scholarly journals Use of Attributes on 3D Seismic Data for Studying Mishrif Formation in East Abu-Amoud Field, South-Eastern Iraq

2021 ◽  
pp. 4802-4809
Author(s):  
Mohammed H. Al-Aaraji ◽  
Hussein H. Karim
Keyword(s):  
Seismic Data ◽  
Acoustic Impedance ◽  
Seismic Attributes ◽  
3D Seismic ◽  
Seismic Section ◽  
Reflected Waves ◽  
South Eastern ◽  
Seismic Sections ◽  
Sonic Logs ◽  
3D Seismic Data

      The seismic method depends on the nature of the reflected waves from the interfaces between layers, which in turn depends on the density and velocity of the layer, and this is called acoustic impedance. The seismic sections of the East Abu-Amoud field that is located in Missan Province, south-eastern Iraq, were studied and interpreted for updating the structural picture of the major Mishrif Formation for the reservoir in the field. The Mishrif Formation is rich in petroleum in this area, with an area covering about 820 km2. The horizon was calibrated and defined on the seismic section with well logs data (well tops, check shot, sonic logs, and density logs) in the interpretation process to identify the upper and lower boundaries of the Formation.  Seismic attributes were used to study the formation, including instantaneous phase attributes and relative acoustic impedance on time slice of 3D seismic data . Also, relative acoustic impedance was utilized to study the top of the Mishrif Formation. Based on these seismic attributes, karst features of the formation were identified. In addition, the nature of the lithology in the study area and the change in porosity were determined through the relative acoustic impedance The overlap of the top of the Mishrif Formation with the bottom of the Khasib Formation was determined because the Mishrif Formation is considered as an unconformity surface.

scholarly journals Structural Interpretation of Seismic Data of Mishrif Formation in East Abu-Amoud Field, South-eastern Iraq

2021 ◽  
pp. 3612-3619
Author(s):  
Mohammed H. Al-Aaraji ◽  
Hussein H. Karim
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Well Logs ◽  
Seismic Section ◽  
Reflected Waves ◽  
South Eastern ◽  
Vertical Variations ◽  
Structural Image ◽  
Seismic Sections ◽  
Sonic Logs

      The seismic method depends on the nature of the reflected waves from the interfaces between layers, which in turn depends on the density and velocity of the layer, and this is called acoustic impedance. The seismic sections of the East Abu-Amoud field that is located in Missan Province, south-eastern Iraq, were studied and interpreted for updating the structural picture of the major Mishrif Formation for the reservoir in the Abu-amoud field. The Mishrif Formation is rich in petroleum in this area, with an area covering about 820 km2. The seismic interpretation of this study was carried out utilizing the software of Petrel-2017. The horizon was calibrated and defined on the seismic section with well-logs data (well tops, check shot, sonic logs, and density logs) in the interpretations process for identifying the upper and lower boundaries of Mishrif Formation. As well, mapping of two-way time and depth structural maps was carried out, to aid in understanding the lateral and vertical variations and to show the formation of the structural surfaces. The study found that Mishrif thickness increases toward the east, which means that it increases from the Abu-Amoud field in Nasiriyah towards the East Abu-Amoud field in Missan province.       The aim of the study is to draw a high-resolution structural image of the East Abu Amoud field in southeast Iraq and to show the types of the existing faults and structures in the study area.

scholarly journals Evaluation of seismic attributes for reservoir characterization over Edi field, Niger delta, Nigeria using 3d seismic data

2020 ◽  
Vol 8 (2) ◽  
pp. 168
Author(s):  
Nyeneime O. Etuk ◽  
Mfoniso U. Aka ◽  
Okechukwu A. Agbasi ◽  
Johnson C. Ibuot
Keyword(s):  
Seismic Data ◽  
Niger Delta ◽  
Reservoir Characterization ◽  
Seismic Attributes ◽  
3D Seismic ◽  
Root Mean Square Amplitude ◽  
Phase Gradient ◽  
Major Fault ◽  
Seismic Sections ◽  
3D Seismic Data

Seismic attributes were evaluated over Edi field, offshore Western Niger Delta, Nigeria, via 3D seismic data. Manual mappings of the horizons and faults on the in-lines and cross-lines of the seismic sections were done. Various attributes were calculated and out put on four horizons corresponding to the well markers at different formations within the well were identified. The four horizons identified, which includes: H1, H2, H3 and H4 were mapped and interpreted across the field. The operational agenda was thru picking given faults segments on the in–line of seismic volume. A total of five faults coded as F1, F2, F3, F4 and F5, F1 and F5 were the major fault and were observed as extending through the field. Structural and horizon mappings were used to generate time structure maps. The maps showed the various positions and orientations of the faults. Different attributes which include: root mean square amplitude, instantaneous phase, gradient magnitude and chaos were run on the 3D seismic data. The amplitude and incline magnitude maps indicate direct hydrocarbon on the horizon maps; this is very important in the drilling of wells because it shows areas where hydrocarbons are present in the subsurface. The seismic attributes revealed information, which was not readily apparent in the raw seismic data.   

Estimation of effective porosity using geostatistics and multiattribute transforms: A case study

Geophysics ◽  
2004 ◽  
Vol 69 (2) ◽  
pp. 352-372 ◽  
Author(s):  
A. G. Pramanik ◽  
V. Singh ◽  
Rajiv Vig ◽  
A. K. Srivastava ◽  
D. N. Tiwary
Keyword(s):  
Seismic Data ◽  
Acoustic Impedance ◽  
Reservoir Characterization ◽  
Middle Eocene ◽  
Seismic Attributes ◽  
Effective Porosity ◽  
3D Seismic ◽  
Porosity Distribution ◽  
Porosity Logs ◽  
3D Seismic Data

The middle Eocene Kalol Formation in the north Cambay Basin of India is producing hydrocarbons in commercial quantity from a series of thin clastic reservoirs. These reservoirs are sandwiched between coal and shale layers, and are discrete in nature. The Kalol Formation has been divided into eleven units (K‐I to K‐XI) from top to bottom. Multipay sands of the K‐IX unit 2–8 m thick are the main hydrocarbon producers in the study area. Apart from their discrete nature, these sands exhibit lithological variation, which affects the porosity distribution. Low‐porosity zones are found devoid of hydrocarbons. In the available 3D seismic data, these sands are not resolved and generate a composite detectable seismic response, making reservoir characterization through seismic attributes impossible. After proper well‐to‐seismic tie, the major stratigraphic markers were tracked in the 3D seismic data volume for structural mapping and carrying out attribute analysis. The 3D seismic volume was inverted to obtain an acoustic impedance volume using a model‐based inversion algorithm, improving the vertical resolution and resolving the K‐IX pay sands. For better reservoir characterization, effective porosity distribution was estimated through different available techniques taking the K‐IX upper sand as an example. Various sample‐based seismic attributes, the impedance volume, and effective porosity logs were used as inputs for this purpose. These techniques are map‐based geostatistical methods using the acoustic impedance volume, stepwise multilinear regression, probabilistic neural networks (PNN) using multiattribute transforms, and a new technique that incorporates both geostatistics and multiattribute transforms (either linear or nonlinear). This paper is an attempt to compare different available techniques for porosity estimation. On comparison, it is found that the PNN‐based approach using ten sample‐based attributes showed highest crosscorrelation (0.9508) between actual and predicted effective porosity logs at eight wells in the study area. After validation, the predicted effective porosity maps for the K‐IX upper sand are generated using different techniques, and a comparison among them is made. The predicted effective porosity map obtained from PNN‐based model provides more meaningful information about the K‐IX upper sand reservoir. In order to give priority to the actual effective porosity values at wells, the predicted effective porosity map obtained from PNN‐based model for the K‐IX upper sand was combined with actual effective porosity values using co‐kriging geostatistical technique. This final map provides geologically more realistic predicted effective porosity distribution and helps in understanding the subsurface image. The implication of this work in exploration and development of hydrocarbons in the study area is discussed.

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.

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 Transition zones analysis using empirical capillary pressure model from well logs and 3D seismic data on ‘Stephs’ field, onshore, Niger Delta, Nigeria

2019 ◽  
Vol 10 (3) ◽  
pp. 1227-1242
Author(s):  
O. Abiola ◽  
F. O. Obasuyi
Keyword(s):  
Capillary Pressure ◽  
Seismic Data ◽  
Niger Delta ◽  
Time Structure ◽  
Well Logs ◽  
3D Seismic ◽  
Seismic Section ◽  
Transition Zones ◽  
Mobile Water ◽  
3D Seismic Data

AbstractCapillary pressure is an important characteristic that indicates the zones of interaction between two-phase fluids or fluid and rock occurring in the subsurface. The analysis of transition zones (TZs) using Goda (Sam) et al.’s empirical capillary pressure from well logs and 3D seismic data in ‘Stephs’ field, Niger Delta, was carried out to remove the effect of mobile water above the oil–water contact in reservoirs in the absence of core data/information. Two reservoirs (RES B and C) were utilized for this study with net thicknesses (NTG) ranging from 194.14 to 209.08 m. Petrophysical parameters computed from well logs indicate that the reservoirs’ effective porosity ranges from 10 to 30% and the permeability ranges from 100 to > 1000 mD, which are important characteristics of good hydrocarbon bearing zone. Checkshot data were used to tie the well to the seismic section. Faults and horizons were mapped on the seismic section. Time structure maps were generated, and a velocity model was used to convert the time structure maps to its depth equivalent. A total of six faults were mapped, three of which are major growth faults (F1, F4 and F5) and cut across the study area. Reservoir properties were modelled using SIS and SGS. The capillary pressure log, curves and models generated were useful in identifying the impact of mobile water in the reservoir as they show the trend of saturating and interacting fluids. The volume of oil estimated from reservoirs B and C without taking TZ into consideration was 273 × 106 and 406 × 106 mmbbls, respectively, and was found to be higher than the volume of oil estimated from the two reservoirs while taking TZ into consideration which was 242 × 106 and 256 × 106 mmbbls, respectively. The results have indicated the presence of mobile water, which have further established that conventionally recoverable hydrocarbon (RHC) is usually overestimated; hence, TZ analysis has to be performed for enhancing RHC for cost-effective extraction and profit maximization.

Discrete 3D fracture network extraction and characterization from 3D seismic data — A case study at Teapot Dome

2015 ◽  
Vol 3 (3) ◽  
pp. ST29-ST41 ◽  
Author(s):  
Manoj Vallikkat Thachaparambil
Keyword(s):  
Seismic Data ◽  
Seismic Attributes ◽  
3D Seismic ◽  
Spectral Edge ◽  
Teapot Dome ◽  
Fracture Data ◽  
Discontinuity Surfaces ◽  
Seismic Discontinuity ◽  
3D Seismic Data ◽  
Tensleep Formation

Three-dimensional discrete fracture networks (DFNs) extracted from the seismic data of the Tensleep Formation at Teapot Dome successfully matched 1D fracture data from multiple boreholes within the area. The extraction process used four seismic attributes, i.e., variance, chaos, curvature, and spectral edge, and their multiple realizations to define seismic discontinuities that could potentially represent fractures within the Tensleep Formation. All of the potential fracture attributes were further enhanced using a fracture-tracking attribute for better extraction and analysis of seismic discontinuity surfaces and their network properties. A state-of-the-art discontinuity surface extraction and characterization workflow uniformly extracted and interactively characterized the seismic discontinuity surfaces and networks that correlate with borehole fracture data. Among the attributes, a fracture-tracking attribute cube created out of the high-resolution spectral-edge attribute provided the best match with the borehole fracture data from the Tensleep Formation. Therefore, the extracted discontinuity planes were classified as fractures and then characterized. The extracted fracture population also matched earlier published records of faults and fractures at Teapot Dome. Unlike the conventional method, which uses 1D borehole fracture data as primary input and 3D seismic data as a guide volume during DFN modeling, I used 3D seismic attributes as the primary data and the 1D borehole fracture data only for quality control. I also evaluated the power of converting seismic fracture attribute volumes into discrete surfaces and networks for effective correlation with 1D fracture logs from boreholes.

scholarly journals Geometrical Seismic Attribute for Fault Identifiction on Canada Exploration Field

2017 ◽  
Vol 17 (2) ◽  
pp. 91
Author(s):  
Reni Agustiani ◽  
Puguh Hiskiawan ◽  
Rano Rano
Keyword(s):  
Seismic Data ◽  
Data Interpretation ◽  
East Sea ◽  
3D Seismic ◽  
Seismic Attribute ◽  
Data Volume ◽  
Major Fault ◽  
Western Side ◽  
Sonic Logs ◽  
3D Seismic Data

It has been performed data interpretation of 3D seismic data and drilling field exploration wellsBasin Nova ScotiaKanada to know structure fault on the field Missisauga Formation. Seismic dataused is 601 inline, crossline 482, and the data used drilling wells are two wells which there is a loggamma ray, sonic logs and log RHOB. Interpretation is done the analysis of the map in thestructure of time and analysis of seismic attribute maps based on the geometrical attribute serves todetermine their structure or structural faults of the data volume 3D. Based on the time structuremap well known that first well is in the region heights and second wells is in low region. Based oninterpretation of the map attributes known three faults are two major fault and one minor fault.Two faults are in the East Sea drilling wells and a small fracture that was on its western side. Thethree fults are directed from Northwest to the Southeast. Fault is expected to serve as ahydrocarbon trap in the area that will be accumulated in drilling wells.Keywords: geometrical attribute, Seismic data, drilling wells, time structure map.

Estimating the boundary surface between geologic formations from 3D seismic data using neural networks and geostatistics

Geophysics ◽  
2005 ◽  
Vol 70 (1) ◽  
pp. P1-P11 ◽  
Author(s):  
Peter A. Dowd ◽  
Eulogio Pardo-Igúzquiza
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Seismic Data ◽  
Acoustic Impedance ◽  
Boundary Surface ◽  
Boundary Detection ◽  
Geostatistical Simulation ◽  
3D Seismic ◽  
Filter Noise ◽  
3D Seismic Data

The exact locations of horizons that separate geologic sequences are known only at physically sampled locations (e.g., borehole intersections), which, in general, are very sparse. 3D seismic data, on the other hand, provide complete coverage of a volume of interest with the possibility of detecting the boundaries between formations with, for example, contrasted acoustic impedance. Detection of boundaries is hampered, however, by coarse spatial resolution of the seismic data, together with local variability of acoustic impedance within formations. The authors propose a two-part approach to the problem, using neural networks and geostatistics. First, an artificial neural network is used for boundary detection. The training set for the neural net comprises seismic traces that are collocated with the borehole locations. Once the net is trained, it is applied to the entire seismic grid. Second, output from the neural network is processed geostatistically to filter noise and to assess the uncertainty of the boundary locations. A physical counterpart is interpreted for each structure inferred from the spatial semivariogram. Factorial kriging is used for filtering, and uncertainty in the shape of the boundaries is assessed by geostatistical simulation. In this approach, the boundary locations are interpreted as random functions that can be simulated to incorporate their uncertainty in applications. A case study of boundary detection between sandstone and breccia formations in a highly faulted zone is used to illustrate the methodologies.

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