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EAGE 2020 Annual Conference & Exhibition Online
- Conference date: December 8-11, 2020
- Location: Online
- Published: 08 December 2020
21 - 40 of 368 results
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Imaging and Quantifying CO2 Containment Storage Loss Using 3D CSEM
Authors J.P. Morten and A. BjørkeSummaryWe investigate how 3D CSEM can provide subsurface mapping for a hypothetical CO2 containment storage loss scenario. The CO2 distribution in the injection unit is reduced along a supposed transmissible fault. The resistivity reduction due to CO2 escape from the reservoir can be recovered using 3D inversion. Using a rock physics model, we can quantify the change of CO2 volume in the injection unit. Our study results illustrate both the utility of 3D CSEM for CCS and the uncertainty and resolution limitations of the method.
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Near Surface Velocity Estimation from Phase Velocity-Frequency Panels with Deep Learning
By P. ZwartjesSummaryWe have trained a neural network to estimate the near surface Vs profile directly from phase velocity vs. frequency panels. These panels are constructed from the raw shot gathers with all the surface and body waves present. As such, the method has the same goal as dispersion curve inversion. The same approach is applicable to estimation of Vp also. The method has been tested on the SEAM Arid model synthetic dataset and produces encouraging results. Generalization of the method to unseen data remains a challenge, but by brute force modelling and training progress can be made.
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A Novel Identification Method of Carbonate Reservoirs Utilizing the Elastic Theory of Porous and Fractured Media
More LessSummaryFractures and pores coexist in carbonate reservoirs, and this complex pore structure has a significant impact on acoustic logging. This paper studies the variation of acoustic velocity in carbonate samples based on acoustic rock physics experiments. At the same time, a theoretical gas-bearing reservoir identification template is established based on the elastic theory of porous and fractured media, and the gas-bearing reservoir identification template is calibrated with acoustic velocity experimental data on. Based on the above research, a quantitative identification template for gas-bearing reservoirs is established. The case study has verified the reliability of the gas-bearing carbonate reservoir identification method.
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Rayleigh Wave Phase-Slope Tomography
Authors Z. Zhang, T. Alkhalifah, E. Saygin and L. HeSummaryTraditional approaches of utilizing the dispersion curves in S-wave velocity reconstruction have many limitations, namely, the 1D layered model assumption and the automatic/manual picking of dispersion curves. On the other hand, conventional full-waveform inversion (FWI) can easily converge to one of the local minima when applied directly to complicated surface waves. Alternatively, a wave equation dispersion spectrum inversion can avoid these limitations, by inverting the slopes of arrivals at different frequencies. A local-similarity objective function is used to avoid possible cycle skipping. We apply the proposed method on the large-scale ambient-noise data recorded at a large-N array with over 3000 recorders. So we can estimate the shear-wave velocities down to 1.8 km depth. The main benefits of the proposed method are 1) it handles lateral variations; 2) it avoids picking dispersion curves; 3) it utilizes both the fundamental- and higher-modes of Rayleigh waves, and 4) it can be solved using gradient-based local optimizations. A good match between the observed and predicted dispersion spectra also leads to a reasonably good match between the observed and predicted waveforms, though the inversion does not aim to match the waveforms.
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High-Resolution Regularized Elastic Full Waveform Inversion Assisted by Deep Learning
Authors Y. Li, T. Alkhalifah and Z. ZhangSummaryElastic full waveform inversion (EFWI) can, theoretically, give high-resolution estimates of the subsurface. However, in practice, the resolution and illumination of EFWI are limited by the bandwidth and aperture of seismic data. The often-present wells in developed fields as well as some exploratory regions can provide a complementary illumination to the target area. We, thus, introduce a regularization technique, which combines the surface seismic and well log data, to help improve the resolution of EFWI. Using deep fully connected layers, we train our neural network to identify the relation between the means and variances at the well, with the inverted model from an initial EFWI application. The network is used to map the means and variances extracted from the well to the whole model domain. We then perform another EFWI in which we fit the predicted data to the observed one as well as fit the model over a Gaussian window to the predicted means the variances. The tests on the synthetic and real seismic data demonstrate that the proposed method can effectively improve the resolution and illumination of deep-buried reservoirs, which are less illuminated by seismic data.
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A New Rock Physics Model of Shale on the Theory of Micro-Nano Pores
More LessSummaryAs the main storage space of shale reservoir, micro-nano pores have a great influence on the overall elastic property of shale. As a special type of organic mineral in shale, the state of kerogen in shale varies with maturity, meanwhile, kerogen is also the primary place for the growth of micro-nano pores. Conventional shale rock physics model cannot reflect the role of micro-nano pores, thus, we adopt a theory of micro-nano pore to describe its characteristics. Considering the micro-nanometer pores and the state of kerogen at different maturity, we establish a new rock physical model by applying the micro-nano pore model, anisotropic SCA-DEM model, anisotropic Eshelby-Cheng model and Brown-Korringa solid substitution equation. The sensitivity analysis show that the micro-nano pores have the greatest effect on the mature shale, while kerogen and clay have the least effect on the overmature shale.
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Application of Spectral Ratio on Reservoir Classification and Evaluation for Full Waveform Acoustic Logging
More LessSummaryFull acoustic waveform logging provides robust information about formation anelasticity and porous fluid conductivity, which can be an indicator of fractures. In contrast to conventional sonic well logging, details of waveforms of both compressional and shear waves are obtained through full waveform acoustic logging. Amplitude ratios are commonly used for the computation of attenuation based on a constant Q model, although the intrinsic attenuation always involves with fluid flow and is generally considered as frequency dependent. Such assumption makes sense for monopole acoustic logging which holds a relatively narrow effective frequency range. In this study we find consistency between amplitude ratio and conventional logging curves and exciting results have been shown in reservoir classification and evaluation.
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Uncovering the Kujung Carbonate Facies Complexities in an Undisturbed North Madura Platform, East Java Basin, Indonesia
Authors M.N. Juliansyah, R.K. Pratama, P. Monalia, A.K. Wijaya, A. Donurizki and R. IsmailSummaryThe East Java Basin is a productive Tertiary Basin that has been producing hydrocarbon. During the basin forming and development, East Java Basin has gone through three major geological events from Late Cretaceous until present day. North Madura Platform is located in the northern part of the East Java Basin, formed during the Paleogene divergence. During all basin development stages, North Madura Platform has been undisturbed by tectonic disturbances, allowing carbonate to grow and develop across the platform.
Identifying the varieties of the carbonate has been an integral part of the basin analysis in East Java Basin, as most of the proven reservoirs in North Madura Platform are found in Kujung carbonate. Various types of carbonate have been identified using the seismic data, including shelf edge carbonate, platform carbonate, and patch reefs.
Seismic FWI PSDM reprocessing in 2019 has shown tremendous improvement in resolving carbonate seismic facies in North Madura Platform. Detailed carbonate facies has been identified inside Kujung Carbonate, showing different facies and internal characteristics of the carbonates in different regions. This study will showcase the complexities identified in the carbonates that have been developed in North Madura Platform based on seismic facies characteristics from seismic FWI PSDM reprocessing.
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A Novel 4IR Framework for Interwell Saturation Mapping
Authors K. Katterbauer and A.F. MarsalaSummaryThis work focuses on a novel artificial intelligence framework for interwell saturation mapping, incorporating geophysical deep electromagnetic (EM) tomography into near wellbore high resolution characterization. Well logs, dynamic production data and a crosswell electromagnetic tomography of a reservoir volume around the wellbore were used as an AI training set and then subsequently employed to obtain better diagnostics of interwell saturation mapping of the interwell volume in a tight fractured carbonate reservoir. The innovative 4IR approach was deployed on a realistic reservoir box model of fractured carbonate formations, delivering promising results for a more general application in different geological contexts.
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Seeing through the Gas - Improved Imaging on Marte with a Dedicated MAZ Velocity Survey
Authors L. Saxton, J. Northall, M. Wingham, I. De Lemos, X. Song, G. Jones, I. Espin, J. Palmer, M. Chappell and R. RefaatSummaryThe Marte field is located in the North East part of Block 31 offshore Angola in water depths up to 2km. The Marte reservoirs are made up of 3–5km wide lower Miocene deep water erosional turbidite slope channel complexes in a four-way asymmetrical anticline structure. Imaging on the Eastern flank of the structure is compromised due to an overlying shallow gas hydrate channel that has resulted in velocity model errors and absorption effects that have to date not been adequately modelled and compensated for.
In this case study we will show how an integrated workplan was put in place to resolve the imaging issues observed on the Marte field. This involved re-creating the issue with a synthetic model, using this model to design and then acquire a bespoke velocity survey and then using the resulting data to produce updated models of velocity, anisotropy and Q. These models were then used to generate improved images from a 4D monitor survey acquired over PSVM prior to the velocity survey. Finally, we will show the results of post imaging analysis performed to determine the most significant survey design factor that contributed to the improved models and images obtained.
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3D Elastic Passive Source Inversion with an Equivalent Source
Authors H. Wang and T. AlkhalifahSummaryThe key challenge associated with microseismic event measurements is the accurate estimation of the passive source locations and their onset time. Using both compressive and shear waves, that are generated by microseismic events and recorded at the receivers, is conceivably a more accurate and practical way to invert for the sources. Here, we represent the conventional seismic moment tensor source term of the elastic wave equation by an equivalent source. The equivalent source term consists of source images and source functions. Thus, in the optimization problem, we update the source locations (spatial), source functions (temporal) and velocities, simultaneously, using a waveform inversion scheme. We eventually provide an alternative source representation of its mechanism compared to the moment tensor focusing on the components we can invert. The adjoint-state method is used to derive the gradients for the source image, source function and velocity updates. By applying a simultaneous inversion of the source image, the source time function and the velocity model, the proposed method produce accurate estimation for these three variables, as demonstrated by a synthetic 3D example corresponding to the SEG Overthrust model used in this study.
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Application of Convolutional Neural Network in Automated Swell Noise Attenuation
Authors B. Farmani and M.W. PedersenSummaryNoise attenuation is a crucial and recurrent step in the seismic processing sequence. After noise attenuation, quality control (QC) is a mandatory process to ensure that the level of noise left in the data is acceptable and no signal leakage has occurred. This process is usually done by geophysicist and is time consuming and subjective. We train a U-Net convolutional neural network model to automatically perform the QC after swell noise attenuation and label the seismic samples as signal, noise or signal leakage. We show that the classification of the acquired seismic data after the swell noise attenuation with the trained model is very reliable and robust and model is able to detect both residual noise and signal leakage. We also propose a framework to use the classification result to steer the denoise process in an automated fashion. If the model detects residual noise or signal leakage during the denoise process, the selected parameters are automatically tuned to produce the best possible result for each seismic record. We demonstrate that the automated denoise process outperforms the fixed parameters denoise process.
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Using Forward Modelling to Guide Exploration Offshore Nigeria
Authors R. Campbell, M. Branston, E. Saragoussi, E. Oraghalum and I. IfeonuSummaryThe Kalaekule oil field presents many of the typical challenges faced by seismic exploration in the shallow-water blocks offshore Nigeria. The presence of shallow water reduces the ability to record reflections from the seabed and the near surface. Additionally, there are shallow gas bodies and faults affecting the amplitudes that limit our understanding of the geology and the reservoir. Ultimately, the combination of these factors increases uncertainty and the risks for the operators. In this case study, we discuss the steps taken as part of a solution design and modelling project to plan a seismic strategy that will address those challenges in the Kalaekule oil field. After understanding the challenges specific to the field, we evaluated, using seismic forward modelling, how their impacts can be reduced through an optimized data acquisition strategy combined with a tailored processing sequence. Finally, we also considered the uplift that may be achieved through reprocessing the existing legacy data.
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Evaluation of Neural Network Architectures for First Break Picking
Authors P. Zwartjes, M. Fernhout and J. YooSummaryWe have implemented a deep learning based first break picker and trained it on various land seismic datasets and evaluated a number of neural network architectures. A deep network with U-net architecture, pre-trained on coarse scale input data provided the most accurate results. Because we use the full shot gather at various scales, the impact of noisy traces is reduced. The neural network corrects random mispicks. This suggest a practical application, namely to train, or re-train a pre-trained network via transfer learning, on a single dataset after conventional FB picking.
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Full Wavefield Modeling with Vector Reflectivity
Authors N.D. Whitmore, J. Ramos-Martinez, Y. Yang and A. ValencianoSummaryThis work describes a method for computing the full acoustic seismic wavefield using a new two-way equation parameterized by vector reflectivity and velocity. This method is contrasted with full wavefield modeling using variable density and demonstrates the equivalence of the two methods. Thus, if an estimate of reflectivity is known or estimated the full acoustic seismic wavefield can be generated from velocity and reflectivity without explicit knowledge of density. This has an impact in any seismic inversion procedure such as Full Waveform Inversion. A modeling example is shown demonstrating the equivalence of the two methods for a known earth model. Wavefield snapshots and seismograms for both methods are shown including the cases of the following: (1) total vector reflectivity, (2) the vertical and horizontal components of reflectivity separately and (3) variable density. A second example compares recorded field data to synthetic seismograms obtained with the proposed approach, where the estimated reflectivity was extracted from a seismic image. It is noted that data misfits between the real and modeled data could be used in velocity and reflectivity inversion.
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A New Attribute of Identifying Gas Hydrate in Marine Sediments
More LessSummaryIt has always been the focus of researchers to accurately identify gas hydrate location. Geophysical prospecting is a widely used method for gas hydrate exploration, which has high credibility, especially seismic exploration technology is most generally used. In our study, we analyze the different physical properties of gas hydrate and other minerals bearing in unconsolidated and high porosity marine sediments based on the effective medium theory. Thus, a new attribute is put forward to discriminate gas hydrate. The logging data at Dongsha area of South China Sea and Hydrate Ridge in Oregon continental margin are applied to validate this method. Our test results are basically in line with actual situation, which provides a new understanding in hydrate identification.
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Tomographic Q Inversion Based on the Adjoint-State Method
More LessSummaryQ tomography has been developed for estimating attenuation model for several years but is generally ray-based. It needs to compute the Fréchet derivatives in each iteration, which would lead to large computation time when input parameters are increasing. In this paper we propose a new gradient-based method using the adjoint-state technique to estimate the distribution of near surface attenuation without the need for introducing Fréchet derivatives. The advantage of this method is that it depends only on the size of velocity and attenuation models, not the amount of input parameters. We describe the details of our workflow with numerical examples and demonstrate how our method can accurately estimate a Q model.
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A Robust Coherence Calculation Method Based on Cross-Correlation in Orthogonal Directions
More LessSummaryWe propose a coherence calculation method based on cross-correlation in orthogonal directions. We first calculate a predicted seismic trace at the location of center trace in each direction using the inverse distance weighting interpolation algorithm, and based on the fact that the biggest difference shall be the difference between the predicted trace calculated in the direction parallel to the structural trend and that calculated in the direction perpendicular to the structural trend, and the corresponding cross-correlation value of the two traces shall be the smallest, we cross-correlate the predicted seismic traces in orthogonal directions and choose the minimum cross-correlation value as the final coherence attribute. Since every trace is weighted according to its relative distance from the centre trace during the prediction of centre trace, the final coherence result is mainly determined more by the most nearby traces of the centre trace than the distant seismic traces. Therefore, the positioning of structural boundaries are more accurate than other coherence methods. We demonstrate that structures detected by the proposed method are more accurate and much clearer than those detected by conventional C3 method via synthetic and field data sets. The new method may be a potentially tool for facilitating seismic interpretation.
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Hydrocarbon Generation Kinetics of Low Cretaceous Nantun Source Rock in Peripheral Sags of Hailar Basin, China
By M. XieSummaryIn order to clarify the hydrocarbon generation potential and evolution stage of Low Cretaceous Nantun source rock in Hongqi, Dongming and Yimin sags of Hailar Basin, the hydrocarbon generation kinetics was conducted by using gold tube autoclave. The results showed that the kinetic parameters of gaseous hydrocarbons were different, and the main frequency activation energy increased orderly from Dongming to Hongqi and Yimin sags. Among the kinetic parameters of liquid hydrocarbon, the average and main frequency activation energy in Hongqi were the lowest, the distribution of activation energy in Yimin showed bimodal characteristic, the main frequency activation energy increased orderly from Hongqi to Yimin and Dongming sags. The hydrocarbon generation history recovery indicated that the K1n1 source rock entered the oil generation threshold in early Cretaceous, and it is still in the early stage of low to mature stage. The oil conversion rate was 12.67%∼39.50%, only a small amount of hydrocarbon expulsion occurred. The key factor restricting oil-generating is that organic matter hasn’t reached the peak of hydrocarbon generation. The focus on petroleum exploration is to find underlying Tongbomiao and Tamulangou Fm with high paleogeotherm and strong oil generation potential or local mature areas of source rocks of Nantun Fm.
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Prediction of Source Rock Maturity Using Semi Supervised Machine Learning Algorithms
Authors S. AlSinan, P. Nivlet, Y. Altowairqi and I. Leyva PovedaSummaryThe paper present a semi-supervised machine learning workflow that integrates geochemical measurements, elastic logs, pre-stack seismic inversion parameters and non-seismic measurements to classify source rock maturity, and propagate the classes away from the wells in a controlled manner. Semi-supervised algorithms are able to discover spatial structures in high dimensional space by using the unlabeled data. This type of learning algorithms are useful in situations where data labels are limited. The algorithm spreads labels by constructing a similarity graph over the input items and minimizing a loss function with regularization properties making it robust to noise. Data analysis indicate that maturity is not only an attribute of the rock but it is also an attribute of the intrinsic properties of the location. Using location indicators, the algorithm was able to create a regional distribution of maturity.
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