Visualisation of geomorphological features and interpretation of the depositional system of the Brewster Member, Ichthys Field

2017 ◽  
Vol 57 (1) ◽  
pp. 288
Author(s):  
Syed Iftikhar Arsalan ◽  
Keiji Ichizawa ◽  
Keiichi Furuya
Keyword(s):  
Seismic Data ◽  
Morphological Features ◽  
Reservoir Properties ◽  
Seismic Attribute ◽  
Seismic Amplitude ◽  
Channel Belt ◽  
Depositional System ◽  
Thick Overburden ◽  
Depositional Architecture ◽  
Depositional Elements

The Brewster Member of the Early Cretaceous Upper Vulcan Formation is one of the main reservoirs of the Ichthys Field. The Brewster reservoir is characterised as massive sandstone, 150–200 m thick, deposited in a deep marine environment. It has a high net sand-to-gross ratio (over 90%) throughout the field, but heterogeneous reservoir quality between the wells. Visualisation of various geo-morphological features from seismic data is critical for the better understanding of the reservoir. Due to thick overburden, frequency content of the seismic volume is limited at reservoir level. Since the reservoir primarily consists of high net sand-to-gross sandstone, and seismic data has limited resolution, it is difficult to identify the various depositional elements using conventional seismic amplitude interpretation. Seismic attribute visualisation techniques were applied to Ichthys 3D seismic and inverted P-impedance data, enabling identification of several geo-morphological features in this otherwise massive sandstone interval (e.g. feeder channel belt, channel-lobe complex, channel-lobe fringe). Core and log scale sedimentological interpretation were tied to the identified feature to understand the depositional architecture of the Brewster Member, which could lead to a better understanding of the distribution of reservoir properties.

A dictionary learning method with atom splitting for seismic footprint suppression

Geophysics ◽  
2021 ◽  
pp. 1-97
Author(s):  
Dawei Liu ◽  
Lei Gao ◽  
Xiaokai Wang ◽  
wenchao Chen
Keyword(s):  
Dictionary Learning ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Original Data ◽  
Morphological Features ◽  
Time Slice ◽  
Learning Method ◽  
Seismic Attribute ◽  
Morphological Component Analysis ◽  
Value Decomposition

Acquisition footprint causes serious interference with seismic attribute analysis, which severely hinders accurate reservoir characterization. Therefore, acquisition footprint suppression has become increasingly important in industry and academia. In this work, we assume that the time slice of 3D post-stack migration seismic data mainly comprises two components, i.e., useful signals and acquisition footprint. Useful signals describe the spatial distributions of geological structures with local piecewise smooth morphological features. However, acquisition footprint often behaves as periodic artifacts in the time-slice domain. In particular, the local morphological features of the acquisition footprint in the marine seismic acquisition appear as stripes. As useful signals and acquisition footprint have different morphological features, we can train an adaptive dictionary and divide the atoms of the dictionary into two sub-dictionaries to reconstruct these two components. We propose an adaptive dictionary learning method for acquisition footprint suppression in the time slice of 3D post-stack migration seismic data. To obtain an adaptive dictionary, we use the K-singular value decomposition algorithm to sparsely represent the patches in the time slice of 3D post-stack migration seismic data. Each atom of the trained dictionary represents certain local morphological features of the time slice. According to the difference in the variation level between the horizontal and vertical directions, the atoms of the trained dictionary are divided into two types. One type significantly represents the local morphological features of the acquisition footprint, whereas the other type represents the local morphological features of useful signals. Then, these two components are reconstructed using morphological component analysis based on different types of atoms, respectively. Synthetic and field data examples indicate that the proposed method can effectively suppress the acquisition footprint with fidelity to the original data.

Delineation of reservoir channels by different seismic attributes and geobody extractions for robust volumetric estimation, Saffron Field, offshore Nile Delta, Egypt

2021 ◽  
Vol 40 (7) ◽  
pp. 484-493
Author(s):  
Doha Monier ◽  
Azza El Rawy ◽  
Abdullah Mahmoud
Keyword(s):  
Seismic Data ◽  
Nile Delta ◽  
Three Dimensional ◽  
Dynamic Test ◽  
Data Interpretation ◽  
Seismic Attributes ◽  
Reservoir Properties ◽  
Root Mean Square Amplitude ◽  
Seismic Attribute ◽  
Attribute Analysis

The Nile Delta Basin is a major gas province. Commercial gas discoveries there have been proven mainly in Pleistocene to Oligocene sediments, and most discoveries are within sandstone reservoirs. Three-dimensional seismic data acquired over the basin have helped greatly in imaging and visualization of stratigraphy and structure, leading to robust understanding of the subsurface. Channel fairways serve as potential reservoir units; hence, mapping channel surfaces and identifying and defining infill lithology is important. Predicting sand distribution and reservoir presence is one of the key tasks as well as one of the key uncertainties in exploration. Integrating state-of-the-art technologies, such as including 3D seismic reflection surveys, seismic attributes, and geobody extractions, can reduce this uncertainty through recognition and accurate mapping of channel features. In this study, seismic attribute analysis, frequency analysis through spectral decomposition (SD), geobodies, and seismic sections have been used to delineate shallow Plio-Pleistocene El Wastani Formation channel fairways within the Saffron Field, offshore Nile Delta, Egypt. This has led to providing more reliable inputs for calculation of volumetrics. Interpretation of the stacked-channels complex through different seismic attributes helped to discriminate between sand-filled and shale-filled channels and in understanding their geometries. Results include more confident delineation of four distinct low-sinuosity channelized features. Petrophysical evaluation conducted on five wells penetrating Saffron reservoirs included electric logs and modular dynamic test data interpretation. The calculated average reservoir properties were used in different volumetric calculation cases. Different approaches were applied to delineate channel geometries that were later used in performing different volumetric cases. These approaches included defining channels from root-mean-square amplitude extractions, SD color-blended frequencies, and geobodies, all calculated from prestack seismic data. The different volumetric cases performed were compared against the latest field volume estimates proven after several years of production in which an area-versus-depth input showed the closest calculated hydrocarbon volumes to the actual proven field volumes.

Reservoir property prediction using abductive networks

Geophysics ◽  
2010 ◽  
Vol 75 (1) ◽  
pp. P1-P9 ◽  
Author(s):  
Osama A. Ahmed ◽  
Radwan E. Abdel-Aal ◽  
Husam AlMustafa
Keyword(s):  
Statistical Methods ◽  
Seismic Data ◽  
Polynomial Regression ◽  
Seismic Attributes ◽  
Oil Field ◽  
Attribute Selection ◽  
Reservoir Properties ◽  
Seismic Amplitude ◽  
Porosity Logs ◽  
High Degree

Statistical methods, such as linear regression and neural networks, are commonly used to predict reservoir properties from seismic attributes. However, a huge number of attributes can be extracted from seismic data and an efficient method for selecting an attribute subset with the highest correlation to the property being predicted is essential. Most statistical methods, however, lack an optimized approach for this attribute selection. We propose to predict reservoir properties from seismic attributes using abductive networks, which use iterated polynomial regression to derive high-degree polynomial predictors. The abductive networks simultaneously select the most relevant attributes and construct an optimal nonlinear predictor. We applied the approach to predict porosity from seismic data of an area within the 'Uthmaniyah portion of the Ghawar oil field, Saudi Arabia. The data consisted of normal seismic amplitude, acoustic impedance, 16 other seismic attributes, and porosity logs from seven wells located in the study area. Out of 27 attributes, the abductive network selected only the best two to six attributes and produced a more accurate and robust porosity prediction than using the more common neural-network predictors. In addition, the proposed method requires no effort in choosing the attribute subset or tweaking their parameters.

scholarly journals Application of Artificial Neural Network in seismic reservoir characterization: a case study from Offshore Nile Delta

2021 ◽  
Author(s):  
Adel Othman ◽  
Mohamed Fathy ◽  
Islam A. Mohamed
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Nile Delta ◽  
Reservoir Properties ◽  
Seismic Amplitude ◽  
Inversion Methods ◽  
Amplitude Data ◽  
Seismic Reservoir

AbstractThe Prediction of the reservoir characteristics from seismic amplitude data is a main challenge. Especially in the Nile Delta Basin, where the subsurface geology is complex and the reservoirs are highly heterogeneous. Modern seismic reservoir characterization methodologies are spanning around attributes analysis, deterministic and stochastic inversion methods, Amplitude Variation with Offset (AVO) interpretations, and stack rotations. These methodologies proved good outcomes in detecting the gas sand reservoirs and quantifying the reservoir properties. However, when the pre-stack seismic data is not available, most of the AVO-related inversion methods cannot be implemented. Moreover, there is no direct link between the seismic amplitude data and most of the reservoir properties, such as hydrocarbon saturation, many assumptions are imbedded and the results are questionable. Application of Artificial Neural Network (ANN) algorithms to predict the reservoir characteristics is a new emerging trend. The main advantage of the ANN algorithm over the other seismic reservoir characterization methodologies is the ability to build nonlinear relationships between the petrophysical logs and seismic data. Hence, it can be used to predict various reservoir properties in a 3D space with a reasonable amount of accuracy. We implemented the ANN method on the Sequoia gas field, Offshore Nile Delta, to predict the reservoir petrophysical properties from the seismic amplitude data. The chosen algorithm was the Probabilistic Neural Network (PNN). One well was kept apart from the analysis and used later as blind quality control to test the results.

Seismic attribute mapping of a fluvial reservoir in Rhourde Chegga field (Hassi Messaoud, Algeria)

2020 ◽  
Author(s):  
Nasrine Medjdouba ◽  
zahia benaissa ◽  
amar boudella
Keyword(s):  
Seismic Data ◽  
Lower Triassic ◽  
Reservoir Properties ◽  
Seismic Attribute ◽  
The North ◽  
Fluvial Reservoir ◽  
Attribute Analysis ◽  
The Relationship ◽  
Sand Bodies ◽  
Rms Amplitude

<p>Rhourde Chegga field is located in the north of Hassi Messaoud giant field, Algeria. The main hydrocarbon-bearing reservoir in Rhourde Chegga field is the lower Triassic Argilo-Gréseux reservoir. The Triassic sand is deposited as fluvial channels and overbank sands with a thickness ranging from 15 to 20 m, lying unconformably on the Paleozoic formations. Lateral and vertical distribution of the sand bodies makes their mapping very difficult and, sometimes, even impossible with conventional seismic interpretation. </p><p>To better define drilling targets within the Triassic sand in the Rhourde Chegga field, 3D stratigraphic seismic attribute analysis was performed along the reservoir level, using PSTM and mid angle stack seismic data. By combining various attributes (RMS amplitude, half energy, variance, etc.), the channelized feature has been clearly imaged and delineated on the horizon slices and the volume extraction. The relationship between the combined seismic attributes and reservoir properties at well locations showed a good correlation.</p><p>Based on this study, about ten produced wells have been successfully drilled, confirming the efficiency of seismic attribute analysis to predicted channel body geometry.</p><p>Keywords: Channel, Attributes, Amplitude, Fluvial reservoir.</p><p> </p>

Effects of incomplete parameterization on full-wavefield viscoelastic seismic data for petrophysical reservoir properties

Geophysics ◽  
2007 ◽  
Vol 72 (3) ◽  
pp. O9-O17 ◽  
Author(s):  
Upendra K. Tiwari ◽  
George A. McMechan
Keyword(s):  
Seismic Data ◽  
Input Data ◽  
Elastic Model ◽  
Quality Factors ◽  
Noise Levels ◽  
Reservoir Properties ◽  
Estimated Parameters ◽  
Model Parameterization ◽  
The Earth ◽  
Incomplete Model

In inversion of viscoelastic full-wavefield seismic data, the choice of model parameterization influences the uncertainties and biases in estimating seismic and petrophysical parameters. Using an incomplete model parameterization results in solutions in which the effects of missing parameters are attributed erroneously to the parameters that are included. Incompleteness in this context means assuming the earth is elastic rather than viscoelastic. The inclusion of compressional and shear-wave quality factors [Formula: see text] and [Formula: see text] in inversion gives better estimates of reservoir properties than the less complete (elastic) model parameterization. [Formula: see text] and [Formula: see text] are sensitive primarily to fluid types and saturations. The parameter correlations are sensitive also to the model parameterization. As noise increases in the viscoelastic input data, the resolution of the estimated parameters decreases, but the parameter correlations are relatively unaffected by modest noise levels.

scholarly journals Prediction of Shale Gas Reservoirs Using Fluid Mobility Attribute Driven by Post-Stack Seismic Data: A Case Study from Southern China

2020 ◽  
Vol 11 (1) ◽  
pp. 219
Author(s):  
Jing Zeng ◽  
Alexey Stovas ◽  
Handong Huang ◽  
Lixia Ren ◽  
Tianlei Tang
Keyword(s):  
Shale Gas ◽  
Seismic Data ◽  
Spectral Decomposition ◽  
Southern China ◽  
Gas Reservoirs ◽  
Seismic Attribute ◽  
Longmaxi Formation ◽  
Shale Gas Reservoirs ◽  
The Sichuan Basin ◽  
Shale Reservoirs

Paleozoic marine shale gas resources in Southern China present broad prospects for exploration and development. However, previous research has mostly focused on the shale in the Sichuan Basin. The research target of this study is expanded to the Lower Silurian Longmaxi shale outside the Sichuan Basin. A prediction scheme of shale gas reservoirs through the frequency-dependent seismic attribute technology is developed to reduce drilling risks of shale gas related to complex geological structure and low exploration level. Extracting frequency-dependent seismic attribute is inseparable from spectral decomposition technology, whereby the matching pursuit algorithm is commonly used. However, frequency interference in MP results in an erroneous time-frequency (TF) spectrum and affects the accuracy of seismic attribute. Firstly, a novel spectral decomposition technology is proposed to minimize the effect of frequency interference by integrating the MP and the ensemble empirical mode decomposition (EEMD). Synthetic and real data tests indicate that the proposed spectral decomposition technology provides a TF spectrum with higher accuracy and resolution than traditional MP. Then, a seismic fluid mobility attribute, extracted from the post-stack seismic data through the proposed spectral decomposition technology, is applied to characterize the shale reservoirs. The application result indicates that the seismic fluid mobility attribute can describe the spatial distribution of shale gas reservoirs well without well control. Based on the seismic fluid mobility attribute section, we have learned that the shale gas enrich areas are located near the bottom of the Longmaxi Formation. The inverted velocity data are also introduced to further verify the reliability of seismic fluid mobility. Finally, the thickness map of gas-bearing shale reservoirs in the Longmaxi Formation is obtained by combining the seismic fluid mobility attribute with the inverted velocity data, and two favorable exploration areas are suggested by analyzing the thickness, structure, and burial depth. The present work can not only be used to evaluate shale gas resources in the early stage of exploration, but also help to design the landing point and trajectory of directional drilling in the development stage.

scholarly journals Pitfalls and limitations in seismic attribute interpretation of tectonic features

2015 ◽  
Vol 3 (1) ◽  
pp. SB5-SB15 ◽  
Author(s):  
Kurt J. Marfurt ◽  
Tiago M. Alves
Keyword(s):  
Seismic Data ◽  
Seismic Attributes ◽  
Seismic Attribute ◽  
3D Data ◽  
Amplitude Data ◽  
Seismic Sections ◽  
Push Down ◽  
Data Coherence ◽  
Level Of Experience ◽  
Tectonic Features

Seismic attributes are routinely used to accelerate and quantify the interpretation of tectonic features in 3D seismic data. Coherence (or variance) cubes delineate the edges of megablocks and faulted strata, curvature delineates folds and flexures, while spectral components delineate lateral changes in thickness and lithology. Seismic attributes are at their best in extracting subtle and easy to overlook features on high-quality seismic data. However, seismic attributes can also exacerbate otherwise subtle effects such as acquisition footprint and velocity pull-up/push-down, as well as small processing and velocity errors in seismic imaging. As a result, the chance that an interpreter will suffer a pitfall is inversely proportional to his or her experience. Interpreters with a history of making conventional maps from vertical seismic sections will have previously encountered problems associated with acquisition, processing, and imaging. Because they know that attributes are a direct measure of the seismic amplitude data, they are not surprised that such attributes “accurately” represent these familiar errors. Less experienced interpreters may encounter these errors for the first time. Regardless of their level of experience, all interpreters are faced with increasingly larger seismic data volumes in which seismic attributes become valuable tools that aid in mapping and communicating geologic features of interest to their colleagues. In terms of attributes, structural pitfalls fall into two general categories: false structures due to seismic noise and processing errors including velocity pull-up/push-down due to lateral variations in the overburden and errors made in attribute computation by not accounting for structural dip. We evaluate these errors using 3D data volumes and find areas where present-day attributes do not provide the images we want.

Predict Channel Sand Body Distribution Characteristics of South Eighth District Based on RMS Amplitude Attributes & Frequency Division

2013 ◽  
Vol 734-737 ◽  
pp. 404-407 ◽  
Author(s):  
Yu Shuang Hu ◽  
Si Miao Zhu
Keyword(s):  
Seismic Data ◽  
Physical Property ◽  
Sedimentary Facies ◽  
Distribution Area ◽  
Frequency Division ◽  
Root Mean Square Amplitude ◽  
Seismic Attribute ◽  
Body Distribution ◽  
Attribute Analysis ◽  
Sand Body

A big tendency in oil industry is underestimating the heterogeneity of the reservoir and overestimating the connectivity, which results in overly optimistic estimates of the capacity. With the development of seismic attributes, we could pick up hidden reservoir lithology and physical property information from the actual seismic data, strengthen seismic data application in actual work, to ensure the objectivity of the results. In this paper, the channel sand body distribution in south eighth district of oilfield Saertu is predicted through seismic data root-mean-square amplitude and frequency division to identify sand body boundaries, predict the distribution area channel sand body characteristics successfully, which consistent with the sedimentary facies distribution. The result proves that seismic attribute analysis has good practicability in channel sand body prediction and sedimentary facies description.

Latest developments in seismic texture analysis for subsurface structure, facies, and reservoir characterization: A review

Geophysics ◽  
2011 ◽  
Vol 76 (2) ◽  
pp. W1-W13 ◽  
Author(s):  
Dengliang Gao
Keyword(s):  
Texture Analysis ◽  
Reservoir Characterization ◽  
Preliminary Test ◽  
Seismic Facies ◽  
Least Squares Regression ◽  
Reservoir Properties ◽  
Seismic Amplitude ◽  
Texture Model ◽  
Facies Classification ◽  
Analysis Window

In exploration geology and geophysics, seismic texture is still a developing concept that has not been sufficiently known, although quite a number of different algorithms have been published in the literature. This paper provides a review of the seismic texture concepts and methodologies, focusing on latest developments in seismic amplitude texture analysis, with particular reference to the gray level co-occurrence matrix (GLCM) and the texture model regression (TMR) methods. The GLCM method evaluates spatial arrangements of amplitude samples within an analysis window using a matrix (a two-dimensional histogram) of amplitude co-occurrence. The matrix is then transformed into a suite of texture attributes, such as homogeneity, contrast, and randomness, which provide the basis for seismic facies classification. The TMR method uses a texture model as reference to discriminate among seismic features based on a linear, least-squares regression analysis between the model and the data within an analysis window. By implementing customized texture model schemes, the TMR algorithm has the flexibility to characterize subsurface geology for different purposes. A texture model with a constant phase is effective at enhancing the visibility of seismic structural fabrics, a texture model with a variable phase is helpful for visualizing seismic facies, and a texture model with variable amplitude, frequency, and size is instrumental in calibrating seismic to reservoir properties. Preliminary test case studies in the very recent past have indicated that the latest developments in seismic texture analysis have added to the existing amplitude interpretation theories and methodologies. These and future developments in seismic texture theory and methodologies will hopefully lead to a better understanding of the geologic implications of the seismic texture concept and to an improved geologic interpretation of reflection seismic amplitude.

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