Accurate knowledge of fracture extents generated in multistage unconventional completions remains elusive. Crosswell low-frequency distributed acoustic sensing (LF-DAS) measurements can determine the time and location of a frac hit. Knowing where and when a frac hit occurs constrains the fracture extent but does not estimate it quantitatively. A recent study on crosswell LF-DAS demonstrated a simple method to rapidly determine the instantaneous fracture propagation rate when a frac hit occurs. This method, the zero strain rate location method (ZSRLM), is based on laboratory experiments and numerical modeling assuming a radial fracture geometry. An estimated fracture propagation velocity can be used to extrapolate a final fracture extent. The propagation rate is calculated based on dynamic estimates of the nearest distance from the fiber to the front of a propagating fracture.In this work, the ZSRLM is adapted to estimate the distance to the fracture front based on rectangular fracture geometries. A three-dimensional displacement discontinuity method program generates crosswell LF-DAS strain rate waterfall plots considering a single, rectangular fracture of constant height. Over thirty different simulations were conducted varying formation mechanical properties, fracture height, and the vertical and horizontal offset between the treatment and monitor well. For each simulated case, the ZSRLM is used to estimate the distance to the fracture front based on the simulated waterfall plots. The difference between the estimated and actual distance to the front is minimized by a shape factor. The relationship between the shape factor, fracture height ratio, and vertical offset ratio is determined. Using a shape factor improves the performance of the ZSRLM by up to a factor of two for rectangular fractures. The updated ZSRLM is applied to extrapolate final fracture extents in two field cases: a single cluster stage in the Montney formation and a multi-cluster stage of an Austin Chalk completion.
{"title":"Low-frequency distributed acoustic sensing shape factors for fracture front detection","authors":"S. Leggett","doi":"10.1190/int-2022-0100.1","DOIUrl":"https://doi.org/10.1190/int-2022-0100.1","url":null,"abstract":"Accurate knowledge of fracture extents generated in multistage unconventional completions remains elusive. Crosswell low-frequency distributed acoustic sensing (LF-DAS) measurements can determine the time and location of a frac hit. Knowing where and when a frac hit occurs constrains the fracture extent but does not estimate it quantitatively. A recent study on crosswell LF-DAS demonstrated a simple method to rapidly determine the instantaneous fracture propagation rate when a frac hit occurs. This method, the zero strain rate location method (ZSRLM), is based on laboratory experiments and numerical modeling assuming a radial fracture geometry. An estimated fracture propagation velocity can be used to extrapolate a final fracture extent. The propagation rate is calculated based on dynamic estimates of the nearest distance from the fiber to the front of a propagating fracture.In this work, the ZSRLM is adapted to estimate the distance to the fracture front based on rectangular fracture geometries. A three-dimensional displacement discontinuity method program generates crosswell LF-DAS strain rate waterfall plots considering a single, rectangular fracture of constant height. Over thirty different simulations were conducted varying formation mechanical properties, fracture height, and the vertical and horizontal offset between the treatment and monitor well. For each simulated case, the ZSRLM is used to estimate the distance to the fracture front based on the simulated waterfall plots. The difference between the estimated and actual distance to the front is minimized by a shape factor. The relationship between the shape factor, fracture height ratio, and vertical offset ratio is determined. Using a shape factor improves the performance of the ZSRLM by up to a factor of two for rectangular fractures. The updated ZSRLM is applied to extrapolate final fracture extents in two field cases: a single cluster stage in the Montney formation and a multi-cluster stage of an Austin Chalk completion.","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49301845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Compared with conventional geophone data, distributed fiber-optic sensing, including distributed acoustic sensing (DAS), can provide better spatial coverage for imaging the subsurface with finer spatial sampling. Because DAS measures subsurface seismic responses differently than the geophone, imaging technologies (e.g., reverse time migration and full-waveform inversion) that are developed for conventional geophone data cannot be readily applied to original DAS data without causing uncertainties in phase or depth, especially when one compares the DAS imaging results against the usual geophone imaging results. Based on vertical seismic profile field data from a CO2 sequestration site, we have compared the imaging results of the CO2 storage reservoir associated with the DAS and the geophone data, respectively, and we illustrate the differences between the imaging results of the DAS and geophone data. The difference between the DAS and geophone imaging results could be critical in obtaining time-lapse signals for monitoring reservoir changes, e.g., in subsurface CO2 sequestration. We develop to convert DAS to geophone data so that we can reduce the discrepancies between DAS and geophone imaging results and we therefore can reuse existing seismic imaging technologies. Two conversion methods, one physics-based and one deep-learning (DL)-based, are used for the DAS-to-geophone transformation. Field data results demonstrate that the DL-based approach can better successfully improve the alignment between the DAS and geophone images, whereas the physics-based solution is constrained by its assumption.
{"title":"Imaging distributed acoustic sensing-to-geophone conversion data: A field application to CO2 sequestration data","authors":"Yong Ma, Lei Fu, Weichang Li","doi":"10.1190/int-2022-0098.1","DOIUrl":"https://doi.org/10.1190/int-2022-0098.1","url":null,"abstract":"Compared with conventional geophone data, distributed fiber-optic sensing, including distributed acoustic sensing (DAS), can provide better spatial coverage for imaging the subsurface with finer spatial sampling. Because DAS measures subsurface seismic responses differently than the geophone, imaging technologies (e.g., reverse time migration and full-waveform inversion) that are developed for conventional geophone data cannot be readily applied to original DAS data without causing uncertainties in phase or depth, especially when one compares the DAS imaging results against the usual geophone imaging results. Based on vertical seismic profile field data from a CO2 sequestration site, we have compared the imaging results of the CO2 storage reservoir associated with the DAS and the geophone data, respectively, and we illustrate the differences between the imaging results of the DAS and geophone data. The difference between the DAS and geophone imaging results could be critical in obtaining time-lapse signals for monitoring reservoir changes, e.g., in subsurface CO2 sequestration. We develop to convert DAS to geophone data so that we can reduce the discrepancies between DAS and geophone imaging results and we therefore can reuse existing seismic imaging technologies. Two conversion methods, one physics-based and one deep-learning (DL)-based, are used for the DAS-to-geophone transformation. Field data results demonstrate that the DL-based approach can better successfully improve the alignment between the DAS and geophone images, whereas the physics-based solution is constrained by its assumption.","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43441499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Seismic-reflection data contain residual noise after processing, which can cause geologic misinterpretation of noise-sensitive seismic attributes. For detailed subsurface imaging, seismic data conditioning can enhance the visualization of subsurface reflection features down to the limit of seismic resolution. This study on the Gabes-Tripoli Basin of western offshore Libya demonstrates the potential of postprocessing seismic data conditioning using a standard industry 3D-seismic data set. The seismic data exhibit distinct reflection discontinuities and configurations interpreted as folds, reverse faults, and crustal- and gravity-driven normal faults. Other reflection discontinuities are interpreted as imaging the external form and internal architecture of buried carbonate platforms. The seismic-reflection interpretation finds that seismic data conditioning by filtering strongly supports the interpretation of the 3D geometry, type, and trend of distinct subsurface reflection features, particularly if used as input for a structural-attribute generation. Postprocessing seismic conditioning initially improved the signal-to-noise ratio by structure-oriented filtering with edge preservation. Application of this filter configuration emphasized subtle geologic features supporting, e.g., the detection of faults close to the limit of the seismic resolution. At the same time, the filtering resulted in a higher lateral continuity of the individual seismic reflectors, supporting the autotracking of the horizons. Structural attributes generated from the conditioned data such as the variance and curvature imaged more subsurface reflection detail when compared with the structural attributes generated from nonconditioned data. The filter-based workflow proposed can be applied in most seismic interpretation software packages and is recommended to be used as a standard procedure preceding a structure-attribute calculation and structural interpretation of the seismic-reflection data of limited quality.
{"title":"Visualizing subtle structural and stratigraphic features on 3D seismic-reflection data: a case study from offshore Libya","authors":"Nabil Khalifa, S. Back","doi":"10.1190/int-2022-0056.1","DOIUrl":"https://doi.org/10.1190/int-2022-0056.1","url":null,"abstract":"Seismic-reflection data contain residual noise after processing, which can cause geologic misinterpretation of noise-sensitive seismic attributes. For detailed subsurface imaging, seismic data conditioning can enhance the visualization of subsurface reflection features down to the limit of seismic resolution. This study on the Gabes-Tripoli Basin of western offshore Libya demonstrates the potential of postprocessing seismic data conditioning using a standard industry 3D-seismic data set. The seismic data exhibit distinct reflection discontinuities and configurations interpreted as folds, reverse faults, and crustal- and gravity-driven normal faults. Other reflection discontinuities are interpreted as imaging the external form and internal architecture of buried carbonate platforms. The seismic-reflection interpretation finds that seismic data conditioning by filtering strongly supports the interpretation of the 3D geometry, type, and trend of distinct subsurface reflection features, particularly if used as input for a structural-attribute generation. Postprocessing seismic conditioning initially improved the signal-to-noise ratio by structure-oriented filtering with edge preservation. Application of this filter configuration emphasized subtle geologic features supporting, e.g., the detection of faults close to the limit of the seismic resolution. At the same time, the filtering resulted in a higher lateral continuity of the individual seismic reflectors, supporting the autotracking of the horizons. Structural attributes generated from the conditioned data such as the variance and curvature imaged more subsurface reflection detail when compared with the structural attributes generated from nonconditioned data. The filter-based workflow proposed can be applied in most seismic interpretation software packages and is recommended to be used as a standard procedure preceding a structure-attribute calculation and structural interpretation of the seismic-reflection data of limited quality.","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48034054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Archie model is the foundation for calculating oil saturation, but limitations exist when the model is used to calculate oil saturation in water-flooded layer. In the process of water injection, the dynamic change in oil saturation will be caused by the different degrees of water flooding and the properties of the injected water. Under the dynamic condition of water flooding, the Archie model is not suitable for calculating the oil saturation of water flooded layers. By combining dynamic and static methods, a "double ratio" model of the same sedimentary facies layer in the later development stage was established: Rt = R0− R0· f( Fw)=R0[1− f( Fw)]. Based on the parameters of rock resistivity and formation water resistivity, an improved Archie model for calculating oil saturation in water flooded layers of the same sedimentary facies was established. The interpretation of the actual data of the Zhenwu Oilfield in Jiangsu, China shows that the average relative error between the calculation result and the core analysis result is 5.46%. The calculation result is reasonable, which offers a scientific basis for predicting the remaining oil distributions. The computational results have been validated by real datasets. This improved mode can provide experience-based guidance for the calculation of the remaining oil saturation of the water flooded layer in the same sedimentary interpretation layer.
{"title":"Calculation of oil saturation in water-flooded layers based on the modified Archie model","authors":"Xiaodong Zhao, Weilong Wang, Qi Li, Guinan Zhen, Boyu Zhou, Beibei Liu, Jiamin Qin, Yaxuan Zhang","doi":"10.1190/int-2022-0036.1","DOIUrl":"https://doi.org/10.1190/int-2022-0036.1","url":null,"abstract":"The Archie model is the foundation for calculating oil saturation, but limitations exist when the model is used to calculate oil saturation in water-flooded layer. In the process of water injection, the dynamic change in oil saturation will be caused by the different degrees of water flooding and the properties of the injected water. Under the dynamic condition of water flooding, the Archie model is not suitable for calculating the oil saturation of water flooded layers. By combining dynamic and static methods, a \"double ratio\" model of the same sedimentary facies layer in the later development stage was established: Rt = R0− R0· f( Fw)=R0[1− f( Fw)]. Based on the parameters of rock resistivity and formation water resistivity, an improved Archie model for calculating oil saturation in water flooded layers of the same sedimentary facies was established. The interpretation of the actual data of the Zhenwu Oilfield in Jiangsu, China shows that the average relative error between the calculation result and the core analysis result is 5.46%. The calculation result is reasonable, which offers a scientific basis for predicting the remaining oil distributions. The computational results have been validated by real datasets. This improved mode can provide experience-based guidance for the calculation of the remaining oil saturation of the water flooded layer in the same sedimentary interpretation layer.","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44542920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Borehole image logs greatly facilitate detailed characterization of rock formations, especially for the highly heterogeneous and anisotropic carbonate rocks. However, interpreting image logs requires massive time and workforce and lacks consistency and repeatability because it relies heavily on a human interpreter's expertise, experience, and alertness. Thus, we propose to train an end-to-end deep neural network (DNN) for instant and consistent facies classification of carbonate rocks from acoustic image logs and gamma ray logs. The DNN is modified from the well-known U-Net for image segmentation. The training data are composed of two datasets: (1) manually labeled field data measured by different imaging tools from the geologically complex Brazilian pre-salt region and (2) noise-free synthetic data. Some short sections of the field data that are challenging for manual labeling due to entangled features and noises or low resolution are left unlabeled for a blind test after training. All labeled data are divided into a training set, a validation set, and a test set to avoid over-fitting. We demonstrate that the trained DNN achieves 77% classification accuracy for the test set and provides reasonable predictions for the challenging unlabeled sets. It is a great achievement given the complexity and variability of the field data. Compared with manual classification, our DNN provides more consistent and higher-resolution predictions in a highly efficient manner and thus dramatically contributes to automatic image log interpretation.
{"title":"Automatic facies classification from acoustic image logs using deep neural networks","authors":"Nan You, Elita Li, Arthur Cheng","doi":"10.1190/int-2022-0069.1","DOIUrl":"https://doi.org/10.1190/int-2022-0069.1","url":null,"abstract":"Borehole image logs greatly facilitate detailed characterization of rock formations, especially for the highly heterogeneous and anisotropic carbonate rocks. However, interpreting image logs requires massive time and workforce and lacks consistency and repeatability because it relies heavily on a human interpreter's expertise, experience, and alertness. Thus, we propose to train an end-to-end deep neural network (DNN) for instant and consistent facies classification of carbonate rocks from acoustic image logs and gamma ray logs. The DNN is modified from the well-known U-Net for image segmentation. The training data are composed of two datasets: (1) manually labeled field data measured by different imaging tools from the geologically complex Brazilian pre-salt region and (2) noise-free synthetic data. Some short sections of the field data that are challenging for manual labeling due to entangled features and noises or low resolution are left unlabeled for a blind test after training. All labeled data are divided into a training set, a validation set, and a test set to avoid over-fitting. We demonstrate that the trained DNN achieves 77% classification accuracy for the test set and provides reasonable predictions for the challenging unlabeled sets. It is a great achievement given the complexity and variability of the field data. Compared with manual classification, our DNN provides more consistent and higher-resolution predictions in a highly efficient manner and thus dramatically contributes to automatic image log interpretation.","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49461972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The complex of depositional, burial, and diagenetic histories of the Late Cretaceous Nezzazat Group sandstones in Northeastern Africa present the main challenges with regard to reservoir quality. The quality of commercial reservoirs is maintained despite deep burial and the associated high temperature and pressure. The study presents optimum integration of different dataset to address the reservoir quality and reservoir performance controllers. The dataset includes measured porosity and permeability, petrographic point counting data, grain size analysis, X-ray diffraction data, scanning electron microscopy and compaction porosity loss by. The depositional controls on the reservoir quality are the facies, where the higher quality found in the channel and the upper shoreface settings. The coarse-grained sandstone associated with better reservoir quality. The large intergranular porosity is the main porosity control to the fluid to flow. The massive and laminated sandstones are the best quality facies. The labile grains (feldspars and mica) control the permeability distribution. While the secondary diagenetic controllers are the carbonate cementation that inhibited the effects of compaction. The siderite cementation has resulted in a micropore dominated and highly tortuous pore system. Total porosity has largely been preserved in the siderite-cemented sample but virtually eliminated in the dolomite cemented. Low volume of illite associated with better reservoir quality. While the better reservoir quality associated with abundant quartz cementation that protected the primary porosity from compaction. Compaction act as a significant porosity loss factor during diagenesis. Authigenic kaolinite does not significantly affect the reservoir quality. The reservoir sensitivity to formation damage come from the potential for fines (kaolinite, illitic clays, siderite and pyrite) migration within the pore system that are readily to mobilize by fluid flow.
{"title":"Depositional and Diagenetic Controllers on the Sandstone Reservoir Quality of the Late Cretaceous Sediments, Gulf of Suez Basin","authors":"A. Kassem","doi":"10.1190/int-2022-0093.1","DOIUrl":"https://doi.org/10.1190/int-2022-0093.1","url":null,"abstract":"The complex of depositional, burial, and diagenetic histories of the Late Cretaceous Nezzazat Group sandstones in Northeastern Africa present the main challenges with regard to reservoir quality. The quality of commercial reservoirs is maintained despite deep burial and the associated high temperature and pressure. The study presents optimum integration of different dataset to address the reservoir quality and reservoir performance controllers. The dataset includes measured porosity and permeability, petrographic point counting data, grain size analysis, X-ray diffraction data, scanning electron microscopy and compaction porosity loss by. The depositional controls on the reservoir quality are the facies, where the higher quality found in the channel and the upper shoreface settings. The coarse-grained sandstone associated with better reservoir quality. The large intergranular porosity is the main porosity control to the fluid to flow. The massive and laminated sandstones are the best quality facies. The labile grains (feldspars and mica) control the permeability distribution. While the secondary diagenetic controllers are the carbonate cementation that inhibited the effects of compaction. The siderite cementation has resulted in a micropore dominated and highly tortuous pore system. Total porosity has largely been preserved in the siderite-cemented sample but virtually eliminated in the dolomite cemented. Low volume of illite associated with better reservoir quality. While the better reservoir quality associated with abundant quartz cementation that protected the primary porosity from compaction. Compaction act as a significant porosity loss factor during diagenesis. Authigenic kaolinite does not significantly affect the reservoir quality. The reservoir sensitivity to formation damage come from the potential for fines (kaolinite, illitic clays, siderite and pyrite) migration within the pore system that are readily to mobilize by fluid flow.","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45591686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nicolas Clausolles, P. Collon, M. Irakarama, G. Caumon
Variations in the migration velocity model directly affect the position of the imaged reflectors in the subsurface, leading to structural imaging uncertainties. These uncertainties are not explicitly addressed when trying to deterministically build an adequate velocity model. This paper presents a new stochastic geology-controlled velocity modeling method handling the possible presence of a salt weld. This permits to generate a large set of geological scenarios and associated velocity models. Each model is used to remigrate the seismic data. Then, a statistical analysis of the resulting seismic images is performed to quantify the local variability of the seismic responses. The approach is applied to the imaging of salt diapirs, in an iterative scheme (migrate, pick and update). The results show that, similarly to stacking common mid-point gathers, the statistical analysis preferentially preserves recurrent features from an image to another. In particular, this analysis permits to distinguish between connected and detached diapirs without prior knowledge about their connectivity, highlighting the potential of the method to resolve important aspects about basin and reservoir architecture. More generally, it provides quantitative information on the parts of the seismic image most sensitive to migration velocity variations, which opens interesting perspective to quantitative interpretation uncertainty assessment. Finally, the presented application also suggests that it is possible to significantly improve the quality of the generated seismic images by sampling many possible geological scenarios.
{"title":"Stochastic velocity modeling for assessment of imaging uncertainty during seismic migration: application to salt bodies","authors":"Nicolas Clausolles, P. Collon, M. Irakarama, G. Caumon","doi":"10.1190/int-2022-0071.1","DOIUrl":"https://doi.org/10.1190/int-2022-0071.1","url":null,"abstract":"Variations in the migration velocity model directly affect the position of the imaged reflectors in the subsurface, leading to structural imaging uncertainties. These uncertainties are not explicitly addressed when trying to deterministically build an adequate velocity model. This paper presents a new stochastic geology-controlled velocity modeling method handling the possible presence of a salt weld. This permits to generate a large set of geological scenarios and associated velocity models. Each model is used to remigrate the seismic data. Then, a statistical analysis of the resulting seismic images is performed to quantify the local variability of the seismic responses. The approach is applied to the imaging of salt diapirs, in an iterative scheme (migrate, pick and update). The results show that, similarly to stacking common mid-point gathers, the statistical analysis preferentially preserves recurrent features from an image to another. In particular, this analysis permits to distinguish between connected and detached diapirs without prior knowledge about their connectivity, highlighting the potential of the method to resolve important aspects about basin and reservoir architecture. More generally, it provides quantitative information on the parts of the seismic image most sensitive to migration velocity variations, which opens interesting perspective to quantitative interpretation uncertainty assessment. Finally, the presented application also suggests that it is possible to significantly improve the quality of the generated seismic images by sampling many possible geological scenarios.","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42395674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Under-compaction and hydrocarbon generation are the main factors affecting pore pressure. The current seismic pore pressure prediction method is to obtain the overpressure trend by estimating the normal compaction trend (NCT) to predict the physical parameters during normal compaction and comparing the measured parameters. However, selecting a single parameter to indicate overpressure may cause insufficient consideration of factors such as hydrocarbon generation. Since hydrocarbon generation requires specific temperature and other conditions, we roughly divide the pore pressure into two parts: under-compaction in the early stage and hydrocarbon generation after reaching the hydrocarbon generation threshold. We propose a petrophysical model for estimating the normal compaction trend before hydrocarbon generation, modify the bulk modulus of the model, and use the bulk modulus method to calculate the pressure generated by under-compaction; the pressure is added to obtain the final pore pressure. In the shale gas work area in the Sichuan Basin, the prediction results are more in line with the actual situation, and the petrophysical analysis shows that the ratio of free hydrocarbon content and kerogen to water is the influencing factor indicating pore pressure. The practicality of the pore pressure prediction formula considering hydrocarbon generation in oil and gas sweet spots is illustrated through an example in the research area.
{"title":"Estimation of pore pressure considering hydrocarbon generation pressurization using Bayesian inversion","authors":"Jiale Zhang, Z. Zong, Kun Luo","doi":"10.1190/int-2022-0082.1","DOIUrl":"https://doi.org/10.1190/int-2022-0082.1","url":null,"abstract":"Under-compaction and hydrocarbon generation are the main factors affecting pore pressure. The current seismic pore pressure prediction method is to obtain the overpressure trend by estimating the normal compaction trend (NCT) to predict the physical parameters during normal compaction and comparing the measured parameters. However, selecting a single parameter to indicate overpressure may cause insufficient consideration of factors such as hydrocarbon generation. Since hydrocarbon generation requires specific temperature and other conditions, we roughly divide the pore pressure into two parts: under-compaction in the early stage and hydrocarbon generation after reaching the hydrocarbon generation threshold. We propose a petrophysical model for estimating the normal compaction trend before hydrocarbon generation, modify the bulk modulus of the model, and use the bulk modulus method to calculate the pressure generated by under-compaction; the pressure is added to obtain the final pore pressure. In the shale gas work area in the Sichuan Basin, the prediction results are more in line with the actual situation, and the petrophysical analysis shows that the ratio of free hydrocarbon content and kerogen to water is the influencing factor indicating pore pressure. The practicality of the pore pressure prediction formula considering hydrocarbon generation in oil and gas sweet spots is illustrated through an example in the research area.","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47267353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Identification and prediction of high-quality source rocks is the key to obtaining new resources in the exploration area of Cenozoic basins in offshore China. We investigate the seismic response and area of hydrocarbon source rocks based on seismic data, well curves, lithologic interpretation, and geochemical analysis. The target is the source rock development zone of the W Formation in a survey of the South China Sea. The results show that the seismic response of thick layer source rocks differ from surrounding rocks in the seismic profile (strong reflections with opposite polarity at the top and bottom and messy or chaotic reflections inside). Seismic reflections of interlayer source rocks have the characteristics of low frequency and continuous strong amplitude. The dominant frequency and maximum amplitude decrease as the number of mudstone layers increases. Through seismic petrophysical analysis, we have obtained three sensitive parameters of source rock in this survey: clay content, P-wave impedance, and elastic impedance. We use different classification methods to realize the classification and prediction of hydrocarbon source rocks, among which the Kernel Fisher Discriminant Analysis (KFDA) method is the best. The prediction results are consistent with the geological background, geochemical information, and well curves.
{"title":"Seismic response analysis and distribution prediction of source rocks in a survey of the South China Sea","authors":"Weihua Jia, Z. Zong, Hongchao Sun, T. Lan","doi":"10.1190/int-2022-0072.1","DOIUrl":"https://doi.org/10.1190/int-2022-0072.1","url":null,"abstract":"Identification and prediction of high-quality source rocks is the key to obtaining new resources in the exploration area of Cenozoic basins in offshore China. We investigate the seismic response and area of hydrocarbon source rocks based on seismic data, well curves, lithologic interpretation, and geochemical analysis. The target is the source rock development zone of the W Formation in a survey of the South China Sea. The results show that the seismic response of thick layer source rocks differ from surrounding rocks in the seismic profile (strong reflections with opposite polarity at the top and bottom and messy or chaotic reflections inside). Seismic reflections of interlayer source rocks have the characteristics of low frequency and continuous strong amplitude. The dominant frequency and maximum amplitude decrease as the number of mudstone layers increases. Through seismic petrophysical analysis, we have obtained three sensitive parameters of source rock in this survey: clay content, P-wave impedance, and elastic impedance. We use different classification methods to realize the classification and prediction of hydrocarbon source rocks, among which the Kernel Fisher Discriminant Analysis (KFDA) method is the best. The prediction results are consistent with the geological background, geochemical information, and well curves.","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46794155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Numerous studies have demonstrated the capability of supervised deep learning techniques for predicting geologic features of interest from seismic sections, including features that are difficult to identify using traditional interpretation methods. However, the successful application of these techniques in practice has been limited by the difficulty of obtaining a large training data set where the seismic data and corresponding ground truth labels are well-defined. Manually creating large amounts of labels requires a heavy workload, and the uncertainty of the interpretation and labeling process decreases the model’s ability for making accurate predictions. Using the chalk-flint sequence scenario onshore Denmark as an example, we have developed a novel workflow for predicting subresolution thin layers from seismic sections. It entails generating large quantities of synthetic training data with high-quality labels using stochastic geologic modeling, training a convolutional neural network based on the synthetic data set, and applying it to real seismic data. This is, to our knowledge, the first example of using deep learning to predict subresolution thin layers from seismic data based on geostatistically generated training images. It is shown that a neural network trained on synthetic data can predict a realistic number of subresolution flint layers from the real seismic data that have been collected from the Stevns region in Denmark, which has value for the understanding of the overall geologic characteristics of succession and engineering applications such as construction site evaluation.
{"title":"USING SYNTHETIC DATA TRAINED CONVOLUTIONAL NEURAL NETWORK FOR PREDICTING SUB-RESOLUTION THIN LAYERS FROM SEISMIC DATA","authors":"Dongfang Qu, K. Mosegaard, R. Feng, L. Nielsen","doi":"10.1190/int-2022-0059.1","DOIUrl":"https://doi.org/10.1190/int-2022-0059.1","url":null,"abstract":"Numerous studies have demonstrated the capability of supervised deep learning techniques for predicting geologic features of interest from seismic sections, including features that are difficult to identify using traditional interpretation methods. However, the successful application of these techniques in practice has been limited by the difficulty of obtaining a large training data set where the seismic data and corresponding ground truth labels are well-defined. Manually creating large amounts of labels requires a heavy workload, and the uncertainty of the interpretation and labeling process decreases the model’s ability for making accurate predictions. Using the chalk-flint sequence scenario onshore Denmark as an example, we have developed a novel workflow for predicting subresolution thin layers from seismic sections. It entails generating large quantities of synthetic training data with high-quality labels using stochastic geologic modeling, training a convolutional neural network based on the synthetic data set, and applying it to real seismic data. This is, to our knowledge, the first example of using deep learning to predict subresolution thin layers from seismic data based on geostatistically generated training images. It is shown that a neural network trained on synthetic data can predict a realistic number of subresolution flint layers from the real seismic data that have been collected from the Stevns region in Denmark, which has value for the understanding of the overall geologic characteristics of succession and engineering applications such as construction site evaluation.","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44509225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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