity models, which are directly estimated from blended and irregularly-sampled data, is adequate. The workflow integrates a genetic algorithm and a convolutional neural network to derive optimum parameters. Bio-inspired operators enable the simultaneous update of the blending and sampling operators. To relate the choice of survey parameters to the performance of a joint migration inversion, we utilize a convolutional neural network. The applied network architecture discards suboptimal solutions among newly generated ones. Conversely, it passes optimal ones to the subsequent step, which successfully enhances the efficiency of the proposed approach. The resultant acquisition scenario yields a notable enhancement in both reflectivity and velocity estimates attributed solely to the choice of survey parameters.
{"title":"Survey Design Towards Optimum Reflectivity and Velocity Estimates Directly from Blended and Irregularly-Sampled Data","authors":"S. Nakayama, G. Blacquière, T. Ishiyama","doi":"10.3997/2214-4609.201901052","DOIUrl":"https://doi.org/10.3997/2214-4609.201901052","url":null,"abstract":"The application of blended acquisition along with irregular acquisition geometries contributes to the economic perspective of a seismic survey. The joint migration inversion scheme is capable of directly processing the data acquired in this way, i.e., without deblending or data reconstruction, and of subsequently estimating both reflectively and velocity models. The workflow proposed in this study aims to design the source blending operator as well as detector and source sampling operators. The approach iteratively computes these parameters in such a way that the quality of reflectivity and velocity models, which are directly estimated from blended and irregularly-sampled data, is adequate. The workflow integrates a genetic algorithm and a convolutional neural network to derive optimum parameters. Bio-inspired operators enable the simultaneous update of the blending and sampling operators. To relate the choice of survey parameters to the performance of a joint migration inversion, we utilize a convolutional neural network. The applied network architecture discards suboptimal solutions among newly generated ones. Conversely, it passes optimal ones to the subsequent step, which successfully enhances the efficiency of the proposed approach. The resultant acquisition scenario yields a notable enhancement in both reflectivity and velocity estimates attributed solely to the choice of survey parameters.","PeriodicalId":6840,"journal":{"name":"81st EAGE Conference and Exhibition 2019","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84935451","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 : 2019-06-03DOI: 10.3997/2214-4609.201900757
W. Choi, C. Kim, D. Cheon, S. Pyun
{"title":"Automatic Classification of Microseismic Signals Related to Mining Activities by Supervised Learning","authors":"W. Choi, C. Kim, D. Cheon, S. Pyun","doi":"10.3997/2214-4609.201900757","DOIUrl":"https://doi.org/10.3997/2214-4609.201900757","url":null,"abstract":"","PeriodicalId":6840,"journal":{"name":"81st EAGE Conference and Exhibition 2019","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85119096","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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