Reservoir characterization using microseismic facies analysis integrated with surface seismic attributes

2016 ◽  
Vol 4 (2) ◽  
pp. T167-T181 ◽  
Author(s):  
Aamir Rafiq ◽  
David W. Eaton ◽  
Adrienne McDougall ◽  
Per Kent Pedersen
Keyword(s):  
Seismic Data ◽  
Principal Curvature ◽  
Facies Analysis ◽  
Shape Index ◽  
Seismic Attributes ◽  
Seismic Survey ◽  
3D Seismic ◽  
Frequency Distributions ◽  
Scaling Properties ◽  
3D Seismic Data

We have developed the concept of microseismic facies analysis, a method that facilitates partitioning of an unconventional reservoir into distinct facies units on the basis of their microseismic response along with integrated interpretation of microseismic observations with 3D seismic data. It is based upon proposed links between magnitude-frequency distributions and scaling properties of reservoirs, including the effects of mechanical bed thickness and stress heterogeneity. We evaluated the method using data from hydraulic fracture monitoring of a Late Cretaceous tight sand reservoir in central Alberta, in which microseismic facies can be correlated with surface seismic attributes (primarily principal curvature, coherence, and shape index) from a coincident 3D seismic survey. Facies zones are evident on the basis of attribute crossplots, such as maximum moment release rate versus cluster azimuth. The microseismically defined facies correlate well with principal curvature anomalies from 3D seismic data. By combining microseismic facies analysis with regional trends derived from log and core data, we delineate reservoir partitions that appear to reflect structural and depositional trends.

scholarly journals APPLYING FILTERS AND SEISMIC ATTRIBUTES FOR ENHANCING FAULTS IN THE 3D SEISMIC SURVEY OF ALTO DE SIRIRIZINHO (SERGIPE-ALAGOAS BASIN, NORTHEAST BRAZIL)

2013 ◽  
Vol 31 (1) ◽  
pp. 109
Author(s):  
Arthur Victor Medeiros Francelino ◽  
Alex Francisco Antunes
Keyword(s):  
Seismic Data ◽  
Median Filter ◽  
Random Noise ◽  
Seismic Attributes ◽  
Oil Fields ◽  
Seismic Survey ◽  
Northeast Brazil ◽  
3D Seismic ◽  
3D Seismic Data

The 3D seismic data allow that mature oil fields be reevaluated in order to improve the characterization of faults that affect the flow of hydrocarbons. The use of seismic attributes and filtering allows an improvement in the identification and enhancement of these fractures on seismic data. In this study, we used two different filters: the dip-steered median filter to remove random noise and increase the lateral continuity of reflections, and the fault-enhancement filter used to enhance the discontinuities of the reflections. After filtering, similarity and curvature attributes were applied in order to identify the distribution of fractures along the data. Theuse of these attributes and filters contributed greatly to the identification and enhancement of the continuity of the fractures. RESUMO: Com o advento da sísmica 3D, campos de petróleo maduros podem ser reavaliados melhorando a caracterização das falhas que influenciam o fluxo de hidrocarbonetos. A utilização de filtragens e atributos sísmicos possibilita uma melhora na identificação e no realce dessas fraturas no dado sísmico. No presente trabalho foram utilizados dois tipos de filtros, sendo o dip-steered median filter, com a finalidade de retirar os ruídos aleatórios e aumentar a continuidade lateral das reflexões, e o fault-enhancement filter para realçar as descontinuidades das reflexões. Após a etapa de filtragem foram aplicados os atributos de similaridade e curvatura, para se identificar a distribuição das falhas. O uso dos atributos e filtragens colaborou fortemente para a identificação e o realce da continuidade das fraturas. Palavras-chave: reservatório fraturado; interpretação sísmica e atributos sísmicos

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.

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.   

Imposing interpretational constraints on a seismic interpretation CNN

Geophysics ◽  
2021 ◽  
pp. 1-36
Author(s):  
Haibin Di ◽  
Cen Li ◽  
Stewart Smith ◽  
Zhun Li ◽  
Aria Abubakar
Keyword(s):  
Seismic Data ◽  
Recent Progress ◽  
Facies Analysis ◽  
Three Dimensional ◽  
Seismic Interpretation ◽  
3D Seismic ◽  
Geologic Time ◽  
Seismic Amplitude ◽  
Subsurface Geology ◽  
3D Seismic Data

With the expanding size of three-dimensional (3D) seismic data, manual seismic interpretation becomes time consuming and labor intensive. For automating this process, the recent progress in machine learning, particularly the convolutional neural networks (CNNs), has been introduced into the seismic community and successfully implemented for interpreting seismic structural and stratigraphic features. In principle, such automation aims at mimicking the intelligence of experienced seismic interpreters to annotate subsurface geology both accurately and efficiently. However, most of the implementations and applications are relatively simple in their CNN architectures, which primary rely on the seismic amplitude but undesirably fail to fully use the pre-known geologic knowledge and/or solid interpretational rules of an experienced interpreter who works on the same task. A general applicable framework is proposed for integrating a seismic interpretation CNN with such commonly-used knowledge and rules as constraints. Three example use cases, including relative geologic time-guided facies analysis, layer-customized fault detection, and fault-oriented stratigraphy mapping, are provided for both illustrating how one or more constraints can be technically imposed and demonstrating what added values such a constrained CNN can bring. It is concluded that the imposition of interpretational constraints is capable of improving CNN-assisted seismic interpretation and better assisting the tasks of subsurface mapping and modeling.

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.

Why 3D seismic data are an asset for exploration and mine planning? Velocity tomography of weakness zones in the Kevitsa Ni-Cu-PGE mine, northern Finland

Geophysics ◽  
2018 ◽  
Vol 83 (2) ◽  
pp. B33-B46 ◽  
Author(s):  
Alireza Malehmir ◽  
Ari Tryggvason ◽  
Chris Wijns ◽  
Emilia Koivisto ◽  
Teemu Lindqvist ◽  
...  
Keyword(s):  
Seismic Data ◽  
Mine Planning ◽  
Seismic Survey ◽  
Open Pit ◽  
3D Seismic ◽  
Reflection Data ◽  
Northern Side ◽  
Weakness Zone ◽  
Weakness Zones ◽  
3D Seismic Data

Kevitsa is a disseminated Ni-Cu-PGE (platinum group elements) ore body in northern Finland, hosted by an extremely high-velocity ([Formula: see text]) ultramafic intrusion. It is currently being mined at a depth of approximately 100 m with open-pit mining. The estimated mine life is 20 years, with the final pit reaching a depth of 500–600 m. Based on a series of 2D seismic surveys and given the expected mine life, a high-resolution 3D seismic survey was justified and conducted in the winter of 2010. We evaluate earlier 3D reflection data processing results and complement that by the results of 3D first-arrival traveltime tomography. The combined results provide insights on the nature of some of the reflectors within the intrusion. In particular, a major discontinuity, a weakness zone, is delineated in the tomography results on the northern side of the planned pit. Supported by the reflection data, we estimate the discontinuity, likely a thrust sheet, to extend down approximately 600 m and laterally 1000 m. The weakness zone terminates prominent internal reflectivity of the Kevitsa intrusion, and it is associated with the extent of the economic mineralization. Together with other weakness zones, a couple of which are also revealed by the tomography study, the discontinuity forms a major wedge block that influences the mine bench stability on the northern side of the open pit and likely will cause more issues during the extraction of the ore in this part of the mine. We argue that 3D seismic data should routinely be acquired prior to commencement of mining activities to maximize exploration efficiency at depth and also to optimize mining as it continues toward depth. Three-dimensional seismic data over mineral exploration areas are valuable and can be revisited for different purposes but are difficult to impossible to acquire after mining has commenced.

Improved structural interpretation of legacy 3D seismic data from Karee platinum mine (South Africa) through the application of novel seismic attributes

2019 ◽  
Vol 68 (1) ◽  
pp. 145-163 ◽  
Author(s):  
Musa S.D. Manzi ◽  
Gordon R.J. Cooper ◽  
Alireza Malehmir ◽  
Raymond J. Durrheim
Keyword(s):  
South Africa ◽  
Seismic Data ◽  
Seismic Attributes ◽  
3D Seismic ◽  
Structural Interpretation ◽  
3D Seismic Data

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.

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