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

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.

Practical aspects of working with 2D migrated seismic data

2019 ◽  
Vol 7 (3) ◽  
pp. SG1-SG9
Author(s):  
Donald A. Herron ◽  
Timothy E. Smith
Keyword(s):  
Seismic Data ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Essential Elements ◽  
3D Seismic ◽  
Gas Industry ◽  
Out Of Plane ◽  
Exploration And Production ◽  
3D Seismic Data ◽  
Fundamental Limitation

Despite the ever-increasing use of 3D seismic data in today’s exploration and production activities, 2D seismic data continue to play an important role in the oil and gas industry. Interpretations of 2D regional and megaregional surveys are essential elements of integrated exploration programs, establishing frameworks for basin analysis, structural synthesis, and play fairway identification and mapping. When correlating and mapping horizons on 2D migrated seismic data, interpreters use certain practical techniques for handling structural misties, which are caused by the fundamental limitation of 2D migration to account for out-of-plane components of dip.

From exploration to production—key trends and differences in the exploration and development of hydrocarbons from Australia's established offshore petroleum provinces

2015 ◽  
Vol 55 (2) ◽  
pp. 492
Author(s):  
Howard Ewan ◽  
Frances Cullen
Keyword(s):  
Seismic Data ◽  
3D Seismic ◽  
Considerable Success ◽  
Early Success ◽  
Offshore Oil Production ◽  
Exploration And Production ◽  
History Of ◽  
Offshore Oil ◽  
3D Seismic Data ◽  
Opening Up

Since peaking at more than 200 MMbbl in 2000, offshore oil production in Australia has declined steadily by falling to below 100 MMbbl in 2011. Various initiatives and policies have been introduced by the Australian Government to encourage exploration of frontier basins to find a new oil province that may stop this production decline. By analysing the exploration and production profiles of established petroleum provinces in offshore Australia, the reasons behind similarities, differences and trends can be understood. This knowledge can be applied to future exploration efforts and provides a variety of possible outcomes should success come in immature and frontier provinces. Analysis of data mainly from the Bonaparte, Browse, Gippsland, North Carnarvon and, to a lesser extent, the Otway and Perth basins, shows that early success typifies these petroleum provinces. The degree of early success is, however, not always a good indication of later exploration results. While the creaming curves of some basins conform to the hypothetical basin creaming curve, characterised by large initial reserves increases and falling discovery size across time, it is evident that this cannot be universally applied. Considerable success much later in the exploration history of a basin is demonstrated, by success driven policy implementation, exploration incentivisation and increases in data acquisition. In this extended abstract, the importance of using advances in technology is demonstrated by reserves additions following the opening up of deepwater areas and the increased use of high-density 2D seismic and 3D seismic data. This information supports the notion that frontier areas may hold the key to alleviating dependence on foreign supply, success in immature basins is still possible and should not be overlooked.

A comparison of classification techniques for seismic facies recognition

2015 ◽  
Vol 3 (4) ◽  
pp. SAE29-SAE58 ◽  
Author(s):  
Tao Zhao ◽  
Vikram Jayaram ◽  
Atish Roy ◽  
Kurt J. Marfurt
Keyword(s):  
Seismic Data ◽  
Gaussian Mixture ◽  
Seismic Facies ◽  
Support Vector ◽  
3D Seismic ◽  
Generative Topographic Mapping ◽  
Learning Methods ◽  
Facies Classification ◽  
Data Volume ◽  
3D Seismic Data

During the past decade, the size of 3D seismic data volumes and the number of seismic attributes have increased to the extent that it is difficult, if not impossible, for interpreters to examine every seismic line and time slice. To address this problem, several seismic facies classification algorithms including [Formula: see text]-means, self-organizing maps, generative topographic mapping, support vector machines, Gaussian mixture models, and artificial neural networks have been successfully used to extract features of geologic interest from multiple volumes. Although well documented in the literature, the terminology and complexity of these algorithms may bewilder the average seismic interpreter, and few papers have applied these competing methods to the same data volume. We have reviewed six commonly used algorithms and applied them to a single 3D seismic data volume acquired over the Canterbury Basin, offshore New Zealand, where one of the main objectives was to differentiate the architectural elements of a turbidite system. Not surprisingly, the most important parameter in this analysis was the choice of the correct input attributes, which in turn depended on careful pattern recognition by the interpreter. We found that supervised learning methods provided accurate estimates of the desired seismic facies, whereas unsupervised learning methods also highlighted features that might otherwise be overlooked.

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