Pub Date : 2021-10-18DOI: 10.3997/2214-4609.202113187
A. R. Dalkhani, X. Zhang, C. Weemstra
Summary Seismic surface wave tomography is an effective tool for 3D crustal imaging. Conventionally, a two-step inversion algorithm is used to recover a three-dimensional model of surface wave velocity. That is, starting from surface wave dispersion data (frequency-dependent phase velocities), an initial inversion resulting in a series of (2D) maps of frequency-dependent surface-wave velocity is followed by a separate (1D) depth inversion. A single-step 3D non-linear algorithm has recently been proposed in a Bayesian framework. The algorithm involves a reversible jump Markov chain Monte Carlo approach and is referred to transdimensional tomography. Here, we investigate the feasibility of this transdimensional algorithm for the purpose of recovering the 3D surface wave velocity structure below the Reykjanes Peninsula, southwest Iceland. In particular, we investigate this for the specific receiver configuration for which we have obtained year-long recordings of ambient seismic noise. To that end, we designed a number of synthetic tests using receiver-receiver travel times associated with that station configuration. We find that the transdimensional algorithm successfully recovers the 3D velocity structure of the area. In particular, the algorithm successfully adapts its resolution to the density of rays and the level of data noise. Moreover, quantified solution uncertainty makes the result better interpretable.
{"title":"3D transdimensional ambient noise surface wave tomography of the Reykjanes Peninsula – a feasibility study","authors":"A. R. Dalkhani, X. Zhang, C. Weemstra","doi":"10.3997/2214-4609.202113187","DOIUrl":"https://doi.org/10.3997/2214-4609.202113187","url":null,"abstract":"Summary Seismic surface wave tomography is an effective tool for 3D crustal imaging. Conventionally, a two-step inversion algorithm is used to recover a three-dimensional model of surface wave velocity. That is, starting from surface wave dispersion data (frequency-dependent phase velocities), an initial inversion resulting in a series of (2D) maps of frequency-dependent surface-wave velocity is followed by a separate (1D) depth inversion. A single-step 3D non-linear algorithm has recently been proposed in a Bayesian framework. The algorithm involves a reversible jump Markov chain Monte Carlo approach and is referred to transdimensional tomography. Here, we investigate the feasibility of this transdimensional algorithm for the purpose of recovering the 3D surface wave velocity structure below the Reykjanes Peninsula, southwest Iceland. In particular, we investigate this for the specific receiver configuration for which we have obtained year-long recordings of ambient seismic noise. To that end, we designed a number of synthetic tests using receiver-receiver travel times associated with that station configuration. We find that the transdimensional algorithm successfully recovers the 3D velocity structure of the area. In particular, the algorithm successfully adapts its resolution to the density of rays and the level of data noise. Moreover, quantified solution uncertainty makes the result better interpretable.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127458334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-18DOI: 10.3997/2214-4609.202113327
L. Cui, K. Wu
Summary To further improve the quality and efficiency of subsurface fault zone image and study its geometry. Herein we adopted post-stack seismic data conditioning and a combination of seismic multi-attribute for producing a new hybrid attribute through a supervised multilayer perceptron (MLP) neural network in the Jurassic formation of Cai36 3D prospect located in the eastern part of the Junggar Basin. We first conditioned original seismic data by using the dip-steering cube extracted from the original seismic data. Secondly, we extracted conventional seismic attributes from the conditioned data sensitive to fault zone signatures. Thirdly, we selected a set of “picks” at a time slice representing the presence or absence of fault zones. Then we adopted the supervised MLP neural network to train over the selected seismic attributes extracted at the fault zone and non-fault zone positions. We obtained a new fault probability cube as new attributes. Finally, we analyzed a typical strike-slip fault zone using the new attributes. This study provides an effective way of fault zone imaging from seismic data and adds new insights into its geometry. Therefore, the workflows used here could be widely applied to other 3D surveys.
{"title":"Characterizing Subsurface Damage Zones From 3D Seismic Data Using Artificial Neural Network Approach","authors":"L. Cui, K. Wu","doi":"10.3997/2214-4609.202113327","DOIUrl":"https://doi.org/10.3997/2214-4609.202113327","url":null,"abstract":"Summary To further improve the quality and efficiency of subsurface fault zone image and study its geometry. Herein we adopted post-stack seismic data conditioning and a combination of seismic multi-attribute for producing a new hybrid attribute through a supervised multilayer perceptron (MLP) neural network in the Jurassic formation of Cai36 3D prospect located in the eastern part of the Junggar Basin. We first conditioned original seismic data by using the dip-steering cube extracted from the original seismic data. Secondly, we extracted conventional seismic attributes from the conditioned data sensitive to fault zone signatures. Thirdly, we selected a set of “picks” at a time slice representing the presence or absence of fault zones. Then we adopted the supervised MLP neural network to train over the selected seismic attributes extracted at the fault zone and non-fault zone positions. We obtained a new fault probability cube as new attributes. Finally, we analyzed a typical strike-slip fault zone using the new attributes. This study provides an effective way of fault zone imaging from seismic data and adds new insights into its geometry. Therefore, the workflows used here could be widely applied to other 3D surveys.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129076744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-18DOI: 10.3997/2214-4609.202113162
B. Du, J. Gao, X. Li, G. Zhang, X. Guo, R. Jiang, R. Yang
Summary Shale gas reservoir in deep burial has the features of developed fracture and complex in-situ stress property. To improve the accuracy of stress prediction for shale gas reservoir, we proposed that Differential Horizontal Stress Ratio (DHSR) evaluate the in-situ stress property for shale gas reservoir in function of Poisson’s ratio and fracture density. First of all, new rock physics model for shale gas reservoir is established considering the influence of Total Organic Content (TOC), fracture and anisotropy. Then, pre-stack angle gathers of different azimuthal angles is obtained by Offset Vector Tile (OVT) processing. Finally, pre-stack seismic anisotropy inversion was executed to obtain the Poisson’s ratio and fracture density. DHSR can be estimated by above two parameters. The real data test demonstrated that the predicted DHSR is consisting with prior geology information. The stress evaluation can offer useful geophysical evidence for hydraulic fracturing and well track design.
{"title":"The Application of in-situ Stress Prediction in Shale Gas Reservoir Through Pre-stack Seismic Anisotropy Inversion – A Case Study from Sichuan Basin, SW China.","authors":"B. Du, J. Gao, X. Li, G. Zhang, X. Guo, R. Jiang, R. Yang","doi":"10.3997/2214-4609.202113162","DOIUrl":"https://doi.org/10.3997/2214-4609.202113162","url":null,"abstract":"Summary Shale gas reservoir in deep burial has the features of developed fracture and complex in-situ stress property. To improve the accuracy of stress prediction for shale gas reservoir, we proposed that Differential Horizontal Stress Ratio (DHSR) evaluate the in-situ stress property for shale gas reservoir in function of Poisson’s ratio and fracture density. First of all, new rock physics model for shale gas reservoir is established considering the influence of Total Organic Content (TOC), fracture and anisotropy. Then, pre-stack angle gathers of different azimuthal angles is obtained by Offset Vector Tile (OVT) processing. Finally, pre-stack seismic anisotropy inversion was executed to obtain the Poisson’s ratio and fracture density. DHSR can be estimated by above two parameters. The real data test demonstrated that the predicted DHSR is consisting with prior geology information. The stress evaluation can offer useful geophysical evidence for hydraulic fracturing and well track design.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127994817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-18DOI: 10.3997/2214-4609.202112541
U. Waheed, T. Alkhalifah, B. Li, E. Haghighat, A. Stovas, J. Virieux
Summary Traveltimes corresponding to both compressional and shear waves are needed for many applications in seismology ranging from seismic imaging to earthquake localization. Since the behavior of shear waves in anisotropic media is considerably more complicated than the isotropic case, accurate traveltime computation for shear waves in anisotropic media remains a challenge. Ray tracing methods are often used to compute qSV wave traveltimes but they become unstable around triplication points and, therefore, we often use the weak anisotropy approximation. Here, we employ the emerging paradigm of physics-informed neural networks to solve transversely isotropic eikonal equation for the qSV wave that otherwise are not easily solvable using conventional finite difference methods. By minimizing a loss function formed by imposing the validity of eikonal equation, we train a neural network to produce traveltime solutions that are consistent with the underlying equation. Through tests on synthetic models, we show that the method is capable of producing accurate qSV wave traveltimes even at triplication points and works for arbitrary strength of medium anisotropy.
{"title":"Traveltime Computation for qSV Waves in TI Media Using Physics-Informed Neural Networks","authors":"U. Waheed, T. Alkhalifah, B. Li, E. Haghighat, A. Stovas, J. Virieux","doi":"10.3997/2214-4609.202112541","DOIUrl":"https://doi.org/10.3997/2214-4609.202112541","url":null,"abstract":"Summary Traveltimes corresponding to both compressional and shear waves are needed for many applications in seismology ranging from seismic imaging to earthquake localization. Since the behavior of shear waves in anisotropic media is considerably more complicated than the isotropic case, accurate traveltime computation for shear waves in anisotropic media remains a challenge. Ray tracing methods are often used to compute qSV wave traveltimes but they become unstable around triplication points and, therefore, we often use the weak anisotropy approximation. Here, we employ the emerging paradigm of physics-informed neural networks to solve transversely isotropic eikonal equation for the qSV wave that otherwise are not easily solvable using conventional finite difference methods. By minimizing a loss function formed by imposing the validity of eikonal equation, we train a neural network to produce traveltime solutions that are consistent with the underlying equation. Through tests on synthetic models, we show that the method is capable of producing accurate qSV wave traveltimes even at triplication points and works for arbitrary strength of medium anisotropy.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126040985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-18DOI: 10.3997/2214-4609.202112547
P. Pereira, J. Carneiro, C. Ribeiro, J. M. Martins
Summary The relevance of technological solutions as Carbon Capture, Utilization and Storage (CCUS) have been increasing with the expectation to play a fundamental role in the next decades to mitigate CO2 emissions in Europe and worldwide, essentially those associated to the industrial sectors. Under the scope of the ongoing STRATEGY CCUS project, this work comprises a feasibility study in the Lusitanian basin, a Portuguese promising region with a large potential for CO2 storage, applied to seventeen storage units: thirteen offshore and four onshore. Different methods are presented for a storage resource assessment, including the Boston Square Analysis (BSA) and a four-tiered storage capacity pyramid. The last method aimed to determine critical CO2 storage parameters, namely injectivity and storage capacity, under a stochastic framework with the application of Monte Carlo simulations and a sensitivity analysis of reservoir petrophysical properties. The results from BSA allowed the identification of main gaps and strengths for all storage units in the Lusitanian basin. In addition, the total storage capacity of this basin is about 3.12 Gt CO2, based on stochastic modelling approach, although most the deep saline aquifers were classified as theoretical resources and therefore further characterisation studies must be conducted to increase their maturation level.
{"title":"Resource Maturity and Sensitivity Analysis of CO2 Storage Capacity in the Lusitanian Basin, Portugal","authors":"P. Pereira, J. Carneiro, C. Ribeiro, J. M. Martins","doi":"10.3997/2214-4609.202112547","DOIUrl":"https://doi.org/10.3997/2214-4609.202112547","url":null,"abstract":"Summary The relevance of technological solutions as Carbon Capture, Utilization and Storage (CCUS) have been increasing with the expectation to play a fundamental role in the next decades to mitigate CO2 emissions in Europe and worldwide, essentially those associated to the industrial sectors. Under the scope of the ongoing STRATEGY CCUS project, this work comprises a feasibility study in the Lusitanian basin, a Portuguese promising region with a large potential for CO2 storage, applied to seventeen storage units: thirteen offshore and four onshore. Different methods are presented for a storage resource assessment, including the Boston Square Analysis (BSA) and a four-tiered storage capacity pyramid. The last method aimed to determine critical CO2 storage parameters, namely injectivity and storage capacity, under a stochastic framework with the application of Monte Carlo simulations and a sensitivity analysis of reservoir petrophysical properties. The results from BSA allowed the identification of main gaps and strengths for all storage units in the Lusitanian basin. In addition, the total storage capacity of this basin is about 3.12 Gt CO2, based on stochastic modelling approach, although most the deep saline aquifers were classified as theoretical resources and therefore further characterisation studies must be conducted to increase their maturation level.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"94 1-2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116608460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-18DOI: 10.3997/2214-4609.202113256
B. Wang, F. Zhang
Summary present a SH-SH wave inversion method for the transversely isotropic media with vertical axis of symmetry (VTI media) based on a modified approximation of the SH-SH wave reflection coefficient.
{"title":"Inversion of SH-SH wave anisotropy parameters in VTI media","authors":"B. Wang, F. Zhang","doi":"10.3997/2214-4609.202113256","DOIUrl":"https://doi.org/10.3997/2214-4609.202113256","url":null,"abstract":"Summary present a SH-SH wave inversion method for the transversely isotropic media with vertical axis of symmetry (VTI media) based on a modified approximation of the SH-SH wave reflection coefficient.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"158 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121355760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-18DOI: 10.3997/2214-4609.202113339
J. Lin, E. Haber
Summary This paper avoids the difficulties in using conventional methods in lithology segmentation task by putting the tasks in the frame of computer vision. First, we setup a lithology dataset which contains paired topology, satellite and lithology images; Second, two heated neural networks HyperNet and UNet are introduced and applied in lithology segmentation task. The experiments show that both HyperNet and UNet are efficient and promising for the application in lithology segmentation. % Neural networks can increase the predicted accuracy three times than random guess, that greatly reduce the workload of professional lithology geologist.
{"title":"Lithology segmentation using deep neural network","authors":"J. Lin, E. Haber","doi":"10.3997/2214-4609.202113339","DOIUrl":"https://doi.org/10.3997/2214-4609.202113339","url":null,"abstract":"Summary This paper avoids the difficulties in using conventional methods in lithology segmentation task by putting the tasks in the frame of computer vision. First, we setup a lithology dataset which contains paired topology, satellite and lithology images; Second, two heated neural networks HyperNet and UNet are introduced and applied in lithology segmentation task. The experiments show that both HyperNet and UNet are efficient and promising for the application in lithology segmentation. % Neural networks can increase the predicted accuracy three times than random guess, that greatly reduce the workload of professional lithology geologist.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131099845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-18DOI: 10.3997/2214-4609.202113138
P. Tempone, S. Merten, S. V. Puijvelde, M. Shkrob, F. V. D. Broek
Summary Knowing which overpressure mechanisms are likely to occur in basins can improve the ability to predict abnormal pressures and can thus provide vital information to better manage exploration and drilling risks. Overpressure formation has been studied extensively in the scientific literature, however narrowing down publications to relevant results and linking these publications to a position on the globe in a systematic way can be difficult and time-consuming. In this study, we used semantic search and advanced analytics to screen over 120,000 reviewed and georeferenced publications for mentions of overpressure formation to a basket of ~1100 publications and subsequently analysed results for temporal and spatial trends, allowing the pore pressure specialist to focus on studying data to assess the implications and risk instead of spending time searching for relevant information and data.
{"title":"Leveraging semantic search and advanced analytics to map studies on overpressure mechanisms across the globe","authors":"P. Tempone, S. Merten, S. V. Puijvelde, M. Shkrob, F. V. D. Broek","doi":"10.3997/2214-4609.202113138","DOIUrl":"https://doi.org/10.3997/2214-4609.202113138","url":null,"abstract":"Summary Knowing which overpressure mechanisms are likely to occur in basins can improve the ability to predict abnormal pressures and can thus provide vital information to better manage exploration and drilling risks. Overpressure formation has been studied extensively in the scientific literature, however narrowing down publications to relevant results and linking these publications to a position on the globe in a systematic way can be difficult and time-consuming. In this study, we used semantic search and advanced analytics to screen over 120,000 reviewed and georeferenced publications for mentions of overpressure formation to a basket of ~1100 publications and subsequently analysed results for temporal and spatial trends, allowing the pore pressure specialist to focus on studying data to assess the implications and risk instead of spending time searching for relevant information and data.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133279120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-18DOI: 10.3997/2214-4609.202113257
W. Jia, J. Gao, H. Li, M. Cao, Q. Zeng
Summary In the regions with igneous rocks, it is very difficult to conduct velocity modelling and velocity imaging because of large buried depth, low signal-to-noise ratio of seismic data, large change of lithologies, drastic change of lateral velocity and complex seismic wave field. In this paper, an igneous rock velocity modelling method based on facies-controlled inversion is proposed and applied to migration imaging. Firstly, based on the analysis of lithofacies in this method, the active periods and lithofacies of volcanic rocks are determined, and the initial velocity model is established by using facies-controlled velocity inversion. Secondly, a high-precision velocity model is constructed by multi-information constrained target inversion method. This method has been successfully applied in many prospect areas in western China. Through the comparative analysis of imaging sections and comprehensive attributes, it shows that this method can eliminate the inherited pseudo structures and pseudo faults in the underlying strata of igneous rocks to the maximum extent, and restore the real underground structures, which provides a reference for the velocity-depth modelling and imaging of similar special geologic bodies.
{"title":"A Velocity Model Building Method in the Igneous Rock Based on Facies-controlled Inversion","authors":"W. Jia, J. Gao, H. Li, M. Cao, Q. Zeng","doi":"10.3997/2214-4609.202113257","DOIUrl":"https://doi.org/10.3997/2214-4609.202113257","url":null,"abstract":"Summary In the regions with igneous rocks, it is very difficult to conduct velocity modelling and velocity imaging because of large buried depth, low signal-to-noise ratio of seismic data, large change of lithologies, drastic change of lateral velocity and complex seismic wave field. In this paper, an igneous rock velocity modelling method based on facies-controlled inversion is proposed and applied to migration imaging. Firstly, based on the analysis of lithofacies in this method, the active periods and lithofacies of volcanic rocks are determined, and the initial velocity model is established by using facies-controlled velocity inversion. Secondly, a high-precision velocity model is constructed by multi-information constrained target inversion method. This method has been successfully applied in many prospect areas in western China. Through the comparative analysis of imaging sections and comprehensive attributes, it shows that this method can eliminate the inherited pseudo structures and pseudo faults in the underlying strata of igneous rocks to the maximum extent, and restore the real underground structures, which provides a reference for the velocity-depth modelling and imaging of similar special geologic bodies.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132370976","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: 10.3997/2214-4609.202112989
S. Wang
Summary The transient percolation mathematical model with threshold pressure gradient in vertically fractured multi-well system is developed and solved by using finite element method. Then the wellbore storage coefficient and skin factor are introduced by Laplace Transformation and Stethfest Inversion. In this paper, simulated computation of fractured multi-well system is made by taking the element of rectangular well pattern in the circular impermeable reservoir as an example, and type curves of pressure behavior are drawn. The characteristic of type curves and influences of well property, productivity in adjacent wells, injection-production ratio, well spacing and fracture conductivity are analyzed. The study shows that the testing data of production wells are easily influenced by adjacent wells in the subordinate phase of oil and gas field development. During the well test interpretation, using fractured multi-well system model can eliminate the interferences to a large extent, and improve the utilization and effect of well testing data.
{"title":"Pressure Transient Analysis in Vertically Fractured Multi-well System","authors":"S. Wang","doi":"10.3997/2214-4609.202112989","DOIUrl":"https://doi.org/10.3997/2214-4609.202112989","url":null,"abstract":"Summary The transient percolation mathematical model with threshold pressure gradient in vertically fractured multi-well system is developed and solved by using finite element method. Then the wellbore storage coefficient and skin factor are introduced by Laplace Transformation and Stethfest Inversion. In this paper, simulated computation of fractured multi-well system is made by taking the element of rectangular well pattern in the circular impermeable reservoir as an example, and type curves of pressure behavior are drawn. The characteristic of type curves and influences of well property, productivity in adjacent wells, injection-production ratio, well spacing and fracture conductivity are analyzed. The study shows that the testing data of production wells are easily influenced by adjacent wells in the subordinate phase of oil and gas field development. During the well test interpretation, using fractured multi-well system model can eliminate the interferences to a large extent, and improve the utilization and effect of well testing data.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"67 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":"132172586","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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