Jean-Paul van Gestel, Sebastien Soulas, Garth Naldrett
Over the last decade Distributed Acoustic Sensing (DAS) data acquisition has seen great improvements from better interrogators, engineered fiber and lessons learned from subsea installation and acquisition. This has given us confidence that DAS cables can be installed in wells with subsea trees to be used as receivers for Vertical Seismic Profile (VSP) seismic imaging. VSP imaging for deepwater fields has shown to provide better illumination and higher frequency seismic data. Permanent DAS cable installation can be used to acquire highly repeatable time lapse (4D) data. DAS cables were installed in a number of subsea wells on two deepwater oil fields with the intention to cover the crest of these fields with high frequency seismic data. A system was developed to allow for DAS acquisition on these offshore, subsea wells with long distance tie backs using permanently installed interrogators on the floating platforms and engineered fiber in the wells. On each of these fields a DAS cable has now been installed and a subsequently a zero offset (ZO) DAS VSP was acquired for verification and commissioning. These ZO DAS VSP acquisitions showed high fidelity installations resulting in DAS VSP data with excellent data quality. These first subsea DAS acquisitions show great promise and further installations and acquisitions are planned with the ultimate goal of providing high frequency seismic images over the crest of these fields to reduce the uncertainty in decisions around reservoir management and future infill drilling.
在过去的十年中，分布式声学传感(DAS)数据采集已经取得了巨大的进步，包括更好的询问器、工程光纤以及从海底安装和采集中吸取的经验教训。这给了我们信心，DAS电缆可以安装在有海底采油树的井中，用作垂直地震剖面(VSP)地震成像的接收器。应用于深水油田的VSP成像技术可以提供更好的照明和更高频率的地震数据。永久DAS电缆安装可用于获取高度可重复的时间推移(4D)数据。DAS电缆安装在两个深水油田的一些海底井中，目的是用高频地震数据覆盖这些油田的顶部。开发了一种系统，允许在这些海上海底井中进行DAS采集，这些井使用在浮动平台上永久安装的询问器和井中的工程光纤进行长距离回接。在每个油田都安装了DAS电缆，随后获得了零偏移(ZO) DAS VSP进行验证和调试。这些ZO DAS VSP采集显示了高保真安装，从而使DAS VSP数据具有出色的数据质量。这些首次海底DAS采集显示了巨大的前景，计划进一步安装和采集，最终目标是提供这些油田顶部的高频地震图像，以减少油藏管理和未来填充钻井决策的不确定性。
{"title":"Development of a subsea DAS acquisition system","authors":"Jean-Paul van Gestel, Sebastien Soulas, Garth Naldrett","doi":"10.1190/geo2023-0196.1","DOIUrl":"https://doi.org/10.1190/geo2023-0196.1","url":null,"abstract":"Over the last decade Distributed Acoustic Sensing (DAS) data acquisition has seen great improvements from better interrogators, engineered fiber and lessons learned from subsea installation and acquisition. This has given us confidence that DAS cables can be installed in wells with subsea trees to be used as receivers for Vertical Seismic Profile (VSP) seismic imaging. VSP imaging for deepwater fields has shown to provide better illumination and higher frequency seismic data. Permanent DAS cable installation can be used to acquire highly repeatable time lapse (4D) data. DAS cables were installed in a number of subsea wells on two deepwater oil fields with the intention to cover the crest of these fields with high frequency seismic data. A system was developed to allow for DAS acquisition on these offshore, subsea wells with long distance tie backs using permanently installed interrogators on the floating platforms and engineered fiber in the wells. On each of these fields a DAS cable has now been installed and a subsequently a zero offset (ZO) DAS VSP was acquired for verification and commissioning. These ZO DAS VSP acquisitions showed high fidelity installations resulting in DAS VSP data with excellent data quality. These first subsea DAS acquisitions show great promise and further installations and acquisitions are planned with the ultimate goal of providing high frequency seismic images over the crest of these fields to reduce the uncertainty in decisions around reservoir management and future infill drilling.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":"4 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136283817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Denys Grombacher, Matthew Peter Griffiths, Mathias Østbjerg Vang, Mason Andrew Kass, Jakob Juul Larsen
Spikelets in NMR data occur at predictable frequencies depending only on the repetition time of the excitation sequence. While spikelets are well documented in other NMR fields, we report their presence in steady-state surface NMR data for the first time. These observations are accompanied by analytical developments to understand and predict their behaviour, which follow directly from existing steady-state surface NMR modelling. We show that spikelets represent copies of the surface NMR signal occurring at multiple locations in the frequency domain, including locations that are distinct from the Larmor frequency. These features are shown to be detectable without requiring additional effort in the field, and are shown to be readily processed and modeled with only minor modifications to the processing and modelling workflows. Finally, field spikelet data is also inverted to demonstrate that these data can be fit using subsurface models consistent with a reference surface NMR inversion.
{"title":"Observation of spikelets in steady-state surface nuclear magnetic resonance data","authors":"Denys Grombacher, Matthew Peter Griffiths, Mathias Østbjerg Vang, Mason Andrew Kass, Jakob Juul Larsen","doi":"10.1190/geo2023-0145.1","DOIUrl":"https://doi.org/10.1190/geo2023-0145.1","url":null,"abstract":"Spikelets in NMR data occur at predictable frequencies depending only on the repetition time of the excitation sequence. While spikelets are well documented in other NMR fields, we report their presence in steady-state surface NMR data for the first time. These observations are accompanied by analytical developments to understand and predict their behaviour, which follow directly from existing steady-state surface NMR modelling. We show that spikelets represent copies of the surface NMR signal occurring at multiple locations in the frequency domain, including locations that are distinct from the Larmor frequency. These features are shown to be detectable without requiring additional effort in the field, and are shown to be readily processed and modeled with only minor modifications to the processing and modelling workflows. Finally, field spikelet data is also inverted to demonstrate that these data can be fit using subsurface models consistent with a reference surface NMR inversion.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":"9 18","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135137928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The well-known Biot-Gassmann equation for the fluid dependence of incompressibility of a porous rock is in error. However, a recent numerical calculation on a simple rock model verifies that equation. The calculation appears to be correct, but constitutes a special case, not representative of real rock. Physical experimentation on actual rocks is required to verify the corrected theory.
{"title":"Discussion on the logical error in Gassmann poroelasticity: Numerical computations and generalizations","authors":"Leon Thomsen","doi":"10.1190/geo2023-0560.1","DOIUrl":"https://doi.org/10.1190/geo2023-0560.1","url":null,"abstract":"The well-known Biot-Gassmann equation for the fluid dependence of incompressibility of a porous rock is in error. However, a recent numerical calculation on a simple rock model verifies that equation. The calculation appears to be correct, but constitutes a special case, not representative of real rock. Physical experimentation on actual rocks is required to verify the corrected theory.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":"114 40","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135138242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Denys Grombacher, Matthew Peter Griffiths, Mason Andrew Kass, Jakob Juul Larsen
Recent developments in surface nuclear magnetic resonance (NMR) data transmit schemes, called steady-state measurements, involve acquisition of the NMR signal during a train of closely spaced identical pulses, and show great promise to enhance the measurements signal-to-noise ratio. The steady-state signal displays a complex dependence on a range of experimental parameters, such as the strength of the individual pulse and the separation between the pulses, as well as subsurface parameters, including the relaxation times controlling the time-dependence of the NMR signals. It is imperative to understand the role that each of these parameters plays in controlling the depth of origin for steady-state signals so as to enable an optimization of a suite of measurements consisting to the fewest possible measurements delivering satisfactory resolution. A range of synthetic studies are conducted to gain insights into controls on steady-state signals depths of origin. Relaxation times, duty cycle, pulse train timing are all observed to play strong controls on the signals depth, in addition to the pulse duration and current strength. Discussion of whether high-duty cycle steady-state sequences may enhance depth penetration is given, along with the presentation of a field data set composed on a non-traditional depth sounding approach, where repetition times are varied to encode depth sensitivity.
{"title":"An investigation of factors affecting the depths of steady-state surface NMR signals","authors":"Denys Grombacher, Matthew Peter Griffiths, Mason Andrew Kass, Jakob Juul Larsen","doi":"10.1190/geo2023-0068.1","DOIUrl":"https://doi.org/10.1190/geo2023-0068.1","url":null,"abstract":"Recent developments in surface nuclear magnetic resonance (NMR) data transmit schemes, called steady-state measurements, involve acquisition of the NMR signal during a train of closely spaced identical pulses, and show great promise to enhance the measurements signal-to-noise ratio. The steady-state signal displays a complex dependence on a range of experimental parameters, such as the strength of the individual pulse and the separation between the pulses, as well as subsurface parameters, including the relaxation times controlling the time-dependence of the NMR signals. It is imperative to understand the role that each of these parameters plays in controlling the depth of origin for steady-state signals so as to enable an optimization of a suite of measurements consisting to the fewest possible measurements delivering satisfactory resolution. A range of synthetic studies are conducted to gain insights into controls on steady-state signals depths of origin. Relaxation times, duty cycle, pulse train timing are all observed to play strong controls on the signals depth, in addition to the pulse duration and current strength. Discussion of whether high-duty cycle steady-state sequences may enhance depth penetration is given, along with the presentation of a field data set composed on a non-traditional depth sounding approach, where repetition times are varied to encode depth sensitivity.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":"8 15","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135137776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The development of the distributed acoustic sensing (DAS) technique enables us to record seismic data at a significantly improved spatial sampling rate at meter scales, which offers new opportunities for high-resolution subsurface imaging. However, DAS recordings are often characterized by low signal-to-noise ratio (S/N) due to the presence of data noise, significantly degrading the reliability of imaging and interpretation. Current DAS data noise reduction methods remain insufficient in simultaneously preserving weak signals and eliminating various types of noise. Particularly, when dealing with DAS data that are contaminated by four types of noise (i.e., high-frequency noise, high-amplitude erratic noise, horizontal noise, and random background noise), it becomes challenging to attenuate the weak signals while maintaining fine-scale features. To address the issues raised above, we propose an integrated local orthogonalization (LO) method that can remove a mixture of different types of noise while protecting the useful signal. The proposed LO method effectively eliminates the aforementioned noise by concatenating multiple denoising operators including a bandpass filter, structure-oriented spatially-varying median filter, dip filter in the frequency-wavenumber domain, and curvelet filter. Next, the local orthogonalization weighting operator is applied to extract signal energy from the removed noise section. We demonstrate the robustness of the proposed LO method on various challenging DAS datasets from the FORGE geothermal field. The denoising results demonstrate that the proposed LO method can successfully minimize the levels of different types of noise while preserving the energy of weak signals.
{"title":"Protecting the weak signals in distributed acoustic sensing data processing using local orthogonalization: the FORGE data example","authors":"Yapo Abolé Serge Innocent Oboué, Yunfeng Chen, Sergey Fomel, Yangkang Chen","doi":"10.1190/geo2022-0676.1","DOIUrl":"https://doi.org/10.1190/geo2022-0676.1","url":null,"abstract":"The development of the distributed acoustic sensing (DAS) technique enables us to record seismic data at a significantly improved spatial sampling rate at meter scales, which offers new opportunities for high-resolution subsurface imaging. However, DAS recordings are often characterized by low signal-to-noise ratio (S/N) due to the presence of data noise, significantly degrading the reliability of imaging and interpretation. Current DAS data noise reduction methods remain insufficient in simultaneously preserving weak signals and eliminating various types of noise. Particularly, when dealing with DAS data that are contaminated by four types of noise (i.e., high-frequency noise, high-amplitude erratic noise, horizontal noise, and random background noise), it becomes challenging to attenuate the weak signals while maintaining fine-scale features. To address the issues raised above, we propose an integrated local orthogonalization (LO) method that can remove a mixture of different types of noise while protecting the useful signal. The proposed LO method effectively eliminates the aforementioned noise by concatenating multiple denoising operators including a bandpass filter, structure-oriented spatially-varying median filter, dip filter in the frequency-wavenumber domain, and curvelet filter. Next, the local orthogonalization weighting operator is applied to extract signal energy from the removed noise section. We demonstrate the robustness of the proposed LO method on various challenging DAS datasets from the FORGE geothermal field. The denoising results demonstrate that the proposed LO method can successfully minimize the levels of different types of noise while preserving the energy of weak signals.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":"93 18","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135092248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Giuseppe Provenzano, Romain Brossier, Ludovic Métivier
Full waveform inversion (FWI) in the North Sea has demonstrated its imaging power starting from low-resolution models obtained by traveltime tomography, enriching them with geologically interpretable fine-scale details. However, building a traveltime-based kinematically accurate starting model for FWI is a time-consuming and rather subjective process requiring phase identification and selection. The two main problems faced by FWI starting from non-informative initial models are the liability to cycle-skipping and a lack of sensitivity to low-wavenumbers in the deep subsurface not sampled by turning waves. On a North Sea ocean-bottom-cable (OBC) 3D dataset, we apply a novel Vp-building methodology that addresses those issues by jointly inverting reflections and refractions (JFWI) using a robust misfit function in the vertical traveltime domain (pseudotime). While pseudotime addresses reflectivity-velocity coupling and attenuates phase-ambiguities at short offsets, a graph-space optimal transport (GSOT) objective function with dedicated data windowing averts cycle-skipping at intermediate-to-long offsets. A fast and balanced reflectivity reconstrution is obtained prior to JFWI thanks to an asymptotic-preconditioned Impedance Waveform Inversion (IpWI). Starting from a linearly increasing one-dimensional model, GSOT-pseudotime JFWI is effective at obtaining a meaningful P-wave velocity macromodel down to depths sampled by reflections only, without phase identification and picking. P-wave FWI, finally, starting from the JFWI-based model, injects the high-wavenumbers missing in the JFWI solution, attaining apparent improvements in both shallow and deep model reconstruction and imaging compared to the previous studies in the literature, and a satisfactory prediction of the ground-truth logs.
{"title":"Robust and efficient waveform-based velocity-model-building by optimal-transport in the pseudotime domain: an OBC case study in the North Sea","authors":"Giuseppe Provenzano, Romain Brossier, Ludovic Métivier","doi":"10.1190/geo2023-0052.1","DOIUrl":"https://doi.org/10.1190/geo2023-0052.1","url":null,"abstract":"Full waveform inversion (FWI) in the North Sea has demonstrated its imaging power starting from low-resolution models obtained by traveltime tomography, enriching them with geologically interpretable fine-scale details. However, building a traveltime-based kinematically accurate starting model for FWI is a time-consuming and rather subjective process requiring phase identification and selection. The two main problems faced by FWI starting from non-informative initial models are the liability to cycle-skipping and a lack of sensitivity to low-wavenumbers in the deep subsurface not sampled by turning waves. On a North Sea ocean-bottom-cable (OBC) 3D dataset, we apply a novel Vp-building methodology that addresses those issues by jointly inverting reflections and refractions (JFWI) using a robust misfit function in the vertical traveltime domain (pseudotime). While pseudotime addresses reflectivity-velocity coupling and attenuates phase-ambiguities at short offsets, a graph-space optimal transport (GSOT) objective function with dedicated data windowing averts cycle-skipping at intermediate-to-long offsets. A fast and balanced reflectivity reconstrution is obtained prior to JFWI thanks to an asymptotic-preconditioned Impedance Waveform Inversion (IpWI). Starting from a linearly increasing one-dimensional model, GSOT-pseudotime JFWI is effective at obtaining a meaningful P-wave velocity macromodel down to depths sampled by reflections only, without phase identification and picking. P-wave FWI, finally, starting from the JFWI-based model, injects the high-wavenumbers missing in the JFWI solution, attaining apparent improvements in both shallow and deep model reconstruction and imaging compared to the previous studies in the literature, and a satisfactory prediction of the ground-truth logs.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":"309 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135475580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In land seismic acquisition, the quality of common-shot gathers is severely degraded by Wind Turbine Noise (WTN) when wind turbines are operating continuously in surveys. The high-amplitude WTN overlap or even completely submerge the body and surface waves (signals). Through time-space and frequency analysis, three main features of the WTN are observed: 1) it is periodic with nearly constant frequencies over time; 2) it is coherent but exhibits different apparent velocities in space; 3) it has relatively narrow bands with varying central frequencies. The first feature enables WTN to distort signals from shallow to deep, while the latter two features make traditional methods that separate noise and signals based on velocity and frequency differences less effective. To suppress the WTN, we first analyze its formation and propagation mechanism, and then propose a WTN simulation model to validate the presented mechanism. Based on our analysis of WTN and signals, we consider common-shot gathers as the linear superpositions of periodic WTN and relatively broadband signals (referred to as low-oscillatory signals). This additive mixture aligns with the feasibility premise of Morphological Component Analysis (MCA). Finally, based on MCA theory, we propose a sparsity-promoting separation method to suppress WTN in common-shot gathers. To implement our separation method, we construct two dictionaries using the Tunable Q-factor Wavelet Transform (TQWT) and the Discrete Cosine Transform (DCT). TQWT and DCT can sparsely represent oscillating waves (signals) and periodic waves (WTN), respectively. This work contributes to the existing knowledge of WTN separation by modeling the periodicity of WTN and the low-oscillatory behavior of signal, rather than relying on velocity or frequency differences. The proposed method has been tested on both synthetic and field data, and both tests demonstrate its effectiveness in separating WTN and preserving signals.
{"title":"Modeling and Sparsity Promoting Separation of Wind Turbine Noise in Common-shot Gathers","authors":"Yanglijiang Hu, Xiaokai Wang, Qinlong Hou, Dawei Liu, Xinmin Shang, Meng Zhang, Wenchao Chen","doi":"10.1190/geo2023-0033.1","DOIUrl":"https://doi.org/10.1190/geo2023-0033.1","url":null,"abstract":"In land seismic acquisition, the quality of common-shot gathers is severely degraded by Wind Turbine Noise (WTN) when wind turbines are operating continuously in surveys. The high-amplitude WTN overlap or even completely submerge the body and surface waves (signals). Through time-space and frequency analysis, three main features of the WTN are observed: 1) it is periodic with nearly constant frequencies over time; 2) it is coherent but exhibits different apparent velocities in space; 3) it has relatively narrow bands with varying central frequencies. The first feature enables WTN to distort signals from shallow to deep, while the latter two features make traditional methods that separate noise and signals based on velocity and frequency differences less effective. To suppress the WTN, we first analyze its formation and propagation mechanism, and then propose a WTN simulation model to validate the presented mechanism. Based on our analysis of WTN and signals, we consider common-shot gathers as the linear superpositions of periodic WTN and relatively broadband signals (referred to as low-oscillatory signals). This additive mixture aligns with the feasibility premise of Morphological Component Analysis (MCA). Finally, based on MCA theory, we propose a sparsity-promoting separation method to suppress WTN in common-shot gathers. To implement our separation method, we construct two dictionaries using the Tunable Q-factor Wavelet Transform (TQWT) and the Discrete Cosine Transform (DCT). TQWT and DCT can sparsely represent oscillating waves (signals) and periodic waves (WTN), respectively. This work contributes to the existing knowledge of WTN separation by modeling the periodicity of WTN and the low-oscillatory behavior of signal, rather than relying on velocity or frequency differences. The proposed method has been tested on both synthetic and field data, and both tests demonstrate its effectiveness in separating WTN and preserving signals.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135479759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Peng Li, Mingliang Liu, Motaz Alfarraj, Pejman Tahmasebi, Dario Grana
The main challenge in the inversion of seismic data to predict the petrophysical properties of hydrocarbon-saturated rocks is that the physical relations that link the data to the model properties are often non-linear and the solution of the inverse problem is generally not unique. As a possible alternative to traditional stochastic optimization methods, we propose to adopt machine learning algorithms by estimating relations between data and unknown variables from a training dataset with limited computational cost and without prior assumptions. We present a probabilistic approach for seismic petrophysical inversion based on physics-informed neural network with a reparameterization network. The novelty of the proposed approach includes the definition of a physics-informed neural network algorithm in a probabilistic setting, the use of an additional neural network for rock physics model hyperparameters estimation, and the implementation of Approximate Bayesian Computation to quantify the model uncertainty. The reparameterization network allows including unknown model parameters, such as rock physics model hyperparameters. The proposed method predicts the most likely model of petrophysical variables based on the input seismic dataset and the training dataset and provides a quantification of the uncertainty of the model. The method is scalable and can be adapted to various geophysical inverse problems. We test the inversion on a North Sea dataset with post-stack and pre-stack data to obtain the prediction of petrophysical properties. Compared to regular neural networks, the predictions of the proposed method show higher accuracy in the predicted results and allow quantifying the posterior uncertainty.
{"title":"Probabilistic physics-informed neural network for seismic petrophysical inversion#xD;","authors":"Peng Li, Mingliang Liu, Motaz Alfarraj, Pejman Tahmasebi, Dario Grana","doi":"10.1190/geo2023-0214.1","DOIUrl":"https://doi.org/10.1190/geo2023-0214.1","url":null,"abstract":"The main challenge in the inversion of seismic data to predict the petrophysical properties of hydrocarbon-saturated rocks is that the physical relations that link the data to the model properties are often non-linear and the solution of the inverse problem is generally not unique. As a possible alternative to traditional stochastic optimization methods, we propose to adopt machine learning algorithms by estimating relations between data and unknown variables from a training dataset with limited computational cost and without prior assumptions. We present a probabilistic approach for seismic petrophysical inversion based on physics-informed neural network with a reparameterization network. The novelty of the proposed approach includes the definition of a physics-informed neural network algorithm in a probabilistic setting, the use of an additional neural network for rock physics model hyperparameters estimation, and the implementation of Approximate Bayesian Computation to quantify the model uncertainty. The reparameterization network allows including unknown model parameters, such as rock physics model hyperparameters. The proposed method predicts the most likely model of petrophysical variables based on the input seismic dataset and the training dataset and provides a quantification of the uncertainty of the model. The method is scalable and can be adapted to various geophysical inverse problems. We test the inversion on a North Sea dataset with post-stack and pre-stack data to obtain the prediction of petrophysical properties. Compared to regular neural networks, the predictions of the proposed method show higher accuracy in the predicted results and allow quantifying the posterior uncertainty.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":"354 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135636184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gabriele Busanello, Ran Bachrach, Ali Sayed, Bahaa Soliman
Distributed acoustic sensing (DAS) technology enables high-density seismic acquisition at a fraction of the cost. When deployed on the surface, surface distributed acoustic sensing (S-DAS) acquisition provides a cost-effective solution for dense high-resolution near surface characterization through the analysis and inversion of surface waves. This is made possible by the relatively low cost of the fiber and the dense spatial sampling of the realized seismic data. S-DAS data were collected during the acquisition of a 3D land large-scale field test and processed with a focus on recent advancements in the use of surface-wave analysis and inversion. We compare and validate the result from the S-DAS recording with co-located multicomponent (3C) geophones and a conventional high-density surface seismic nodal acquisition. The comparison to 3C geophones demonstrated that for applications such as surface wave inversion S-DAS can outperform conventional geophones and shows consistency between electrical resistivity tomography (ERT) and surface seismic inversion from S-DAS. Additionally, continuous passive recording of environmental noise also offers a convenient alternative to active shooting allowing the surface wave inversion from reconstructed virtual shots.
{"title":"SURFACE DISTRIBUTED ACOUSTIC SENSING (S-DAS) FOR HIGH RESOLUTION NEAR SURFACE CHARACTERIZATION. RESULTS FROM A 3D ONSHORE FIELD EXPERIMENT","authors":"Gabriele Busanello, Ran Bachrach, Ali Sayed, Bahaa Soliman","doi":"10.1190/geo2023-0084.1","DOIUrl":"https://doi.org/10.1190/geo2023-0084.1","url":null,"abstract":"Distributed acoustic sensing (DAS) technology enables high-density seismic acquisition at a fraction of the cost. When deployed on the surface, surface distributed acoustic sensing (S-DAS) acquisition provides a cost-effective solution for dense high-resolution near surface characterization through the analysis and inversion of surface waves. This is made possible by the relatively low cost of the fiber and the dense spatial sampling of the realized seismic data. S-DAS data were collected during the acquisition of a 3D land large-scale field test and processed with a focus on recent advancements in the use of surface-wave analysis and inversion. We compare and validate the result from the S-DAS recording with co-located multicomponent (3C) geophones and a conventional high-density surface seismic nodal acquisition. The comparison to 3C geophones demonstrated that for applications such as surface wave inversion S-DAS can outperform conventional geophones and shows consistency between electrical resistivity tomography (ERT) and surface seismic inversion from S-DAS. Additionally, continuous passive recording of environmental noise also offers a convenient alternative to active shooting allowing the surface wave inversion from reconstructed virtual shots.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":"250 1‐2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135678742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Seismic 3D full-horizon tracking is a fundamental and crucial step in sequence analysis and reservoir modeling. Existing automatic full-horizon tracking approaches lack effective methods for representing the stratigraphic sequence relationships in seismic data. However, the inability to represent the stratigraphic sequence relationships fully and accurately makes it challenging to address discontinuous areas affected by faults and unconformities. To address this issue effectively, we propose a knowledge graph representing the stratigraphic sequence relationship, which enables the simultaneous extraction of all horizon surfaces once the stratigraphic distribution of the seismic data is obtained. In this method, horizon patches are generated, and the fault attribute is calculated, followed by the construction of an initial knowledge graph that characterizes the overall distribution of both horizon patches and faults. The initial knowledge graph comprises nodes and edges. Here, the nodes represent horizon patches, and their attributes cover the geographical location information of the patches and faults. Simultaneously, edges represent the relationship between horizon patches, including the stratigraphic sequence relationship, and their attributes illustrate the potential of connecting these patches. Furthermore, we developed a multi-layer knowledge graph based on the point set topology to fuse the nodes. This allows for continuous merging of horizon patches to obtain horizon surfaces across discontinuities with the constraints of fault attributes and stratigraphic sequence relationships in 3D space. Both synthetic and field examples demonstrated that the proposed method can effectively represent the stratigraphic sequence relationships and accurately track horizons dislocated in discontinuous areas with faults and unconformities.
{"title":"Seismic 3D full-horizon tracking based on knowledge graph to represent the stratigraphic sequence relationship","authors":"Xin He, Cheng Zhou, Yusheng Zhang, Feng Qian, Guangmin Hu, Yalin Li","doi":"10.1190/geo2023-0360.1","DOIUrl":"https://doi.org/10.1190/geo2023-0360.1","url":null,"abstract":"Seismic 3D full-horizon tracking is a fundamental and crucial step in sequence analysis and reservoir modeling. Existing automatic full-horizon tracking approaches lack effective methods for representing the stratigraphic sequence relationships in seismic data. However, the inability to represent the stratigraphic sequence relationships fully and accurately makes it challenging to address discontinuous areas affected by faults and unconformities. To address this issue effectively, we propose a knowledge graph representing the stratigraphic sequence relationship, which enables the simultaneous extraction of all horizon surfaces once the stratigraphic distribution of the seismic data is obtained. In this method, horizon patches are generated, and the fault attribute is calculated, followed by the construction of an initial knowledge graph that characterizes the overall distribution of both horizon patches and faults. The initial knowledge graph comprises nodes and edges. Here, the nodes represent horizon patches, and their attributes cover the geographical location information of the patches and faults. Simultaneously, edges represent the relationship between horizon patches, including the stratigraphic sequence relationship, and their attributes illustrate the potential of connecting these patches. Furthermore, we developed a multi-layer knowledge graph based on the point set topology to fuse the nodes. This allows for continuous merging of horizon patches to obtain horizon surfaces across discontinuities with the constraints of fault attributes and stratigraphic sequence relationships in 3D space. Both synthetic and field examples demonstrated that the proposed method can effectively represent the stratigraphic sequence relationships and accurately track horizons dislocated in discontinuous areas with faults and unconformities.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":"261 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135678730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：