NEW SEISMIC METHODS AND POTENTIAL FORTHE REGION

2000 ◽  
Vol 40 (1) ◽  
pp. 326 ◽  
Author(s):  
S.B. Reymond
Keyword(s):  
Seismic Data ◽  
Seismic Attributes ◽  
3D Seismic ◽  
Data Types ◽  
Seismic Attribute ◽  
Single Class ◽  
Polynomial Coefficients ◽  
Seismic Methods ◽  
Exploration And Production ◽  
New Interpretation

New seismic data types and new interpretation tools are rapidly changing and expanding the applicability of seismic methods. In Australia, the hydrocarbon and mining industries are both using an increased proportion of 3D seismic data to improve lateral predictability and to reduce exploration and production risks and costs. New techniques and workflows described below are expected to come into more common usage over the next five years.3D reservoir characterisation workflows integrate all reservoir data types including well log measurements, seismic data, reservoir models and simulations. Within the domain of seismic data, complementary data types, such as 3D, 4D, 4C, AVO and down-hole seismic, need to be synthesised into a single optimum reservoir representation. One way of producing such results is by using seismic classification which is based on a set of geostatistical and neural network algorithms to produce a single class map or cube mapping reservoir parameters with an uncertainty estimate associated with each trace and sample location.Seismic classification is applied to two types of seismic data: attribute grids and 3D seismic attribute cubes. Seismic classification provides a tool for generalised inversion of seismic data for lithofacies, faults and fluids (DHI, Direct Hydrocarbon Indicators). At the acquisition and processing stages of seismic data, the same classification algorithms are used to assess data quality to quantify and improve seismic data quality.Recent developments in seismic attributes have shown that additional reservoir characterisation information is obtained by decomposing the seismic trace into a set of polynomial coefficients. Such seismic attributes are computed both on the seismic trace and on a synthetic trace computed along the borehole trajectory in order to calibrate the seismic attribute by measured reservoir parameters.An increasing number of 3D attribute cubes or transforms of the raw seismic volume are used by geophysicists to better capture lateral changes in seismic response. The potential and pitfalls of 3D attribute cubes are illustrated with reference to Australasian examples.Increasing interest is also being shown in fault and fracture mapping bom seismic data with applications in both the mining and hydrocarbon industries. Fault mapping and automated extraction can both be based on structural seismic attribute grids and cubes.

scholarly journals Characterization of reservoir sands using 3D seismic attributes in the coastal swamp area of Niger Delta Basin

2021 ◽  
Author(s):  
Oluwatoyin Khadijat Olaleye ◽  
Pius Adekunle Enikanselu ◽  
Michael Ayuk Ayuk
Keyword(s):  
Seismic Data ◽  
Niger Delta ◽  
Seismic Attributes ◽  
Effective Porosity ◽  
High Porosity ◽  
Hydrocarbon Accumulation ◽  
3D Seismic ◽  
Seismic Attribute ◽  
Niger Delta Basin

AbstractHydrocarbon accumulation and production within the Niger Delta Basin are controlled by varieties of geologic features guided by the depositional environment and tectonic history across the basin. In this study, multiple seismic attribute transforms were applied to three-dimensional (3D) seismic data obtained from “Reigh” Field, Onshore Niger Delta to delineate and characterize geologic features capable of harboring hydrocarbon and identifying hydrocarbon productivity areas within the field. Two (2) sand units were delineated from borehole log data and their corresponding horizons were mapped on seismic data, using appropriate check-shot data of the boreholes. Petrophysical summary of the sand units revealed that the area is characterized by high sand/shale ratio, effective porosity ranged from 16 to 36% and hydrocarbon saturation between 72 and 92%. By extracting attribute maps of coherence, instantaneous frequency, instantaneous amplitude and RMS amplitude, characterization of the sand units in terms of reservoir geomorphological features, facies distribution and hydrocarbon potential was achieved. Seismic attribute results revealed (1) characteristic patterns of varying frequency and amplitude areas, (2) major control of hydrocarbon accumulation being structural, in terms of fault, (3) prospective stratigraphic pinch-out, lenticular thick hydrocarbon sand, mounded sand deposit and barrier bar deposit. Seismic Attributes analysis together with seismic structural interpretation revealed prospective structurally high zones with high sand percentage, moderate thickness and high porosity anomaly at the center of the field. The integration of different seismic attribute transforms and results from the study has improved our understanding of mapped sand units and enhanced the delineation of drillable locations which are not recognized on conventional seismic interpretations.

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.

scholarly journals Study on the 3D Sedimentary Characteristics of Sandstone Type Uranium Reservoir Based on 3D Seismic Attribute

Minerals ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1096
Author(s):  
Zhangqing Sun ◽  
Yaguang Liu ◽  
Fuxing Han ◽  
Fengjiao Zhang ◽  
Xiyang Ou ◽  
...  
Keyword(s):  
Seismic Data ◽  
Seismic Attributes ◽  
3D Seismic ◽  
Uranium Deposits ◽  
Seismic Attribute ◽  
Sedimentary Characteristics ◽  
Reference Information ◽  
Drilling Data ◽  
The One

It is of great significance to quickly obtain the sedimentary characteristics of sandstone type uranium reservoir for guiding prospecting sandstone type uranium deposits. In order to solve this problem, a method based on the extraction and optimization of 3D seismic attributes is proposed. The target stratum of the uranium reservoir is accurately located by using the gamma and acoustic logging data together. The well seismic calibration for the uranium reservoir is carried out by making full use of the logging and seismic data. The high-density fine horizon tracking is implemented for the top, bottom, and obvious adjacent interfaces of the target stratum. Various seismic attributes along the target interface are extracted using stratigraphic slices. Analyzing the consistency between the results obtained by various seismic attributes and drilling data, the one that can best characterize the sedimentary characteristics of the target uranium reservoir is selected as the optimal seismic attribute. The sedimentary and its evolutionary characteristics of the target uranium reservoir are obtained by extracting the above optimal seismic attribute. A case study shows that we can obtain the 3D sedimentary characteristics of the target uranium reservoir fast and efficiently using the method based on the 3D seismic attribute. They can be used for providing important reference information for the exploration of sandstone type uranium deposits.

The Application of Seismic Attribute in the Prediction of Inner Front Facies Sands

2012 ◽  
Vol 616-618 ◽  
pp. 705-709
Author(s):  
Kai Chun Yu ◽  
Yan Zhu ◽  
Yan Meng
Keyword(s):  
Comparative Analysis ◽  
Seismic Data ◽  
Prediction Accuracy ◽  
Seismic Attributes ◽  
Small River ◽  
3D Seismic ◽  
Seismic Attribute ◽  
Well Spacing ◽  
Reservoir Description ◽  
3D Seismic Data

The south of Daqing placanticline is mainly front facies reservoir which is chiefly the smooth, direct, narrow and small river. The size of the river is small and the reservoir thickness is mainly 1.0m-1.2m.The prediction accuracy of the interwell sandbody is only up to 60~70% under the condition of existing well network and well spacing, causing the relatively worse development effect. In order to raise the prediction accuracy of the interwell sandbody, it proposes the thinking of identifying the boundary and orientation of the inner front facies by means of the combination of wells and seismos of 3D seismic data. After the comparative analysis of the wells and the seismos, it determines the seismic attributes which can reflect the sandstone and identify mudstone preferablely, and proposes the method of reservoir description by means of the combination of wells and seismos which makes the prediction accuracy of the interwell sandbody reach to 80.77%.

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.

Using AI/ML to Explore & Develop Quickly and Efficiently

2021 ◽  
Author(s):  
Anthony Aming
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Seismic Data ◽  
3D Seismic ◽  
Structure Amplitude ◽  
Zero Crossing ◽  
Petroleum Systems ◽  
Exploration And Production ◽  
Data Volume ◽  
3D Seismic Data

Abstract See how application of a fully trained Artificial Intelligence (AI) / Machine Learning (ML) technology applied to 3D seismic data volumes delivers an unbiased data driven assessment of entire volumes or corporate seismic data libraries quickly. Whether the analysis is undertaken using onsite hardware or a cloud based mega cluster, this automated approach provides unparalleled insights for the interpretation and prospectivity analysis of any dataset. The Artificial Intelligence (AI) / Machine Learning (ML) technology uses unsupervised genetics algorithms to create families of waveforms, called GeoPopulations, that are used to derive Amplitude, Structure (time or depth depending on the input 3D seismic volume) and the new seismic Fitness attribute. We will show how Fitness is used to interpret paleo geomorphology and facies maps for every peak, trough and zero crossing of the 3D seismic volume. Using the Structure, Amplitude and Fitness attribute maps created for every peak, trough and zero crossing the Exploration and Production (E&P) team can evaluate and mitigate Geological and Geophysical (G&G) risks and uncertainty associated with their petroleum systems quickly using the entire 3D seismic data volume.

Unsupervised machine learning using 3D seismic data applied to reservoir evaluation and rock type identification

2021 ◽  
pp. 1-69
Author(s):  
Marwa Hussein ◽  
Robert R. Stewart ◽  
Deborah Sacrey ◽  
Jonny Wu ◽  
Rajas Athale
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Rock Type ◽  
Seismic Attributes ◽  
Reservoir Quality ◽  
3D Seismic ◽  
Reservoir Properties ◽  
Unsupervised Machine Learning ◽  
Amplitude Data ◽  
3D Seismic Data

Net reservoir discrimination and rock type identification play vital roles in determining reservoir quality, distribution, and identification of stratigraphic baffles for optimizing drilling plans and economic petroleum recovery. Although it is challenging to discriminate small changes in reservoir properties or identify thin stratigraphic barriers below seismic resolution from conventional seismic amplitude data, we have found that seismic attributes aid in defining the reservoir architecture, properties, and stratigraphic baffles. However, analyzing numerous individual attributes is a time-consuming process and may have limitations for revealing small petrophysical changes within a reservoir. Using the Maui 3D seismic data acquired in offshore Taranaki Basin, New Zealand, we generate typical instantaneous and spectral decomposition seismic attributes that are sensitive to lithologic variations and changes in reservoir properties. Using the most common petrophysical and rock typing classification methods, the rock quality and heterogeneity of the C1 Sand reservoir are studied for four wells located within the 3D seismic volume. We find that integrating the geologic content of a combination of eight spectral instantaneous attribute volumes using an unsupervised machine-learning algorithm (self-organizing maps [SOMs]) results in a classification volume that can highlight reservoir distribution and identify stratigraphic baffles by correlating the SOM clusters with discrete net reservoir and flow-unit logs. We find that SOM classification of natural clusters of multiattribute samples in the attribute space is sensitive to subtle changes within the reservoir’s petrophysical properties. We find that SOM clusters appear to be more sensitive to porosity variations compared with lithologic changes within the reservoir. Thus, this method helps us to understand reservoir quality and heterogeneity in addition to illuminating thin reservoirs and stratigraphic baffles.

The Use of Near-Seafloor 3D Seismic Data in Deepwater Exploration and Production

2004 ◽  
Vol 29 (1) ◽  
pp. 35-43 ◽  
Author(s):  
G.S. Steffens ◽  
R.C. Shipp ◽  
B.E. Prather ◽  
J.A. Nott ◽  
J.L. Gibson ◽  
...  
Keyword(s):  
Seismic Data ◽  
3D Seismic ◽  
Exploration And Production ◽  
3D Seismic Data

Application of 3D seismic attributes to optimize the placement of horizontal wells within a tight gas sand reservoir, Ordos Basin, China

Geophysics ◽  
2016 ◽  
Vol 81 (3) ◽  
pp. B77-B86 ◽  
Author(s):  
Zhiguo Wang ◽  
Jinghuai Gao ◽  
Xiaolan Lei ◽  
Xiaojie Cui ◽  
Daxing Wang
Keyword(s):  
Seismic Data ◽  
Ordos Basin ◽  
Horizontal Wells ◽  
Tight Gas ◽  
3D Seismic ◽  
Seismic Attribute ◽  
Horizontal Drilling ◽  
The Ordos Basin ◽  
Gas Bearing ◽  
3D Seismic Data

The Lower Permian Xiashihezi Formation in the Ordos Basin, China, is a quartz-sandstone reservoir with low porosity and low permeability. We have acquired 3D seismic data and well data from 18 vertical and four horizontal wells to indicate the potential of seismic attribute analyses in locating seismic sweet spots for lateral placement of horizontal wells. Using the analytic wavelet transform with a Morse wavelet, the integration of high tuning spectral components, high sweetness and high spectral attenuation helped us to estimate the distribution of gas-bearing tight sands in the Xiashihezi Formation. Our results revealed that the principal target of horizontal drilling and production was gas-bearing massive point bars in the braided river delta setting of the Ordos Basin. The integrated workflow of the seismic attribute analysis contributes to the optimal horizontal well planning by mining and exposing critical geological information of a tight gas sand reservoir from within 3D seismic data.

A simple procedure for handling misties on 2D migrated seismic lines

2016 ◽  
Vol 4 (3) ◽  
pp. B17-B21 ◽  
Author(s):  
Donald A. Herron
Keyword(s):  
Seismic Data ◽  
Simple Procedure ◽  
Vertical Plane ◽  
3D Seismic ◽  
Production Environments ◽  
Exploration And Production ◽  
Technical Basis ◽  
3D Seismic Data ◽  
Seismic Lines

Despite the ever-increasing use of 3D seismic data in modern exploration and production environments, 2D seismic data are still widely used in many projects. Mapping of horizons interpreted on 2D migrated seismic lines must necessarily address the problem of misties at line intersections, whether the data are migrated in the time or the depth domain. These misties are the result of the inability of 2D migration to account for the dip of reflections out of the vertical plane of the migrated section. This tutorial describes the technical basis for a simple procedure by which the deeper of the values for an interpreted horizon at the intersection of two 2D migrated seismic lines is used to guide mapping of the horizon.

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