Pub Date : 2023-07-01DOI: 10.3997/2214-4609.202112410
H. P. Huu
{"title":"A New Workflow to Enhance Intercept and Gradient Data Quality","authors":"H. P. Huu","doi":"10.3997/2214-4609.202112410","DOIUrl":"https://doi.org/10.3997/2214-4609.202112410","url":null,"abstract":"","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124746686","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 : 2021-10-20DOI: 10.3997/2214-4609.202113053
G. Rodríguez-Pradilla, J. Verdon
{"title":"Seismic Monitoring of the United Downs Deep Geothermal Power Project (UDDGP) Site with Public Seismic Networks","authors":"G. Rodríguez-Pradilla, J. Verdon","doi":"10.3997/2214-4609.202113053","DOIUrl":"https://doi.org/10.3997/2214-4609.202113053","url":null,"abstract":"","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124679046","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: 10.3997/2214-4609.202113170
C. Walter, A. Braun, G. Fotopoulos
Summary Within this study, a vertical buzz test methodology is applied to characterize the distance at which the electromagnetic interference generated by a UAV platform attenuates below the sensitivity threshold of a high-resolution magnetometer in a controlled setting. A DJI Wind 4 heavy-lift, multi-rotor UAV platform was used to characterize the spatial extent of the electromagnetic interference generated inflight. The vertical setback distance of a UAV-borne aeromagnetic system was characterized using a vertical buzz test maneuver in a magnetically quiet area. Through conducting the characterization test, it was determined that the DJI Wind 4 with a 2.2 kg payload required a vertical setback distance of approximately 5 m when surveying with a magnetometer employing a sensitivity of 0.01 nT. Furthermore, it was determined that a magnetometers vertical setback distance is unique for each specific combination of UAV platform and magnetometer employed within a UAV-borne aeromagnetic system. Based on previous tests, using the same magnetometer and methodology, the vertical setback distance was determined to be 3 m, for both a DJI - S900 and M600. Therefore, the assessment shown herein should be conducted to characterize the vertical setback distance for specific UAV magnetometry systems (each platform and magnetometer) prior to conducting surveys.
{"title":"A Procedure for Quantifying a UAV-borne Magnetometer Vertical Setback Distance","authors":"C. Walter, A. Braun, G. Fotopoulos","doi":"10.3997/2214-4609.202113170","DOIUrl":"https://doi.org/10.3997/2214-4609.202113170","url":null,"abstract":"Summary Within this study, a vertical buzz test methodology is applied to characterize the distance at which the electromagnetic interference generated by a UAV platform attenuates below the sensitivity threshold of a high-resolution magnetometer in a controlled setting. A DJI Wind 4 heavy-lift, multi-rotor UAV platform was used to characterize the spatial extent of the electromagnetic interference generated inflight. The vertical setback distance of a UAV-borne aeromagnetic system was characterized using a vertical buzz test maneuver in a magnetically quiet area. Through conducting the characterization test, it was determined that the DJI Wind 4 with a 2.2 kg payload required a vertical setback distance of approximately 5 m when surveying with a magnetometer employing a sensitivity of 0.01 nT. Furthermore, it was determined that a magnetometers vertical setback distance is unique for each specific combination of UAV platform and magnetometer employed within a UAV-borne aeromagnetic system. Based on previous tests, using the same magnetometer and methodology, the vertical setback distance was determined to be 3 m, for both a DJI - S900 and M600. Therefore, the assessment shown herein should be conducted to characterize the vertical setback distance for specific UAV magnetometry systems (each platform and magnetometer) prior to conducting surveys.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"11 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":"115453825","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: 10.3997/2214-4609.202113268
A. Singh, D. Vashisth, S. Srivastava
Summary Joint inversion of multiple geophysical datasets has its own set of advantages for interpreting the geology of an area. Using neural networks (NN), we propose the joint inversion of MT and DC apparent resistivity datasets to delineate the subsurface conductivity distribution. The NN model used is inspired by the Siamese networks to provide different prediction channels for the two different datasets before integrating them to get the layered earth parameters. The NN model trained on the specified range of model parameters has predicted each layers’ resistivity and thickness close to the true values for all the four types of resistivity distribution (A, Q, H, and K) for a three-layered earth model and takes advantage of the two different datasets to see through the equivalence problem to detect the thin second layer for an H type curve. The NN model accurately estimated the resistivity distribution even when the true data was corrupted with 10% Gaussian noise. Not only the method proposed provides good results for all the models considered but also saves time over other optimisation techniques where every model requires separate simulation. The method, therefore, proves to be a fast, efficient and reliable way for joint inversion of geophysical datasets.
{"title":"A deep learning approach for joint inversion of DC Resistivity and MT data","authors":"A. Singh, D. Vashisth, S. Srivastava","doi":"10.3997/2214-4609.202113268","DOIUrl":"https://doi.org/10.3997/2214-4609.202113268","url":null,"abstract":"Summary Joint inversion of multiple geophysical datasets has its own set of advantages for interpreting the geology of an area. Using neural networks (NN), we propose the joint inversion of MT and DC apparent resistivity datasets to delineate the subsurface conductivity distribution. The NN model used is inspired by the Siamese networks to provide different prediction channels for the two different datasets before integrating them to get the layered earth parameters. The NN model trained on the specified range of model parameters has predicted each layers’ resistivity and thickness close to the true values for all the four types of resistivity distribution (A, Q, H, and K) for a three-layered earth model and takes advantage of the two different datasets to see through the equivalence problem to detect the thin second layer for an H type curve. The NN model accurately estimated the resistivity distribution even when the true data was corrupted with 10% Gaussian noise. Not only the method proposed provides good results for all the models considered but also saves time over other optimisation techniques where every model requires separate simulation. The method, therefore, proves to be a fast, efficient and reliable way for joint inversion of geophysical datasets.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"30 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":"115598266","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: 10.3997/2214-4609.202113190
F. Junting, Z. Feng, Z. Hui, F. Jilin
Summary The sensitivity of D-T neutron porosity logging to formation porosity change is lower than that of Am-Be neutron porosity logging.In order to improve the sensitivity of porosity measurement, based on a measurement system consisting of four detectors and D-T neutron source, The Monte Carlo simulation method was used to establish the numerical calculation model , the effects of density and hydrogen index on the thermal neutron count ratio and inelastic gamma count ratio are studied . A new method to correct the influence of density in porosity measurement by inelastic gamma count ratio is proposed. The data in the model well was processed using this method, and the processed results showed better accuracy and sensitivity to porosity than the unprocessed values.
{"title":"A new determining porosity method based on four detectors and D-T neutron source","authors":"F. Junting, Z. Feng, Z. Hui, F. Jilin","doi":"10.3997/2214-4609.202113190","DOIUrl":"https://doi.org/10.3997/2214-4609.202113190","url":null,"abstract":"Summary The sensitivity of D-T neutron porosity logging to formation porosity change is lower than that of Am-Be neutron porosity logging.In order to improve the sensitivity of porosity measurement, based on a measurement system consisting of four detectors and D-T neutron source, The Monte Carlo simulation method was used to establish the numerical calculation model , the effects of density and hydrogen index on the thermal neutron count ratio and inelastic gamma count ratio are studied . A new method to correct the influence of density in porosity measurement by inelastic gamma count ratio is proposed. The data in the model well was processed using this method, and the processed results showed better accuracy and sensitivity to porosity than the unprocessed values.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"617 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":"123069999","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: 10.3997/2214-4609.202112985
O. Alzankawi, M. Fitouri, P. Rebaud
Summary This paper describes a technology used in detecting a formation collapse on a casing in a deviated well in the Great Burgan Field in Kuwait. The Greater Burgan field is located in the south eastern part of Kuwait, the field contain several reservoirs in the Cretaceous and Jurassic formations. The intermediate section that is of interest in this paper is drilled across the Cretaceous and Tertiary shallow formations. This section is typically drilled using 16 in. bit and cased with 13 3/8 in. casing; the landing point of the section is typically planned at the top of Ahmadi shale formation. Since most of this interval is carbonate, the mud used to drill the section is water-based mud ranging from 8.8 to 9.1 ppg; the mud window is kept small to prevent losses across the loss prone highly fractured carbonate (Tayarat & Damam) formations.
{"title":"Advanced Ultrasonic technology measuring annulus thickness and detecting formation collapse on casing","authors":"O. Alzankawi, M. Fitouri, P. Rebaud","doi":"10.3997/2214-4609.202112985","DOIUrl":"https://doi.org/10.3997/2214-4609.202112985","url":null,"abstract":"Summary This paper describes a technology used in detecting a formation collapse on a casing in a deviated well in the Great Burgan Field in Kuwait. The Greater Burgan field is located in the south eastern part of Kuwait, the field contain several reservoirs in the Cretaceous and Jurassic formations. The intermediate section that is of interest in this paper is drilled across the Cretaceous and Tertiary shallow formations. This section is typically drilled using 16 in. bit and cased with 13 3/8 in. casing; the landing point of the section is typically planned at the top of Ahmadi shale formation. Since most of this interval is carbonate, the mud used to drill the section is water-based mud ranging from 8.8 to 9.1 ppg; the mud window is kept small to prevent losses across the loss prone highly fractured carbonate (Tayarat & Damam) formations.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"62 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":"116713974","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: 10.3997/2214-4609.202113239
G. Zhang, Y. Wang, C. Liu, B. She, B. Zou
Summary The traditional deconvolution methods have some disadvantages, such as suppressing weak reflection coefficients and are difficult to identify thin interbedding and so on. In order to overcome these shortcomings, this paper presents a new approach to improve the resolution of seismic data, based upon joint dictionary learning and sparse representation (JDLSR). The characteristics of reflection coefficients can be obtained by dictionary learning. In order to explore the correspondence between seismic data and reflection coefficients more efficiently, we introduce the joint dictionary learning. The combined features (DR and DS) of log reflection coefficients and seismic data of well beside can be learned by joint dictionary learning. The known seismic data are sparsely represented under DS to obtain the representation coefficient, which can be combined with DR to reconstruct the unknown reflection coefficients. The effectiveness of the proposed method is verified by the single-channel seismic data and the classical Marmousi model. This method is applied to high-resolution processing of actual seismic data, and it is found that the result is better than sparse-spike deconvolution (SSD).
{"title":"A high resolution method of seismic data via joint dictionary learning and sparse representation","authors":"G. Zhang, Y. Wang, C. Liu, B. She, B. Zou","doi":"10.3997/2214-4609.202113239","DOIUrl":"https://doi.org/10.3997/2214-4609.202113239","url":null,"abstract":"Summary The traditional deconvolution methods have some disadvantages, such as suppressing weak reflection coefficients and are difficult to identify thin interbedding and so on. In order to overcome these shortcomings, this paper presents a new approach to improve the resolution of seismic data, based upon joint dictionary learning and sparse representation (JDLSR). The characteristics of reflection coefficients can be obtained by dictionary learning. In order to explore the correspondence between seismic data and reflection coefficients more efficiently, we introduce the joint dictionary learning. The combined features (DR and DS) of log reflection coefficients and seismic data of well beside can be learned by joint dictionary learning. The known seismic data are sparsely represented under DS to obtain the representation coefficient, which can be combined with DR to reconstruct the unknown reflection coefficients. The effectiveness of the proposed method is verified by the single-channel seismic data and the classical Marmousi model. This method is applied to high-resolution processing of actual seismic data, and it is found that the result is better than sparse-spike deconvolution (SSD).","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"11 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":"124912206","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: 10.3997/2214-4609.202113321
B. Steffens, V. Demyanov, D. Arnold, H. Lewis
Summary In this work we introduce a novel reservoir modelling workflow where modelling is assisted by an entropy-driven particle swarm optimizer. Producing a representative range of reservoir models that cover geological uncertainties in an effective way is a challenging task. We therefore make use of entropy to ensure that the ensemble of generated models adequately reflects the available information and provides diversity that reflects the associated variability in fluid flow behavior. The workflow is tested on a synthetic case study of a fractured reservoir. The results indicate that the entropy-driven PSO is able to prevent the diversity of the ensemble of models from collapsing whilst staying within the bounds of a predefined expected dynamic flow response. It is also shown that the entropy-driven PSO outperforms a standard PSO in this task. Secondary outcomes from the workflow, such as a spatial entropy map, provide a great tool for further uncertainty assessment and can be used to identify swept or unswept reservoir regions and the regions where more information is needed to reduce the uncertainty.
{"title":"Entropy-driven particle swarm optimization for reservoir modelling under geological uncertainty – application to a fractured reservoir","authors":"B. Steffens, V. Demyanov, D. Arnold, H. Lewis","doi":"10.3997/2214-4609.202113321","DOIUrl":"https://doi.org/10.3997/2214-4609.202113321","url":null,"abstract":"Summary In this work we introduce a novel reservoir modelling workflow where modelling is assisted by an entropy-driven particle swarm optimizer. Producing a representative range of reservoir models that cover geological uncertainties in an effective way is a challenging task. We therefore make use of entropy to ensure that the ensemble of generated models adequately reflects the available information and provides diversity that reflects the associated variability in fluid flow behavior. The workflow is tested on a synthetic case study of a fractured reservoir. The results indicate that the entropy-driven PSO is able to prevent the diversity of the ensemble of models from collapsing whilst staying within the bounds of a predefined expected dynamic flow response. It is also shown that the entropy-driven PSO outperforms a standard PSO in this task. Secondary outcomes from the workflow, such as a spatial entropy map, provide a great tool for further uncertainty assessment and can be used to identify swept or unswept reservoir regions and the regions where more information is needed to reduce the uncertainty.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"55 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":"126619199","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: 10.3997/2214-4609.202113245
L. Torgersen, D. Marín
Summary The Tertiary succession is a promising reservoir in the western margin of the Barents Sea. Previous studies have mapped injectites and submarine fans in a single 3D seismic dataset in the Sorvestnaget Basin. Wells have confirmed the reservoir potential, but the lateral variation of these reservoir rocks and the role of active tectonics in their distribution is not yet properly understood. Even though both the tectonics and the sedimentation are studied, they are only studied separately and there are no references displaying the role of tectonics on the sedimentation for the Tertiary succession in this area. The technostratigraphical evolution during Tertiary in the study area can be related to the regional geological mechanism such as, rifting, uplift, subsidence, and glaciation. All of these which have an important factor for controlling the deposition and remobilization of sediments in the subsurface. Salt halokinesis have together with the regional tectonic events also affected the Tertiary succession to a large degree as a major halokinetic event occurred during Eocene, reworking the sediments by truncation, erosion, and the formation of local deep basins.
{"title":"Tertiary Technostratigraphic Evolution of the Veslemøy High and Sørvestnaget Basin, Western Barents Sea","authors":"L. Torgersen, D. Marín","doi":"10.3997/2214-4609.202113245","DOIUrl":"https://doi.org/10.3997/2214-4609.202113245","url":null,"abstract":"Summary The Tertiary succession is a promising reservoir in the western margin of the Barents Sea. Previous studies have mapped injectites and submarine fans in a single 3D seismic dataset in the Sorvestnaget Basin. Wells have confirmed the reservoir potential, but the lateral variation of these reservoir rocks and the role of active tectonics in their distribution is not yet properly understood. Even though both the tectonics and the sedimentation are studied, they are only studied separately and there are no references displaying the role of tectonics on the sedimentation for the Tertiary succession in this area. The technostratigraphical evolution during Tertiary in the study area can be related to the regional geological mechanism such as, rifting, uplift, subsidence, and glaciation. All of these which have an important factor for controlling the deposition and remobilization of sediments in the subsurface. Salt halokinesis have together with the regional tectonic events also affected the Tertiary succession to a large degree as a major halokinetic event occurred during Eocene, reworking the sediments by truncation, erosion, and the formation of local deep basins.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"226 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":"122689144","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: 10.3997/2214-4609.202010793
J. Zhao, S. Peng, X. Cui
Summary To efficiently separate weak diffractions from the GPR data, which usually has a single coverage and is easily contaminated with noise, we formulate a GPR diffraction separation method by incorporating the plane-wave destruction method and online dictionary learning technique. To promote the focusing ability of diffractions, a reweighted L2-norm and L1-norm minimization model is also introduced for accomplishing high-resolution GPR images, which has potential in focusing diffractions and reducing migration noise. The results obtained in our provided field example illustrates its good performance in separating and imaging of GPR diffractions and its potential value in illuminating fractures and the broken conditions.
{"title":"Separating and Sparse Imaging of GPR Diffractions by Dictionary Learning and Least-Squares Migration","authors":"J. Zhao, S. Peng, X. Cui","doi":"10.3997/2214-4609.202010793","DOIUrl":"https://doi.org/10.3997/2214-4609.202010793","url":null,"abstract":"Summary To efficiently separate weak diffractions from the GPR data, which usually has a single coverage and is easily contaminated with noise, we formulate a GPR diffraction separation method by incorporating the plane-wave destruction method and online dictionary learning technique. To promote the focusing ability of diffractions, a reweighted L2-norm and L1-norm minimization model is also introduced for accomplishing high-resolution GPR images, which has potential in focusing diffractions and reducing migration noise. The results obtained in our provided field example illustrates its good performance in separating and imaging of GPR diffractions and its potential value in illuminating fractures and the broken conditions.","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":"122072820","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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