Tao Zhao and Kurt J. Marfurt University of Oklahoma Attribute Assisted Seismic Facies Classification on a Turbidite System in Canterbury Basin, Offshore New Zealand Tao Zhao and Kurt J. Marfurt University of Oklahoma

Outline Introduction Review of classification techniques Data description Preliminary facies classification Calibration with geologic feature Conclusions Acknowledgements 2

Outline Introduction Review of classification techniques Data description Preliminary facies classification Calibration with geologic feature Conclusions Acknowledgements 3

Introduction Great data complexity Limited prior knowledge Problems Great data complexity Limited prior knowledge Enormous data size High dimensionality In modern seismic interpretation, one critical problem is the great data complexity, which has two aspects. First is the amount of data. It’s really common to have a poststack seismic volume over 10 Gigs, and the size increases with the number of attributes. Such giant data size makes conventional line-by-line interpretation extremely tedious. The second aspect of data complexity is the high dimensionality. Each generated attribute contributes one dimension, and each type of well log is also a dimension. When analyzing such data together, it becomes very difficult to even visualize the data. We can co-render three attributes if choose color scheme carefully, but it’s nearly impossible to visualize five or six attributes in one screen. The other problem, specifically in this study, is the limitation of prior knowledge. Being an very recent and highly under-explored prospect, there are no well logs that we have access to in the study area, and published studies are very limited. The only data we have is a poststack seismic volume, which means all the subsequent studies carried out are purely based on seismic data. Lack of well control Very limited previous studies 4

Introduction Can we perform an interpretation using only seismic data? Motivations Can we perform an interpretation using only seismic data? Can we use seismic facies classification techniques to assist such interpretation? Can we trust such facies? Here are the motivations of this study. Given the availability of the data, naturally we would like to see if we can do a relative reliable interpretation using only seismic data. More specifically, we wanted to see if we can perform the interpretation by using seismic facies classification techniques. And in order to make sure the interpretation makes sense, we need to answer this question: can we trust such facies? 5

Introduction Workflow of seismic facies classification Classification as applied to the interpretation of seismic facies (modified from Duda et al., 2001) 6

Outline Introduction Review of classification techniques Data description Preliminary facies classification Calibration with geologic feature Conclusions Acknowledgements 7

Review of Classification Techniques k-means a) b) c) d) Kmeans is probably the most basic classification technique, so I want to use kmeans as an example to show how we define different classes in seismic data. If we want to find 3 classes using two seismic attributes, first we initialize three seed points representing three classes randomly in the 2D space defined by the two attributes. Then for every data sample point, we calculate its distance to all three seed points, and assign the sample points to the class of the nearest seed points, and move the seed points to the center of each class. Then repeat the process again. After certain number of iterations, the model will become stable. And if a new data point is given, it will be classified to the nearest class. Steps in k-means (figure courtesy of Scott Pickford) 8

Review of Classification Techniques Projection techniques 3D input space k-means   a) c) d) b) PCA SOM, GTM We can also use projection techniques to solve seismic facies classification problems. If we have three input attributes, k-means will do the clustering in this 3D space. If we use principle component analysis (PCA), we can project the data into a lower dimensional space, usually 2D, defined by the first two principle components. This PCA projection can serve as the initial model for SOM and GTM algorithms. SOM and GTM deform the initial 2D plane into a 2D manifold that better fits the data. Clusters can be formed in such a deformed 2D manifold, and then color coded to generate a seismic facies map. Differences in k-means and projection techniques 9

Outline Introduction Review of classification techniques Data description Preliminary facies classification Calibration with geologic feature Conclusions Acknowledgements 10

Data Description Canterbury Basin, offshore New Zealand Waka-3D 170° 30’ E 173° 00’ E 45° 30’ S 46° 30’ S Our study area is located in the Canterbury Basin, offshore New Zealand. This is the boundary of the seismic survey, and in this study we are more interested in this smaller area. The colors represent the current sea floor canyons, which are good analogs for the paleocanyons that your are about to see. (Modified from Mitchell and Neil, 2012) (Figure by Origin Energy) 11

Data Description Seismic amplitude Here is a time slice at the approximate level of a Miocene age turbidite system. On seismic amplitude, we can identify two channels, some high energy reflectors, and some gently dipping reflectors. If we pick a horizon that better represents a constant geologic time, 12

Data Description Seismic amplitude We can identify another sinuous channel complex. However, the morphology is not well defined using only seismic amplitude data. So let’s run some seismic attributes that highlight these features. 13

Data Description Peak spectral frequency peak spectral magnitude Here is the co-rendered peak spectral frequency with peak spectral magnitude that emphasize the relative thickness and reflectivity of the channel complexes and surrounding slope fan sediments. And we use Sobel filter similarity to delineate the edges. Now the channel morphology is defined much more clear than in the seismic amplitude image. 14

Data Description Shape index and curvedness Here is the co-rendered shape index and curvedness. And the edges are also defined using Sobel filter similarity. This image highlights incisement, channel flanks, and levees. saddle 15

Data Description GLCM homogeneity Coherent energy And here is the co-rendered GLCM homogeneity and coherent energy. In this image, we can see the strong contrast in these two parts, and we interpret the higher energy reflector to be a sand-filled channel that cut through by the younger lower energy channel. We can also see the slope fan deposits are more coherent, and the inter-channel deposits are more chaotic. 16

Data Description Seismic data co-rendered with attributes Here are just the vertical sections of the previous images. 17

Outline Introduction Review of classification techniques Data description Preliminary facies classification Calibration with geologic feature Conclusions Acknowledgements Now let’s check the seismic facies maps from different classification techniques. 18

Preliminary Facies Classification k-means clustering This is the result from k-means. It provides basic separation of different facies, but is insufficient in details. Now, let’s get fancier! 19

Preliminary Facies Classification Principle component analysis (PCA) This is a crossplot between first two principal components using a 2D colorbar. Because it’s a projection of the original seismic attributes, the image is more smooth and more interpreter friendly. These two principal components serve as the initial model for the SOM and GTM images in the next two slides. 20

Preliminary Facies Classification Self-organizing map (SOM) (distance preserving) On the SOM facies map, we identify two main slope channels (white arrows) which are classified as cyan that converge downstream. Vertical slices show these both to be multistory channels. As the channels move downslope, the slope becomes gentler, such that sediments lose momentum, spread out, and form a lobate feature. Black arrows indicate several sinuous channel complexes. Most of the channel fills appear as cyan, similar to the two main channels, which suggests they are probably mud filled. The coherent slope fans (indicated by red arrows) are characterized by brownish colors. The purplish color facies are less coherent and may indicate massive turbidite current or slump deposits. Blue arrows indicate a facies that we interpreted to be an older, high energy, sand filled channel developed earlier than the mud filled channel cutting through it. This sand-filled channel spreads out and contributes to the lobe further downslope where it is covered by mud deposits transported by later stage channels. 21

Preliminary Facies Classification Self-organizing map (SOM) (traditional Kohonen) On the SOM facies map, we identify two main slope channels (white arrows) which are classified as cyan that converge downstream. Vertical slices show these both to be multistory channels. As the channels move downslope, the slope becomes gentler, such that sediments lose momentum, spread out, and form a lobate feature. Black arrows indicate several sinuous channel complexes. Most of the channel fills appear as cyan, similar to the two main channels, which suggests they are probably mud filled. The coherent slope fans (indicated by red arrows) are characterized by brownish colors. The purplish color facies are less coherent and may indicate massive turbidite current or slump deposits. Blue arrows indicate a facies that we interpreted to be an older, high energy, sand filled channel developed earlier than the mud filled channel cutting through it. This sand-filled channel spreads out and contributes to the lobe further downslope where it is covered by mud deposits transported by later stage channels. 22

Preliminary Facies Classification Generative topographic mapping (GTM) Here is the facies map from generative topographic mapping. Theoretically, GTM should provide a better result than SOM, however, in this specific application, we find any of these two facies maps provides sufficient information to interpret. And because SOM is more widely available to the interpretation community, we choose SOM as our example to show the detailed interpretations. 23

Outline Introduction Review of classification techniques Data description Preliminary facies classification Calibration with geologic feature Conclusions Acknowledgements Now let’s calibrate the SOM facies map with geologic features. 24

Calibration with Geologic Feature 1.8 N 2D histogram 5 km Time (s) SOM latent axis 2 2.1 Similarity 0.3 1 100% Opacity (b) 1.7 (a) SOM latent axis 1 Time (s) 2D colorbar (b) 2.0 SOM latent axis 2 (d) 1.7 Amplitude − + 100% Opacity (c) Time (s) SOM latent axis 1 (c) 2.0 1.8 (e) (d) (f) Time (s) Because seismic is the only data we have, we want to look for evidence for the previous interpretation on vertical seismic amplitude sections. Section A shows the northern channel complex. We use red line to outline the channels. In this vertical section, Horizon A is at the middle of the SOM facies window. We can find channels are mapped in cyan to purple colors, while the surrounding overbank complex deposits mapped in yellow to brown colors. In section B, we can see the lateral migration of this channel complex. Here are at least four channel stories migrating from northeast to southwest. We can also see the oldest story is mapped as lime green, while the younger channels are cyan. This suggests a change in grain size during deposition. The oldest story is sand-filled, and the younger stories are mud-filled. The more distal part of this channel is more spread out, forming a lobe mixed with the other main channel. We also see an oxbow-like feature in the older deposits. At last, for the two twisted channels, we can clearly see the red channel cutting through the older sand-filled channel in lime green. Similar to the northern main channel, we see the width of these two channels has expanded dramatically from proximal to distal, merging into a lobate feature. Inline 2.1 Crossline (e) (f) 1.7 1.7 Time (s) Time (s) 2.0 Horizon A 2.0 25

Calibration with Geologic Feature To better build the link from seismic attributes to different facies, we now see the attribute responses of four key facies. All attribute are normalized using z-score. As expected, similar facies (colors) on the SOM map have similar attribute responses. We see the sample vector from the sand-filled channel deposits, has a similar response to that of facies 3, which is the sandy overbank complexes. The inter-channel overbank complex and the mud-filled sinuous channel complex are in similar facies (blue to purple colors). We can also see the difference in seismic amplitudes of the multistoried channel (more chaotic) and older sand filled channel (more flat and higher amplitude). 26

Outline Introduction Review of classification techniques Data description Preliminary facies classification Calibration with geologic feature Conclusions Acknowledgements 27

Conclusions In this study, we demonstrated the feasibility to characterize the architectural elements in a deep water turbidite system in Canterbury basin, New Zealand, by incorporating seismic attributes, seismic facies classification, and a limited amount of structural interpretation (picking one horizon). Seismic facies classification algorithms provide statistics based machine assistance to identify depositional facies in a turbidite system. The generated seismic facies can be calibrated with geologic features. Multiple sinuous channel complexes and multistory channel stacking pattern are delineated precisely along with their depositional pattern, which can be further utilized to locate potential reservoirs. 28

Outline Introduction Review of classification techniques Data description Preliminary facies classification Calibration with geologic feature Conclusions Acknowledgements 29

Acknowledgements We thank New Zealand Petroleum and Minerals for providing the Waka-3D seismic data to the public. Financial support for this effort and for the development of our own k-means, SOM, GTM, and SVM algorithms was provided by the industry sponsors of the Attribute-Assisted Seismic Processing and Interpretation (AASPI) consortium at the University of Oklahoma. We thank colleagues Jing Zhang, Fangyu Li, Sumit Verma, Lennon Infante, and Brad Wallet for their valuable input and suggestions. All the 3D seismic displays were made using licenses to Petrel, provided to the University of Oklahoma for research and education courtesy of Schlumberger. Finally, we want to express our great respect to the people that have contributed the development of pattern recognition techniques in exploration geophysics. 30

Questions and suggestions? THANKS Questions and suggestions? 31