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.

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.

THE INTEGRATION OF MODERN TECHNOLOGY IN GIPPSLAND'S CENTRAL FIELDS STUDY

1994 ◽  
Vol 34 (1) ◽  
pp. 513
Author(s):  
P.V.Hinton P.V.Hinton ◽  
M.G.Cousins ◽  
P.E.Symes
Keyword(s):  
Seismic Data ◽  
Field Performance ◽  
Simulation Models ◽  
Modern Technology ◽  
Depositional Environments ◽  
Seismic Attribute ◽  
Sequence Stratigraphic ◽  
Multidisciplinary Study ◽  
Attribute Analysis ◽  
Definition Of

The central fields area of the Gippsland Basin, Australia, includes the Halibut, Cobia, Fortescue, and Mackerel oil fields. These large fields are mature with about 80% of the reserves produced. During 1991 and 1992 a multidisciplinary study, integrating the latest technology, was completed to help optimise the depletion of the remaining significant reserves.A grid of 4500 km of high resolution 3D seismic data covering 191 square kilometres allowed the identification of subtle structural traps as well as better definition of sandstone truncation edges which represent the ultimate drainage points. In addition, the latest techniques in seismic attribute analysis provided insight into depositional environments, seal potential and facies distribution. Sequence stratigraphic concepts were used in combination with seismic data to build complex multi million cell 3D geological models. Reservoir simulation models were then constructed to history match past production and to predict future field performance. Facility studies were also undertaken to optimise depletion strategies.The Central Fields Depletion Study has resulted in recommendations to further develop the fields with about 80 work-overs, 50 infill wells, reduction in separator pressures, and gas lift and water handling facility upgrades. These activities are expected to increase ultimate reserves and production. Some of the recommendations have been implemented with initial results of additional drilling on Mackerel increasing platform production from 22,000 BOPD to over 50,000 BOPD. An ongoing program of additional drilling from the four platforms is expected to continue for several years.

scholarly journals Seismic Characteristics and Development of the Upper Jurassic Carbonate Buildups from the Miechów Trough (Southern Poland)

Geosciences ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 239
Author(s):  
Łukasz Słonka ◽  
Piotr Krzywiec
Keyword(s):  
Seismic Data ◽  
Sedimentary Facies ◽  
Upper Jurassic ◽  
Seismic Facies ◽  
Depositional Model ◽  
Attribute Analysis ◽  
Deep Wells ◽  
Depositional System ◽  
Carbonate Buildups ◽  
Mass Flows

The presented study is devoted to the subsurface Upper Jurassic carbonate buildups and surrounding stratified inter-buildup deposits in the hitherto less recognized area, in comparison with other parts of the northern Tethyan shelf in Poland and Europe. The study area is located within the present-day Miechów Trough, almost entirely covered by thick Cretaceous and younger deposits. This paper shows results of the interpretation of 2D seismic data, calibrated by data from deep wells. Investigation of various elements of the Upper Jurassic carbonate depositional system in the Miechów Trough is supported by seismic facies and attribute analysis. The four distinctive seismic facies—(A) bedded, (B) mound-shaped, (C) contorted-chaotic, and (D) chaotic—were assigned to the main Upper Jurassic sedimentary facies, represented by (1) bedded facies, (2) massive facies (carbonate buildups) and (3) deposits of gravity mass-flows. The results of this study were used to construct a depositional model for the Upper Jurassic succession, that focuses on the initiation, growth and demise of the large carbonate buildups in this part of the basin. This paper also presents the more extensive distribution of the Upper Jurassic carbonate buildups than was previously proposed for the Miechów Trough.

Seismic‐attribute analysis utilizing multiazimuth seismic data in the offshore Nile Delta — A case study

2007 ◽  
Author(s):  
Robert Marten ◽  
Walter Rietveld ◽  
Mark Benson ◽  
Alaa Khodeir ◽  
James Keggin ◽  
...  
Keyword(s):  
Seismic Data ◽  
Nile Delta ◽  
Seismic Attribute ◽  
Attribute Analysis

The Application of Facies Modeling Combination of Well Data and Seismic Data in G25-12 Block

2015 ◽  
Vol 733 ◽  
pp. 92-95
Author(s):  
Jia Hui Wang ◽  
Hong Sheng Lv
Keyword(s):  
Sedimentary Facies ◽  
Distribution Law ◽  
Skeleton Model ◽  
Facies Model ◽  
Body Distribution ◽  
Sand Body ◽  
Geological Conditions ◽  
Single Well ◽  
Well Data ◽  
Sand Bodies

The main purpose of lithofacies modeling is to get the actual reservoir lithofacies skeleton model which is maximum approximation of the underground reservoir. The facies model can effectively solve the problem of predicting sand bodies between wells. At the same time, we still use the stochastic modeling method to build the facies model of unconstrained single well simulation and sedimentary facies controlled constrained simulation. We elected the model which is most consistent to the actual geological conditions, providing theoretical guidance for characterizing the interwell sand body distribution law and improving the accuracy of predicting sand bodies between wells, laiding the foundation for further exploration and development of oil reservoir.

scholarly journals Application of Seismic Multi-Attribute Inversion Based on Wireless Network Sensor in Dongying Delta Foreset

2020 ◽  
Author(s):  
Yuanyuan Wang ◽  
Cui Wang
Keyword(s):  
Seismic Data ◽  
Statistical Characteristics ◽  
Support Vector ◽  
Nonlinear Inversion ◽  
Intelligent Network ◽  
Seismic Attribute ◽  
Sand Body ◽  
Shahejie Formation ◽  
Wireless Module ◽  
Attribute Combination

Abstract Seismic attributes, which are extracted from seismic information, are physical indexes used specificallyfor the measurement of geometric, dynamic, or statistical characteristics of seismic data. Current methods for seismic multi-attribute inversion include linear and nonlinear methods. By adopting the wireless module of NFC24l01, combined with the seismic data acquisition sensor, constitutes an intelligent network sensor, and then it sends the collected data to the topmost machine for analysis. Methods for the nonlinear inversion of seismic multi-attributes usually employ tools such as neural networks and support vector machines (SVMs)for mapping. Hence, inversion results obtained via nonlinear methodsare more accurate than those obtained via linear methods. In this work, with spontaneous-potential (SP) curves as the objective of nonlinear inversion, an optimized seismic attribute combination for the inversion of SP curves was identified, and the nonlinear inversion of seismic multi-attributes was achieved via the use of a deep neural network (DNN) to obtain 3D SP data. Finally, the foresetting process of a sand body of the intermediate section in Member 3 of the Shahejie Formation in the Dongying Delta was illustrated via the horizon slice of the SP data.

scholarly journals Application of seismic multi-attribute inversion based on wireless network sensor in Dongying delta foreset

2020 ◽  
Author(s):  
Yuanyuan Wang ◽  
Cui Wang
Keyword(s):  
Seismic Data ◽  
Statistical Characteristics ◽  
Support Vector ◽  
Nonlinear Inversion ◽  
Intelligent Network ◽  
Seismic Attribute ◽  
Sand Body ◽  
Shahejie Formation ◽  
Wireless Module ◽  
Attribute Combination

Abstract Seismic attributes, which are extracted from seismic information, are physical indexes used specificallyfor the measurement of geometric, dynamic, or statistical characteristics of seismic data. Current methods for seismic multi-attribute inversion include linear and nonlinear methods. By adopting the wireless module of NFC24l01, combined with the seismic data acquisition sensor, constitutes an intelligent network sensor, and then it sends the collected data to the topmost machine for analysis. Methods for the nonlinear inversion of seismic multi-attributes usually employ tools such as neural networks and support vector machines (SVMs)for mapping. Hence, inversion results obtained via nonlinear methodsare more accurate than those obtained via linear methods. In this work, with spontaneous-potential (SP) curves as the objective of nonlinear inversion, an optimized seismic attribute combination for the inversion of SP curves was identified, and the nonlinear inversion of seismic multi-attributes was achieved via the use of a deep neural network (DNN) to obtain 3D SP data. Finally, the foresetting process of a sand body of the intermediate section in Member 3 of the Shahejie Formation in the Dongying Delta was illustrated via the horizon slice of the SP 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.

Identification of polygonal faulting from legacy 3D seismic data in vintage Gulf of Mexico data using seismic attributes

2021 ◽  
pp. 1-17
Author(s):  
Karen M. Leopoldino Oliveira ◽  
Heather Bedle ◽  
Karelia La Marca Molina
Keyword(s):  
Seismic Data ◽  
Seismic Attributes ◽  
Fault System ◽  
3D Seismic ◽  
Seismic Attribute ◽  
Variable Amplitude ◽  
Attribute Analysis ◽  
Amplitude Data ◽  
3D Seismic Data ◽  
Polygonal Fault

We analyzed a 1991 3D seismic data located offshore Florida and applied seismic attribute analysis to identify geological structures. Initially, the seismic data appears to have a high signal-to-noise-ratio, being of an older vintage of quality, and appears to reveal variable amplitude subparallel horizons. Additional geophysical analysis, including seismic attribute analysis, reveals that the data has excessive denoising, and that the continuous features are actually a network of polygonal faults. The polygonal faults were identified in two tiers using variance, curvature, dip magnitude, and dip azimuth seismic attributes. Inline and crossline sections show continuous reflectors with a noisy appearance, where the polygonal faults are suppressed. In the variance time slices, the polygonal fault system forms a complex network that is not clearly imaged in the seismic amplitude data. The patterns of polygonal fault systems in this legacy dataset are compared to more recently acquired 3D seismic data from Australia and New Zealand. It is relevant to emphasize the importance of seismic attribute analysis to improve accuracy of interpretations, and also to not dismiss older seismic data that has low accurate imaging, as the variable amplitude subparallel horizons might have a geologic origin.

Sign in / Sign up

Export Citation Format

Share Document