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.

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.

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.   

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.

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 Unsupervised machine learning for time-lapse seismic studies and reservoir monitoring

2021 ◽  
pp. 1-59
Author(s):  
Marwa Hussein ◽  
Robert R. Stewart ◽  
Deborah Sacrey ◽  
David H. Johnston ◽  
Jonny Wu
Keyword(s):  
Machine Learning ◽  
Reservoir Simulation ◽  
Seismic Data ◽  
Water Saturation ◽  
Seismic Analysis ◽  
Time Lapse ◽  
Seismic Attributes ◽  
Rock Properties ◽  
Unsupervised Machine Learning ◽  
4D Seismic

Time-lapse (4D) seismic analysis plays a vital role in reservoir management and reservoir simulation model updates. However, 4D seismic data are subject to interference and tuning effects. Being able to resolve and monitor thin reservoirs of different quality can aid in optimizing infill drilling or locating bypassed hydrocarbons. Using 4D seismic data from the Maui field in the offshore Taranaki basin of New Zealand, we generate typical seismic attributes sensitive to reservoir thickness and rock properties. We find that spectral instantaneous attributes extracted from time-lapse seismic data illuminate more detailed reservoir features compared to those same attributes computed on broadband seismic data. We develop an unsupervised machine learning workflow that enables us to combine eight spectral instantaneous seismic attributes into single classification volumes for the baseline and monitor surveys using self-organizing maps (SOM). Changes in the SOM natural clusters between the baseline and monitor surveys suggest production-related changes that are caused primarily by water replacing gas as the reservoir is being swept under a strong water drive. The classification volumes also facilitate monitoring water saturation changes within thin reservoirs (ranging from very good to poor quality) as well as illuminating thin baffles. Thus, these SOM classification volumes show internal reservoir heterogeneity that can be incorporated into reservoir simulation models. Using meaningful SOM clusters, geobodies are generated for the baseline and monitor SOM classifications. The recoverable gas reserves for those geobodies are then computed and compared to production data. The SOM classifications of the Maui 4D seismic data seems to be sensitive to water saturation change and subtle pressure depletions due to gas production under a strong water drive.

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