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EAGE 2020 Annual Conference & Exhibition Online
- Conference date: December 8-11, 2020
- Location: Online
- Published: 08 December 2020
1 - 50 of 368 results
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Waterflood Analysis in Damaged Formations Using a Multi-Objective Capacitance Resistance Model
Authors M. Salehian, R. Soleimani, S. Norouzi and M. Vasheghani FarahaniSummaryThe Integrated Capacitance Resistive Model (ICRM), a linearized form of Capacitance Resistive Models (CRM), has been commonly used to match liquid production history and estimate interwell connectivity (IWC) in waterflooded reservoirs. Although this model fits cumulative production data accurately, it usually fails to estimate correct values of total production, where backward subtraction of cumulative production delivers highly overestimated or underestimated total production rates. To address this issue, a multi-objective optimization approach is employed to minimize the error between both cumulative and total production data through two consecutive constrained objective functions. This paper validates the modified ICRM in a homogeneous synthetic reservoir to show how the new approach can successfully characterize the waterflooded reservoirs and forecast future production performance. The proposed data-driven approach has been tested on damaged formations to investigate the impact of skin factor, as a key component of formation damage, on the dynamic communication between wells. A correlation is proposed to explain the mathematical and physical relationship between formation damage and interwell connectivity of production wells.
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The Influence of Source Wavelet Estimation Error in Acoustic Time Domain Full Waveform Inversion
Authors P. Pavlopoulou and I. JonesSummaryAlmost all forms of full waveform inversion use a source wavelet estimate: this wavelet can be extracted from recorded data during the pre-processing or sometimes a zero-phase band-limited synthetic wavelet can be used instead. Alternatively, the source information can be regarded as another unknown in the inverse problem and be updated within the inversion procedure itself.
The importance of reliable source wavelet information during the waveform inversion implementation has been widely implied or briefly mentioned by multiple authors, but little detailed study of the effects of poor wavelets has been presented in the literature.
It is the purpose of this study to examine if shape errors in the source wavelet manifest themselves in a significantly damaging way in the velocity model obtained using conventional time-domain acoustic waveform inversion, and if so by what magnitude.
In addition, although it is clear that a suboptimal source wavelet will degrade the velocity model, we will also assess what effect such degradation has on migrated image positions, and compare this depth error to the inherent uncertainty expected in seismic images. In other words, we will try to assess how much wavelet error is acceptable before the image is distorted beyond typically accepted positioning errors.
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Waveform Inversion Methodology for Deep Structural Imaging Offshore Norway
Authors J. Singh, L. Braidwood, V. Valler, O. Michot, C. Wang, I. Jones and R. BekkeheienSummaryHere we present an example of a refraction and reflection FWI case study, from offshore Norway, using various minimization norms, in order to obtain reliable and robust parameter models to address deep imaging challenges associated with sill intrusions and hydrothermal vents. Significant improvement is obtained using a flow involving five variants of FWI.
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Plane-Wave Least-Square Reverse Time Migration with Seislet Fractional Order Threshold Algorithm Constraint
More LessSummaryLeast-square reverse time migration using plane-wave encoding has two problems: Encoding data will introduce crosstalk noise and the excessive number of plane-wave records will reduce the computational efficiency. In this paper, the Seislet transform, which is suitable for seismic data, is combined with the fractional order threshold function based on the Riemann-Liouville fractional integration theory. Then we apply it into the plane-wave least-square reverse time migration. Numerical tests on the complex model show that the plane-wave least-square reverse time migration based on Seislet fractional order threshold algorithm constraint can effectively suppress the crosstalk noise caused by multi-source data. Compared with the traditional method, this proposed method uses less number of plane-wave records to obtain the same imaging effect, and can improve the computational efficiency.
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On the Error Behaviour of Force and Moment Sources in Tetrahedral Spectral Finite Elements
By W.A. MulderSummaryThe representation of a force or of a moment point source in a spectral finite-element code for modelling elastic wave propagation becomes fundamentally different in degenerate cases where the source is located on the boundary of an element. This difference is related to the fact that the finite-element basis functions are continuous across element boundaries, but their derivatives are not. A method is presented that effectively deals with this problem. Tests on 1-D elements show that the numerical errors for a force source follow the expected convergence rate in terms of the element size, apart from isolated cases where superconvergence occurs. For a moment source, the method also converges but one order of accuracy is lost, probably because of the reduced regularity of the problem. Numerical tests in 3D on continuous mass-lumped tetrahedral elements show a similar error behaviour as in the 1-D case, although in 3D, the loss of accuracy for the moment source is not a severe as a full order.
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Ultra-High Resolution 3D of Shales with Nano-CT and Its Control on Gas Transport
Authors M. Garum, P. Lorinczi, P.W.J. Glover and A. HassanpourSummaryThe aim of this study is to better understanding the microstructural and fluid transport properties of gas shale on small size of sample. We use our data to demonstrate of imaging datasets over nano-scales, the integration of 3D technique to identify microstructures properties and the behavior of gas shale which is necessary for modelling elastic behavior, gas storage, gas desorption and gas flow in gas shales. The 3D volumes of shale showed significant in nano-structure (pore size and volume distribution, mineral content, porosity, pore aspect ratios and surface area to volume ratios).
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A Logical Error in Gassmann Poro-Elasticity
By L. ThomsenSummaryThe well-known Gassmann equation for the fluid dependence of rock compressibility is in error, because of a mistake in its derivation, obvious once stated. The mistake is the application of an open-systems theorem to the hydraulically-closed system ( undrained ) of low-frequency wave propagation. This error (NOT solid micro-heterogeneity) is responsible for the difference between Gassmann's result and the prior result by Biot, or the subsequent, equivalent result by Brown and Korringa. Hence, we should use these instead, for interpreting 4D seismic differences. The additional parameter, a rock property, may be measured by quasi-static compression tests.
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Gas-Condensate PVT Fluid Modeling Methodology Based on Limited Data
Authors O. Burachok, D. Pershyn, C. Spyrou, G. Turkarslan, M.L. Nistor, D. Grytsai, S. Matkivskyi, Y. Bikman and O. KondratSummaryA well-established method for fluid characterization is to use regression on the critical parameters of the grouped components of an equation of state (EOS) to replicate the results of fluid experiments performed in the laboratory, mainly constant composition expansion (CCE), constant volume depletion (CVD), and differential liberation (DL). In the case of many mature reservoirs, however, proper fluid laboratory examination is not available. This paper proposes an alternative fluid characterization methodology based on the Engler distillation test (ASTM86). Its objective is to help engineers derive key fluid parameters such as formation volume factors and oil-gas ratios in the absence or limitation of PVT-cell experimental data, based only on the Engler distillation test (ASTM86) results and a fluid composition up to C5+.
The suggested methodology was applied on multiple highly heterogeneous fields located in the Dnieper-Donetsk Basin in Eastern Ukraine and proved to be useful for all the fields of varying fluid types ranging from lean gas with a condensate yield (presence of C5+ per cubic meter of gas) of 10 g/m3 to very rich retrograde gases of 500 g/m3.
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Reflectivity Impedance Combination (RIC): A Solution to Improve the ODiSI Result
By H. Pham HuuSummaryBP’s one-dimensional stochastic inversion method (ODiSI) has been widely applied to estimate reservoir properties, facies probabilities and associated uncertainties from seismic data. Currently, the input to ODiSI is relative impedance data. Relative impedance data is rich in low frequencies so captures the thicker layers. In contrast, reflectivity data is dominated by high frequencies and consequently can help with imaging thin layers. This paper will discuss the advantages and disadvantages of using reflectivity data as the input to ODiSI and prove that simultaneous inversion of reflectivity and relative impedance data improves the resolution of the ODiSI products.
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An Improved Robust Principle Component Analysis for The Denoising of Desert Seismic Data
More LessSummaryContamination of seismic data by background noise causes difficulties for the following inversion, imaging, stratigraphic interpretation, etc. Desert seismic records pose a particular problem because of the strong energy of desert random noise and its serious spectrum overlapping with effective signals. Thus, the robust principle component analysis (RPCA) is introduced into the denoising of desert seismic data. RPCA is a classical method of low-rank matrix recovery. By kernel norm optimization, it can decompose noisy data into optimal low-rank matrix (LM) and sparse matrix (SM) which contain most effective signals and noise, respectively. However, due to the low signal-to-noise ratio and serious spectrum overlapping of desert seismic record, there are still a lot of random noise in its optimal LM. Therefore, the convolutional neural network (CNN) is combined with RPCA to establish the optimal mapping relationship from noisy LM to desert random noise through the training of CNN, so as to accurately predict desert random noise from the LM of desert seismic record. Finally, the denoising result is obtained by subtracting the desert random noise predicted by CNN from the LM. Experiments show that this improved RPCA can suppress random noise and recover effective signal more effectively than the traditional methods.
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A Sequential Inversion for the Velocity and the Intrinsic Attenuation Using Efficient Wavefield Inversion
Authors C. Song and T. AlkhalifahSummaryFull-waveform inversion (FWI) has become a popular method to retrieve high-resolution subsurface model parameters. An accurate simulation of wave propagation plays an important role in achieving better data fitting. For intrinsically attenuative media, wave propagation experiences dispersion and loss of energy. Thus, it is sometimes crucial to consider the intrinsic attenuation of the Earth in the FWI implementation. Viscoacoustic FWI aims at achieving a joint inversion of the velocity and attenuative models. However, multiparameter FWI imposes additional challenges including expanding the null space problem and the parameter trade-off issue. We use an efficient wavefield inversion (EWI) method to invert for the velocity and the intrinsic attenuation, sequentially. This approach is implemented in the frequency domain, and the velocity, in this case, is complex-valued in the viscoacoustic EWI. The inversion for the velocity and the intrinsic attenuation is handled in separate optimizations. As viscoacoustic EWI is able to recover a good velocity model, the velocity update leakage to the Q model is largely reduced. We show the effectiveness of this approach using synthetic data generated for a viscoacoustic Marmousi model.
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Source-Independent Efficient Wavefield Inversion
Authors C. Song and T. AlkhalifahSummaryThe source function accuracy plays an important role in a successful full-waveform inversion (FWI) application. So we often need to estimate the source function before or within the inversion process. Source estimation requires additional computational cost, and an inaccurate source estimation can hamper the convergence of FWI. We develop a source-independent waveform inversion utilizing a recently introduced wavefield reconstruction based method we refer to as efficient wavefield inversion (EWI). In EWI, we essentially reconstruct the wavefield by fitting it to the observed data as well as a wave equation based on iterative Born scattering. However, a wrong source wavelet will induce errors in the reconstructed wavefield, which may lead to a divergence of this optimization problem. We use a convolution-based source-independent misfit function to replace the conventional data fitting term in EWI to formulate a source-independent EWI (SIEWI) objective function. In SIEWI, this new formulation is able to mitigate the cycle-skipping issue and the source wavelet uncertainty, simultaneously. We demonstrate those features on the Overthrust model.
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Investigation of Nano-Scale Structures by Using Nano-CT and FIB-SEM Approaches to Characterizing of Gas Shale
Authors M. Garum, P.W.J. Glover, P. Lorinczi and A. HassanpourSummaryGas shale, FIB-SEM, nano-CT, porosity, permeability, Kerogen, pore volume, size distribution.
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A Data-Driven Choice of Misfit Function for FWI Using Reinforcement Learning
Authors B. Sun and T. AlkhalifahSummaryIn the workflow of Full-Waveform Inversion (FWI), we often tune the parameters of the inversion to help us avoid cycle skipping and obtain high resolution models. For example, typically start by using objective functions that avoid cycle skipping, and then later, we utilize the least squares misfit to admit high resolution information. Such hierarchical approaches are common in FWI, and they often depend on our manual intervention based on many factors, and of course, results depend on experience. However, with the large data size often involved in the inversion and the complexity of the process, making optimal choices is difficult even for an experienced practitioner. Thus, as an example, and within the framework of reinforcement learning, we utilize a deep-Q network (DQN) to learn an optimal policy to determine the proper timing to switch between different misfit functions. Specifically, we train the state-action value function (Q) to predict when to use the conventional L2-norm misfit function or the more advanced optimal-transport matching-filter (OTMF) misfit to mitigate the cycle-skipping and obtain high resolution, as well as improve convergence. We use a simple while demonstrative shifted-signal inversion examples to demonstrate the basic principles of the proposed method.
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3D Acoustic Orthorhombic Anisotropic Passive Source Inversion with Full Waveform Inversion
Authors H. Wang and T. AlkhalifahSummaryFull waveform inversion (FWI) based methods are getting more attractive in passive seismic (micro-seismic) monitoring studies. A high resolution knowledge of the medium in which the events occur is crucial to proper monitoring. In most cases, not only the velocity, but also the anisotropy has a large influence on locating passive events related to hydraulic fracturing. Due to the inherent anisotropy nature of most rocks associated with unconventional reservoirs, accounting for anisotropy is even more important in such investigations. We propose a 3D acoustic orthorhombic FWI method for passive seismic events to invert for the source image, source function and the model parameters, without any a prior knowledge about source location or source function in time. In order to mitigate the effect of the unknown source ignition time, we convolve reference traces with the observed and modeled data. The adjoint-state method is used to derive the gradient for the source image, source function and the anisotropic model parameters. The proposed method produces good estimates of the source location and the model structures for the orthorhombic 3D SEG/EAGE overthrust model.
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Impedance Inversion Based on Structure-Oriented Regularization
More LessSummaryRegularization methods are widely used in impedance inversion problems to solve the ill-posed problem. At present, some common regularization methods are applied to the poststack seismic inversion problems by imposing isotropic smoothness on impedance. However, isotropic smoothness can blur small-scale geologic features and reduce the resolution of inversion results. To overcome this shortcoming, we have developed a new regularization that imposes smoothness along the orientations of geological structures. Such a structure-oriented regularization is often isotropic in most areas but can be anisotropic in areas where the structural features are anisotropic. Therefore, our method can preserve small-scale structural features and increase the accuracy and lateral continuity of inversion results. The inversion results of synthetic seismic data demonstrate that the proposed method can effectively improve the resolution and accuracy of the inversion results.
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Type-Curves for Herschel-Bulkley Fluid Model Resembling Lost Circulation in a Fractured Formation
Authors R. Albattat and H. HoteitSummaryLost circulation is one of the major problems facing oil and gas industry plus other industries. It is defined as a partial or a total escape of the well-hole fluid, either drilling fluid, workover fluid or cementing fluid, into the surrounding formations. The loss fluid can hinder drilling operations, augment nonproductive time, increase the difficulty of managing the circulation fluid and add-up to the overall cost. Therefore, the demand from the industry to have a quick accessible solution on-site is of the essence during the fluid loss phenomenon. In this work, an analytical approach is developed to model the mechanisms of Non-Newtonian fluid for drilling fluid following Herschel-Bulkley model. A derivation of the solution is originated from Cauchy equation of motion to represent a Non-Newtonian fluid flow into a single horizontal fracture. Moreover, simulator is utilized to solve the same mathematical problem on purpose of verifying the results between analytical and numerical solutions. An improvement of the analytical solution is made comparing with latest existing solution in literature pertaining to type-curves of mud loss. Due to limitations on the industrial on-site during drilling operation, type-curves is generated to describe the mud loss volume or front per unit of time.
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Identifying Microseismic Events in Time-Reversed Source Images Using Support Vector Machine
Authors C. Song and T. AlkhalifahSummaryLocating microseismic events is an important procedure in oil and gas extraction. Time-reversal based methods generate source images, which can be used to locate the microseismic events with the help of proper source imaging conditions. However, such images are often contaminated with artifacts, including imaging artifacts, which hamper the proper identification and location of such events. We use the support vector machine (SVM) to develop a classification algorithm of microseismic events within a source image. Using certain features of the source image as input, this trained SVM system is able to distinguish whether each grid point in the source image corresponds to a proper event or an artifact. Applications on a homogeneous model and a Marmousi model show the effectiveness of the proposed method.
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Tuned Inflow Performance Relationship (IPR) For One of the Iranian Gas Solution Oil Field
Authors H. Asaadian and M.K. BeyranvandSummaryInflow performance relationships, IPR, are measureable predictions of a reservoir. Researcher and engineers use this relationship for obtaining optimum production and some production operations like artificial lift, stimulation operations. Many researches have been studied in vertical, horizontal and deviated wells to calculating the IPR. Also many works have been done for estimating the IPR for oil and gas reservoir with various rock and fluid properties.
The goal of this work is to obtain a general Vogel type correlation for an oil reservoir in vertical wells. The data which is used in this paper is resulted from a multi rate well testing for heavy oil reservoir in one of the Iranian south reservoir. This test was done on 12 wells on the oil reservoir which all of these wells have heavy oil production. This general correlation that was obtained from Vogel equation, has new coefficients for this case. It must be noted it is easy to apply this relationship for an oil reservoir, because it only needs common known parameters of the field.
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Optimized Acoustic Approximation and Simulation of P-Wave in Transversely Isotropic Media
More LessSummaryThe standard acoustic approximation in transversely isotropic (TI) media results in an approximate P-wave phase velocity expression with a complicated square root term. Thus, the corresponding wavefields suffer from the unstable SV-wave artifacts. Many subsequent pure P-wave simulation methods were proposed to eliminate the SV-wave artifacts at the expense of decreasing the accuracy or increasing the computing cost. In this abstract, we propose an optimized acoustic approximation for P-wave in TI media to overcome the defect of the standard acoustic approximation. Since the P-wave propagation is weakly dependent on the vertical S-wave velocity, we construct a function of the vertical S-wave velocity squared to approximate the P-wave phase velocity. The corresponding expression is quite concise, without square roots and complicated fractions. Then, we derive a pure P-wave equation in tilted transversely isotropic (TTI) media. We compare and analyze the wavefields of several simulation methods in the homogeneous and complex TTI models. The wavefields of the proposed method only involve the P-wave propagation with high accuracy. And the computing cost is acceptable. So, this optimized acoustic approximation and the corresponding pure P-wave simulation method can be further used for the reverse time migration and full waveform inversion in TI media.
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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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A P-Cable Time-Lapse Seismic Repeatability Study in the Gulf of Mexico
More LessSummaryThe term “P-Cable” refers to a high-resolution marine streamer acquisition system that uses short densely-spaced streamers to provide 3D seismic data with higher temporal and spatial resolution than conventional marine streamer acquisition. It is a containerized system that can be deployed at short notice and relatively low cost, making it attractive for time-lapse seismic surveying. Its small dimensions enable accurate repetition of source and receiver locations and provide greater flexibility and safety in obstructed areas. Two pairs of time-lapse (4D) repeatability test lines were acquired in the Gulf of Mexico in 2014, and these were processed in 2019. The data presented many challenges including strong cable noise and variable streamer depths. Nevertheless the results exhibited low NRMS difference values and low residual energy on 4D difference seismic sections. This demonstrates that the acquisition system can provide high-quality time-lapse seismic data in areas where the reservoir can be adequately imaged by short-offset, low-fold, small-source seismic data. Recent upgrades to the system mitigate many of the problems encountered and should provide further improved time-lapse seismic data. Several hydrocarbon fields are currently monitored using this method.
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Influence of Label Conditions on the Effect of Deep Learning Inversion
More LessSummaryLabel and algorithm are two main factors that influence the effect of deep learning inversion (DLI). Most present researches focus on optimizing algorithms, and pay less attention to how the labels affect the inversion effect, which results in that the application effects of the same algorithm varying with the application regions. This article highlights the importance of label conditions on the effect of DLI. The comparison of inversion results performed with 4 different label sets indicates an ideal DL inversion requires a large number of high-quality and large-diversity labels.1) Label quality is the most important factor, for it directly determines the correctness of inversion results. 2) The increase in both quantity and diversity of the labels is very effective for improving the inversion result, and the diversity is relatively more critical. 3) The better the label structure matches the geological pattern, the better the inversion results are. The research demonstrates that forward modeling based on well interpolation model is an effective method for label augmentation. In order to take full advantage of deep learning, we should integrate it with classic geophysical methods and make them complement each other.
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Fast Elastic Wavefield Reconstruction in a Local Region by Modifying Any Modelling Code
Authors L.E. Jaimes-Osorio, A. Malcolm and P. ZheglovaSummaryThe finite-difference (FD) method is a key tool in geophysics, where it is used to model seismic wave propagations. Often, the region of interest is reduced to small areas. Thus, many methods have been developed to manipulate the source wavefield efficiently to reconstruct the synthetic wavefield locally. However, there are few implementations in the elastic domain, where it is specifically shown how the injection and reconstruction of wavefields should be done. In this study, we show the implementation of multiple point sources method to reconstruct elastic wavefields inside an elastic local domain using a finite difference method. We demonstrate the capability of the elastic wavefield reconstruction using the SEAM East-West 2D elastic model.
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1D Laplace-Fourier Acoustic FWI for Near-Surface Characterization and Initial Velocity Model Building
Authors A. Kontakis, D. Rovetta, D. Colombo, E. Sandoval-Curiel, P.V. Petrov and G.A. NewmanSummaryAccurate characterization of the near-surface velocity model is often a prerequisite for effective migration. Refraction tomography may fail to produce a satisfactory velocity model in the presence of velocity inversions and requires accurate traveltime picking. Full waveform inversion (FWI) can overcome these issues, but often requires a good initial model or the presence of sub-5Hz frequencies in the recorded data, and its 3D implementations can be computationally costly. To address these challenges, we propose a 1D version of Laplace-Fourier acoustic FWI, building on the relative insensitivity of Laplace-Fourier methods to the quality of the initial model. The proposed method approximates locally the 3D medium by an effective 1.5D medium and inverts independently for local 1D velocity profiles, that can be upscaled to a full 3D velocity volume. The independence of the 1D inversions and the cylindrical symmetry exhibited by 1.5D forward modelling can be taken advantage of to produce an efficient, highly parallelizable implementation. The feasibility of the method is studied using a synthetic example, with encouraging results.
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Fully Automatic Picking of Surface Wave Dispersion Curves through Density-Based Spatial Clustering
Authors D. Rovetta, A. Kontakis and D. ColomboSummaryRayleigh surface wave inversion can be used to characterize the near surface, which is a major task in desert environments due to the high complexity of the shallow geology. The inversion results depend on the accuracy of the dispersion curves extracted from the seismic measurements. This extraction is commonly obtained through manual picking which is time consuming, highly subjective and not feasible for modern large seismic surveys. In this work we introduce a novel and fully automatic method built on a density-based spatial clustering algorithm to pick surface wave dispersion curves in the frequency-phase velocity spectrum of the seismic gathers. The method was successfully tested on the SEAM Arid model synthetic dataset. The dispersion curves, extracted automatically using the proposed approach, accurately match the theoretical ones and produce results in very good agreement with the ground truth when inverted for the shear-wave velocity distribution. The presented method is currently under test with field data.
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Research and Application of Prediction Method for Sweet Spots of Shale Gas Using Geophysical Data
Authors W. Xiujiao, C. Sheng, H. Pei, W. Nai, Y. Yadi, D. Chunmeng, H. Zijiao, W. Xing and L. XuanSummaryAs one of the most important procedures in shale gas exploration and development, sweet spots prediction is the preferred method for acquiring and maintaining high productivity and effective production of shale gas.
In this study, firstly, based on well log interpretation and rock physics analysis, the quantitative relationships between the elastic parameters and the evaluation parameters such as density and TOC, were established. Then, the planar distribution of some key reservoir parameters including TOC, high-quality reservoir thickness, brittleness and formation pressure were predicted through prestack inversion of all gathers. Finally, with the fuzzy comprehensive evaluation method, an evaluation scheme was proposed and the spatial distribution of sweet spots in the block was assessed. Based on this scheme and the regional distribution of sweet spots predicted by the seismic data, the Z block was divided into three classes. Class I and Class II as the sweet spots are mainly distributed in the east of the block, which are suggested to give priority to the development. This study can provide important guidance for the factory development of the block.
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The Effective Elastic Properties for Transversely Isotropic Rocks with Randomly Orienting Inclined Penny-Shaped Cracks
More LessSummaryCracks have significant influences on the elastic properties of reservoir rocks, and the effects of crack properties (e.g., crack density, crack aspect ratio and crack orientation) on the elastic properties of rocks have always been the focus of petrophysics and seismic exploration. However, key to the accurate characterization of fractured reservoir is the development of rock physics models that better simulate real fractured reservoirs. Current models are not applicable to rocks with randomly orienting inclined cracks even though such conditions are frequently encountered in the Earth. We derive the theoretical models and simulate the elastic properties of fractured rocks with transversely isotropic background permeated by 3D inclined cracks and randomly orienting cracks, to demonstrate how the elastic properties of fractured rocks are affected by the properties of randomly orienting inclined cracks. The observed petrophysical models and the correlated crack properties and elastic properties have both theoretical and practical implications for insights into the effects of randomly orienting cracks on the fractured rocks and for inverting the crack properties of the fractured reservoirs. This will pave the way for the successful prediction of the elastic properties for rocks with TI background permeated by randomly orienting cracks.
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Velocity Model Building Using Elastic Waveform Inversion on Multi-Component OBN Data in the Gulf of Mexico
Authors C. Perez Solano, R. Plessix, K. Bao, C. Perkins and M. KiehnSummaryIn presence of large elastic parameter variations, acoustic waveform inversion creates artefacts because the approach overfits the data with an inaccurate physical model. An elastic formulation is required to correctly account for the finite-frequency effects and notably the scattering that occurs inside the first Fresnel zone. Over the years, nodal acquisition has become more popular. In the waveform inversion context, it provides low-frequency, long-offset, and full-azimuth data that are very relevant to recover the low-to-mid wavenumber information of the earth parameters. Moreover, with offshore nodal acquisition, not only the pressure field is recorded but also the three particle-displacement or velocity fields. The noise does not have the same effects on the different recording component due to its directionality. Using a nodal acquisition for the Gulf of Mexico, we present the elastic inversion results obtained with the hydrophone and vertical geophone components. We discuss the relevance of considering the vertical geophone component in velocity model building.
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Assessment and Application of Present-Day In-Situ Stress Field Within Deeply Buried Tight Reservoir of Tarim Basin
More LessSummaryConstructing a suitable method to better understand the present-day in-situ stress field within deeply buried tight sandstone reservoir under complex geological conditions is extremely important in the Tarim Basin. In this study, one-dimensional (1D) geomechanical modeling and three-dimensional (3D) heterogeneous stress field simulation were carried out, and the well trajectory optimization were analyzed. Taking the KS 10 gas reservoir of KS gas field in the Kelasu structural belt as an example, the results show that the in-situ stress magnitudes within deeply buried tight sandstone reservoir in the Kelasu structural belt are generally high. The relationship of horizontal maximum (SH), minimum (Sh) principal stress, and vertical stress (SV) is SH>SV>Sh, showing a dominant the strike-slip faulting stress regime. The direction of the SH is generally N-S-trending. The research on the heterogeneous rock mechanical parameters is expected to improve the accuracy of present-day in-situ stress prediction in deeply buried tight reservoirs. The result of present-day in-situ stresses provide a reasonable reference for well trajectory optimization to reduce complex accidents and avoid potential engineering risks, helping increase the oil and gas production and improve the drilling speed.
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Automatic Fracture-Vug Extraction from Imaging Logging Based on Incomplete Path Opening Operation and Cluster of Sinusoid
More LessSummaryIn this study we provide a systematic workflow of fracture-vug extraction and reconstruction for electric well logging image preprocessing for the purpose of accurate quantitative assessment of fractures and vugs reservoirs. It includes first step of identification of fractures and their edges by incomplete path morphologic scheme, and the second step of pattern recognition of the electric well logging images through a correlation with a family of sinusoidal functions prepared for the picking up of fracture parameters for high dip fractures and the Hough transformation for low dip fractures. Then the statistical step has been applied to parameters extraction for the reservoir description. With the suitable combination of parameters can we derive complete fractures and vugs that may have admissible missing pixels by the incomplete path opening operation. The cluster of sinusoid can make up for the deficiency of Hough transform in the extraction of high dip angle fractures and incomplete fractures, and overcome the weak robustness of Hough transform to noise.
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