scholarly journals Use of Seismic Spectral Decomposition, Phase, and Relative Geologic Age as Attributes to Improve Quantitative Porosity Prediction in the Daqing Field, China

2021 ◽  
Vol 11 (17) ◽  
pp. 8034
Author(s):  
David Mora Calderon ◽  
John P. Castagna ◽  
Ramses Meza ◽  
Shumin Chen ◽  
Renqi Jiang
Keyword(s):  
Seismic Data ◽  
Spectral Decomposition ◽  
Reservoir Characterization ◽  
Effective Porosity ◽  
Thin Layers ◽  
Rock Properties ◽  
Seismic Amplitude ◽  
Attribute Analysis ◽  
Stratigraphic Position ◽  
Phase Amplitude

The high production potential of the Daqing oilfield in China is recognized for seismically thin sand bodies that usually are not resolved with conventional seismic data. The present study assesses the usefulness of applying seismic multi-attribute analysis to bandwidth extended data in resolving and making inferences about these thin layers. In thin layers, tuning can obscure relationships between seismic amplitude and rock properties. In such cases, the seismic phase varies with the layer impedance and may hence aid in reservoir characterization. A seismically derived relative geologic age may also be a useful attribute in predicting rock properties because it helps define the stratigraphic position of a layer. When utilized in multi-attribute analysis in the Daqing field, spectral decomposition amplitude, phase, and a relative geological age attribute to improved prediction of well log effective porosity from seismic data and are preferentially selected by stepwise regression. The study follows standard methodology by implementing seismic multi-attribute analysis and discusses the improvement of applying it to bandwidth extended data. This will include a combination of attributes such as relative geologic age, phase, amplitude, and the magnitude components of spectrally decomposed data.

Case study: Seismic resolution and reservoir characterization of thin sands using multiattribute analysis and bandwidth extension in the Daqing field, China

2020 ◽  
Vol 8 (1) ◽  
pp. T89-T102
Author(s):  
David Mora ◽  
John Castagna ◽  
Ramses Meza ◽  
Shumin Chen ◽  
Renqi Jiang
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Statistical Significance ◽  
Physical Property ◽  
Seismic Attributes ◽  
Oil Field ◽  
Thin Layers ◽  
Inversion Method ◽  
Bandwidth Extension ◽  
Multiattribute Analysis

The Daqing field, located in the Songliao Basin in northeastern China, is the largest oil field in China. Most production in the Daqing field comes from seismically thin sand bodies with thicknesses between 1 and 15 m. Thus, it is not usually possible to resolve Daqing reservoirs using only conventional seismic data. We have evaluated the effectiveness of seismic multiattribute analysis of bandwidth extended data in resolving and making inferences about these thin layers. Multiattribute analysis uses statistical methods or neural networks to find relationships between well data and seismic attributes to predict some physical property of the earth. This multiattribute analysis was applied separately to conventional seismic data and seismic data that were spectrally broadened using sparse-layer inversion because this inversion method usually increases the vertical resolution of the seismic. Porosity volumes were generated using target porosity logs and conventional seismic attributes, and isofrequency volumes were obtained by spectral decomposition. The resulting resolution, statistical significance, and accuracy in the determination of layer properties were higher for the predictions made using the spectrally broadened volume.

Robust vector median filtering with a structure-adaptive implementation

Geophysics ◽  
2020 ◽  
Vol 85 (5) ◽  
pp. V407-V414
Author(s):  
Yanghua Wang ◽  
Xiwu Liu ◽  
Fengxia Gao ◽  
Ying Rao
Keyword(s):  
Seismic Data ◽  
Impulse Noise ◽  
Reservoir Characterization ◽  
Median Filter ◽  
Original Data ◽  
Thin Layers ◽  
3D Seismic ◽  
Data Set ◽  
Vector Median ◽  
3D Seismic Data

The 3D seismic data in the prestack domain are contaminated by impulse noise. We have adopted a robust vector median filter (VMF) for attenuating the impulse noise from 3D seismic data cubes. The proposed filter has two attractive features. First, it is robust; the vector median that is the output of the filter not only has a minimum distance to all input data vectors, but it also has a high similarity to the original data vector. Second, it is structure adaptive; the filter is implemented following the local structure of coherent seismic events. The application of the robust and structure-adaptive VMF is demonstrated using an example data set acquired from an area with strong sedimentary rhythmites composed of steep-dipping thin layers. This robust filter significantly improves the signal-to-noise ratio of seismic data while preserving any discontinuity of reflections and maintaining the fidelity of amplitudes, which will facilitate the reservoir characterization that follows.

Impact of phase rotation on reservoir characterization and implementation of seismic well tie technique for calibration offshore Nile Delta, Egypt

2020 ◽  
Vol 39 (5) ◽  
pp. 346-352
Author(s):  
Mohamed G. El-Behiry ◽  
Mohamed S. Al Araby ◽  
Ramy Z. Ragab
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Nile Delta ◽  
Seismic Wavelet ◽  
Well Location ◽  
Formation Behavior ◽  
Dynamic Components ◽  
Well Data ◽  
Phase Amplitude ◽  
Seismic Anomalies

Seismic wavelets are dynamic components that result in a seismic trace when convolved with reflectivity series. The seismic wavelet is described by three components: amplitude, frequency, and phase. Amplitude and frequency are considered static because they mainly affect the appearance of a seismic event. Phase can have a large effect on seismic appearance by changing the way it describes the subsurface. Knowing the wavelet properties of certain seismic data facilitates the process of interpretation by providing an understanding of the appearance of regional geologic markers and hydrocarbon-bearing formation behavior. The process through which seismic data wavelets are understood is called seismic well tie. Seismic well tie is the first step in calibrating seismic data in terms of polarity and phase. It ensures that the seismic data are descriptive to regional markers, well markers, and discoveries (if they exist). The step connects well data to seismic data to ensure that the seismic correctly describes well results at the well location. It then extends the understanding of seismic behavior to the rest of the area covered by the seismic data. Good seismic well tie will greatly reduce uncertainties accompanying seismic interpretation. One important outcome of the seismic well tie process is understanding the phase of seismic data, which affects how seismic data will reflect a known geologic marker or hydrocarbon-bearing zone. This understanding can be useful in quantifying discoveries attached to seismic anomalies and extending knowledge from the well location to the rest of the area covered by seismic data.

Accelerating seismic fault and stratigraphy interpretation with deep CNNs: A case study of the Taranaki Basin, New Zealand

2020 ◽  
Vol 39 (10) ◽  
pp. 727-733
Author(s):  
Haibin Di ◽  
Leigh Truelove ◽  
Cen Li ◽  
Aria Abubakar
Keyword(s):  
New Zealand ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Seismic Survey ◽  
Deep Convolutional Neural Networks ◽  
Seismic Attribute ◽  
Data Set ◽  
Seismic Fault ◽  
Attribute Analysis ◽  
Specific Implementation

Accurate mapping of structural faults and stratigraphic sequences is essential to the success of subsurface interpretation, geologic modeling, reservoir characterization, stress history analysis, and resource recovery estimation. In the past decades, manual interpretation assisted by computational tools — i.e., seismic attribute analysis — has been commonly used to deliver the most reliable seismic interpretation. Because of the dramatic increase in seismic data size, the efficiency of this process is challenged. The process has also become overly time-intensive and subject to bias from seismic interpreters. In this study, we implement deep convolutional neural networks (CNNs) for automating the interpretation of faults and stratigraphies on the Opunake-3D seismic data set over the Taranaki Basin of New Zealand. In general, both the fault and stratigraphy interpretation are formulated as problems of image segmentation, and each workflow integrates two deep CNNs. Their specific implementation varies in the following three aspects. First, the fault detection is binary, whereas the stratigraphy interpretation targets multiple classes depending on the sequences of interest to seismic interpreters. Second, while the fault CNN utilizes only the seismic amplitude for its learning, the stratigraphy CNN additionally utilizes the fault probability to serve as a structural constraint on the near-fault zones. Third and more innovatively, for enhancing the lateral consistency and reducing artifacts of machine prediction, the fault workflow incorporates a component of horizontal fault grouping, while the stratigraphy workflow incorporates a component of feature self-learning of a seismic data set. With seven of 765 inlines and 23 of 2233 crosslines manually annotated, which is only about 1% of the available seismic data, the fault and four sequences are well interpreted throughout the entire seismic survey. The results not only match the seismic images, but more importantly they support the graben structure as documented in the Taranaki Basin.

Implications of the Fresnel-zone texture for seismic amplitude interpretation

Geophysics ◽  
2012 ◽  
Vol 77 (4) ◽  
pp. O35-O44 ◽  
Author(s):  
Dengliang Gao
Keyword(s):  
Spectral Decomposition ◽  
Frequency Tuning ◽  
Fresnel Zone ◽  
Single Point ◽  
Rock Properties ◽  
Reflection Seismic ◽  
Seismic Amplitude ◽  
Convolution Algorithm ◽  
Convolution Model ◽  
Textural Anisotropy

The 3D reflection seismic response is associated with a zone (the Fresnel zone), rather than with a single point used in the idealized 1D convolution model. Unlike a point of incidence, the Fresnel zone is complicated by its textural characters that are defined by the dip and azimuth of microreflectors in the zone. The Fresnel-zone texture makes seismic amplitude interpretation more complicated than previously documented. A conceptual model suggests that seismic amplitude variations with offset (AVO), azimuth (AVAz), and frequency (spectral decomposition) were physically related to textural roughness, textural anisotropy, and textural scale of the Fresnel zone, respectively. Textural roughness is defined by the dip deviation of microreflectors and contributes to the AVO intercept and gradient. Textural anisotropy is defined by the degree of the preferred orientation of the microreflectors and directly affects the AVAz signature. Textural scale is defined by the spacing of the microreflectors and controls the selective frequency tuning in spectral decomposition data. The Fresnel-zone texture gives rise to amplitude variations that can not be accurately modeled by using a 1D reflectivity-wavelet convolution algorithm, and thus poses challenges to the reliability of many previous predictions of rock properties and thickness from amplitude. The AVO, AVAz, and spectral decomposition data should be used to characterize Fresnel-zone texture for predicting depositional facies, deformational fabrics, and hydraulic properties in the subsurface.

Creating detailed subsurface models using predictive image-guided well-log interpolation

2017 ◽  
Vol 5 (3) ◽  
pp. T279-T285 ◽  
Author(s):  
Parvaneh Karimi ◽  
Sergey Fomel ◽  
Rui Zhang
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Structural Information ◽  
Rock Properties ◽  
Well Log ◽  
Initial Model ◽  
The North ◽  
Structural Framework ◽  
Seismic Image ◽  
Seismic Reflectors

Integration of well-log data and seismic data to predict rock properties is an essential but challenging task in reservoir characterization. The standard methods commonly used to create subsurface model do not fully honor the importance of seismic reflectors and detailed structural information in guiding the spatial distribution of rock properties in the presence of complex structures, which can make these methods inaccurate. To overcome initial model accuracy limitations in structurally complex regimes, we have developed a method that uses the seismic image structures to accurately constrain the interpolation of well properties between well locations. A geologically consistent framework provides a more robust initial model that, when inverted with seismic data, delivers a highly detailed yet accurate subsurface model. An application to field data from the North Sea demonstrates the effectiveness of our method, which proves that incorporating the seismic structural framework when interpolating rock properties between wells culminates in the increased accuracy of the final inverted result compared with the standard inversion workflows.

scholarly journals Estimation of reservoir properties from seismic data by smooth neural networks

Geophysics ◽  
2003 ◽  
Vol 68 (6) ◽  
pp. 1969-1983 ◽  
Author(s):  
M. M. Saggaf ◽  
M. Nafi Toksöz ◽  
H. M. Mustafa
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Back Propagation ◽  
Reservoir Rock ◽  
Seismic Survey ◽  
Rock Properties ◽  
Reservoir Properties ◽  
Network Parameters ◽  
Generalization Problem ◽  
Validation Tests

The performance of traditional back‐propagation networks for reservoir characterization in production settings has been inconsistent due to their nonmonotonous generalization, which necessitates extensive tweaking of their parameters in order to achieve satisfactory results and avoid overfitting the data. This makes the accuracy of these networks sensitive to the selection of the network parameters. We present an approach to estimate the reservoir rock properties from seismic data through the use of regularized back propagation networks that have inherent smoothness characteristics. This approach alleviates the nonmonotonous generalization problem associated with traditional networks and helps to avoid overfitting the data. We apply the approach to a 3D seismic survey in the Shedgum area of Ghawar field, Saudi Arabia, to estimate the reservoir porosity distribution of the Arab‐D zone, and we contrast the accuracy of our approach with that of traditional back‐propagation networks through cross‐validation tests. The results of these tests indicate that the accuracy of our approach remains consistent as the network parameters are varied, whereas that of the traditional network deteriorates as soon as deviations from the optimal parameters occur. The approach we present thus leads to more robust estimates of the reservoir properties and requires little or no tweaking of the network parameters to achieve optimal results.

Resolving subtle stratigraphic features using spectral ridges and phase residues

2013 ◽  
Vol 1 (1) ◽  
pp. SA93-SA108 ◽  
Author(s):  
Oswaldo Davogustto ◽  
Marcílio Castro de Matos ◽  
Carlos Cabarcas ◽  
Toan Dao ◽  
Kurt J. Marfurt
Keyword(s):  
Seismic Data ◽  
Thin Layers ◽  
Earth Model ◽  
Well Log ◽  
Anadarko Basin ◽  
Seismic Amplitude ◽  
West Texas ◽  
Amplitude Data ◽  
Using Data ◽  
Extract Information

Seismic interpretation is dependent on the quality and resolution of seismic data. Unfortunately, seismic amplitude data are often insufficient for detailed sequence stratigraphy interpretation. We reviewed a method to derive high-resolution seismic attributes based upon complex continuous wavelet transform pseudodeconvolution (PD) and phase-residue techniques. The PD method is based upon an assumption of a blocky earth model that allowed us to increase the frequency content of seismic data that, for our data, better matched the well log control. The phase-residue technique allowed us to extract information not only from thin layers but also from interference patterns such as unconformities from the seismic amplitude data. Using data from a West Texas carbonate environment, we found out how PD can be used not only to improve the seismic well ties but also to provide sharper sequence terminations. Using data from an Anadarko Basin clastic environment, we discovered how phase residues delineate incised valleys seen on the well logs that are difficult to see on vertical slices through the original seismic amplitude.

scholarly journals Seismic Identification of Unconventional Heterogenous Reservoirs Based on Depositional History—A Case Study of the Polish Carpathian Foredeep

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 6036
Author(s):  
Anna Łaba-Biel ◽  
Anna Kwietniak ◽  
Andrzej Urbaniec
Keyword(s):  
Seismic Data ◽  
Spectral Decomposition ◽  
Sedimentary Basin ◽  
Seismic Stratigraphy ◽  
Seismic Attribute ◽  
Depositional History ◽  
Attribute Analysis ◽  
Carpathian Foredeep ◽  
The Subject

An integrated geological and geophysical approach is presented for the recognition of unconventional targets in the Miocene formations of the Carpathian Foredeep, southern Poland. The subject of the analysis is an unconventional reservoir built of interlayered packets of sandstone, mudstone and claystone, called a “heterogeneous sequence”. This type of sequence acts as both a reservoir and as source rock for hydrocarbons and it consists of layers of insignificant thickness, below the resolution of seismic data. The interpretation of such a sequence has rarely been based on seismic stratigraphy analysis; however, such an approach is proposed here. The subject of interpretation is high-quality seismic data of high resolution that enable detailed depositional analysis. The reconstruction of the depositional history was possible due to the analysis of flattened chronostratigraphic horizons (Wheeler diagram). The identification of depositional positions in a sedimentary basin was the first step for the indication of potential target areas. These areas were also subject to seismic attribute analysis (sweetness) and spectral decomposition. The seismic attribute results positively verified the previously proposed prospects. The results obtained demonstrate that the interpretation of the Miocene sediments in the Carpathian Foredeep should take into account the depositional history reconstruction and paleogeographical analysis.

Seismic-petrophysical reservoir characterization in the northern part of the Chicontepec Basin, Mexico

2016 ◽  
Vol 4 (3) ◽  
pp. T403-T417 ◽  
Author(s):  
Supratik Sarkar ◽  
Sumit Verma ◽  
Kurt J. Marfurt
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Seismic Survey ◽  
Rock Properties ◽  
Well Log ◽  
Third Order ◽  
East Central ◽  
Well Control ◽  
Seismic Geomorphology ◽  
Tight Reservoirs

The Chicontepec Formation in east-central Mexico is comprised of complex unconventional reservoirs consisting of low-permeability disconnected turbidite reservoir facies. Hydraulic fracturing increases permeability and joins these otherwise tight reservoirs. We use a recently acquired 3D seismic survey and well control to divide the Chicontepec reservoir interval in the northern part of the basin into five stratigraphic units, equivalent to global third-order seismic sequences. By combining well-log and core information with principles of seismic geomorphology, we are able to map deepwater facies within these stratigraphic units that resulted from the complex interaction of flows from different directions. Correlating these stratigraphic units to producing and nonproducing wells provides the link between rock properties and Chicontepec reservoirs that could be delineated from surface seismic data. The final product is a prestack inversion-driven map of stacked pay that correlates to currently producing wells and indicates potential untapped targets.

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