Characterizing a Mississippian tripolitic chert reservoir using 3D unsupervised and supervised multiattribute seismic facies analysis: An example from Osage County, Oklahoma

2013 ◽  
Vol 1 (2) ◽  
pp. SB109-SB124 ◽  
Author(s):  
Atish Roy ◽  
Benjamin L. Dowdell ◽  
Kurt J. Marfurt

Seismic interpretation is based on the identification of reflector configuration and continuity, with coherent reflectors having a distinct amplitude, frequency, and phase. Skilled interpreters may classify reflector configurations as parallel, converging, truncated, or hummocky, and use their expertise to identify stratigraphic packages and unconformities. In principal, a given pattern can be explicitly defined as a combination of waveform and reflector configuration properties, although such “clustering” is often done subconsciously. Computer-assisted classification of seismic attribute volumes builds on the same concepts. Seismic attributes not only quantify characteristics of the seismic reflection events, but also measure aspects of reflector configurations. The Mississippi Lime resource play of northern Oklahoma and southern Kansas provides a particularly challenging problem. Instead of defining the facies stratigraphically, we need to define them either diagenetically (tight limestone, stratified limestone and nonporous chert, and highly porous tripolitic chert) or structurally (fractured versus unfractured chert and limestone). Using a 3D seismic survey acquired in Osage County Oklahoma, we use Kohonen self-organizing maps to classify different diagenetically altered facies of the Mississippi Lime play. The 256 prototype vectors (potential clusters) reduce to only three or four distinct “natural” clusters. We use ground truth of seismic facies seen on horizontal image logs to fix three average attribute data vectors near the well locations, resulting in three “known” facies, and do a minimum Euclidean distance supervised classification. The predicted clusters correlate well to the poststack impedance inversion result.

GeoArabia ◽  
2008 ◽  
Vol 13 (1) ◽  
pp. 15-34
Author(s):  
Costas G. Macrides ◽  
Fernando A. Neves

ABSTRACT In 2002, Saudi Aramco conducted its first 3D, 4-component (4C) ocean-bottom cable (OBC) seismic survey in the Arabian Gulf. The main objective was to delineate the middle Cretaceous Upper Khafji Sand Stringers Reservoir overlying the massive Main Khafji Sand Reservoir in the Zuluf field. The Upper Khafji Sand Stringers Reservoir in the Wasia Formation is typically characterized by weak acoustic impedance contrasts. A pre-survey modeling study, based on the logs of compressional (P) and shear-wave (S) velocities (Vp and Vs), indicated that converted compressional-to-shear waves (P-S) could better-image the structure and stratigraphy of the target reservoir. Commensurate with the objectives of the experiment, a pilot 100-square-kilometer survey was acquired with an inline swath-shooting geometry that employed two seabed receiver cables, with a symmetric split-spread deployment of the 4-C sensors. The acquisition geometry consisted of six sail lines per swath with a single-boat, dual-source, flip-flop configuration. The data were processed through dual-sensor summation, horizontal-component rotation and P-P/P-S pre-stack time migration. Post-stack enhancement using non-stationary Gabor deconvolution proved beneficial in compensating for the missing high frequencies in the acquired converted-wave data. Well-to-seismic calibration for both P-P and P-S data at five wells aided in the interpretation of the data. Five horizons were interpreted and correlated between the P-P and P-S sections. The horizons were analyzed using both amplitude and interval times such that the lateral variations of the Vp/Vs ratio of the Upper Khafji Sand Stringers Reservoir could be mapped. A region of low Vp/Vs ratios in the northwest quadrant, obtained from the isochron interval-time analysis, was correlated with higher ‘net sand’ pay at a hidden well located in the middle of this region. These results were further corroborated by seismic facies analysis and provide a qualitative reservoir quality index in the Upper Khafji Sand Stringers Reservoir.


Geophysics ◽  
2012 ◽  
Vol 77 (4) ◽  
pp. O45-O53 ◽  
Author(s):  
Puneet Saraswat ◽  
Mrinal K. Sen

Seismic facies, combined with well-log data and other seismic attributes such as coherency, curvature, and AVO, play an important role in subsurface geological studies, especially for identification of depositional structures. The effectiveness of any seismic facies analysis algorithm depends on whether or not it is driven by local geologic factors, the absence of which may lead to unrealistic information about subsurface geology, depositional environment, and lithology. This includes proper identification of number of classes or facies existing in the data set. We developed a hybrid waveform classification algorithm based on an artificial immune system and self-organizing maps (AI-SOM), that forms the class of unsupervised classification or automatic facies identification followed by facies map generation. The advantage of AI-SOM is that, unlike, a stand-alone SOM, it is more robust in the presence of noise in seismic data. Artificial immune system (AIS) is an excellent data reduction technique providing a compact representation of the training data; this is followed by clustering and identification of number of clusters in the data set. The reduced data set from AIS processing serves as an excellent input to SOM processing. Thus, facies maps generated from AI-SOM are less affected by noise and redundancy in the data set. We tested the effectiveness of our algorithm with application to an offshore 3D seismic volume from F3 block in the Netherlands. The results confirmed that we can better interpret an appropriate number of facies in the seismic data using the AI-SOM approach than with a conventional SOM. We also examined the powerful data-reduction capabilities of AIS and advantages the of AI-SOM over SOM when data under consideration were noisy and redundant.


Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. A39-A43 ◽  
Author(s):  
Feng Qian ◽  
Miao Yin ◽  
Xiao-Yang Liu ◽  
Yao-Jun Wang ◽  
Cai Lu ◽  
...  

One of the most important goals of seismic stratigraphy studies is to interpret the elements of the seismic facies with respect to the geologic environment. Prestack seismic data carry rich information that can help us get higher resolution and more accurate facies maps. Therefore, it is promising to use prestack seismic data for the seismic facies recognition task. However, because each identified object changes from the poststack trace vectors to a prestack trace matrix, effective feature extraction becomes more challenging. We have developed a novel data-driven offset-temporal feature extraction approach using the deep convolutional autoencoder (DCAE). As an unsupervised deep learning method, DCAE learns nonlinear, discriminant, and invariant features from unlabeled data. Then, seismic facies analysis can be accomplished through the use of conventional classification or clustering techniques (e.g., K-means or self-organizing maps). Using a physical model and field prestack seismic surveys, we comprehensively determine the effectiveness of our scheme. Our results indicate that DCAE provides a much higher resolution than the conventional methods and offers the potential to significantly highlight stratigraphic and depositional information.


2020 ◽  
Vol 8 (2) ◽  
pp. T293-T307
Author(s):  
José N. Méndez ◽  
Qiang Jin ◽  
María González ◽  
Wei Hehua ◽  
Cyril D. Boateng

Karsted carbonates of the Ordovician Yingshan Formation represent significant hydrocarbon reservoirs in the Tarim Basin, China. Due to the geologic complexity of the formation, realistically predicting and modeling karst zones and rock properties is challenging. This drives the need to apply diverse techniques for building a suitable geologic model. We have developed a static model approach that uses fully automated seismic facies classification processes for predicting and modeling patterns associated with karst elements. Our method uses a seismic attribute and well logs as input data. We initially processed a seismic facies volume using the hierarchical clustering technique. This is based on seismic attribute values that take into account an optimal number of classes. The outcome reveals various patterns illustrated with low amplitudes highlighting the geomorphology of paleokarst elements. Simultaneously, a seismic traces map of the karsted interval was processed using the hybrid clustering technique conducted on seismic trace shape. In this case, the karst facies was extracted from the output and used as secondary input data in trend analysis of the model. Both outputs obtained from clustering techniques are processed in a volume of the most probable facies, which delineate the karst patterns. The results of the modeling process are visualized in various time slices and cross sections, appropriately recognizing the relationship of estimated patterns with karst zones. We have evaluated the karstification thickness and porosity map obtained from the 3D model that detail a reasonable connectivity between karst elements. This is based on the paleogeographic location and type of filling, as well as the dissolution development along the main striking faults. Finally, our method outputs a logical model of karst zones located within the host rock, which reduces the uncertainty and identify nonperforated segments.


Geophysics ◽  
2007 ◽  
Vol 72 (1) ◽  
pp. P9-P21 ◽  
Author(s):  
Marcílio Castro de Matos ◽  
Paulo Léo Osorio ◽  
Paulo Roberto Johann

Unsupervised seismic facies analysis provides an effective way to estimate reservoir properties by combining different seismic attributes through pattern recognition algorithms. However, without consistent geological information, parameters such as the number of facies and even the input seismic attributes are usually chosen in an empirical way. In this context, we propose two new semiautomatic alternative methods. In the first one, we use the clustering of the Kohonen self-organizing maps (SOMs) as a new way to build seismic facies maps and to estimate the number of seismic facies. In the second method, we use wavelet transforms to identify seismic trace singularities in each geologically oriented segment, and then we build the seismic facies map using the clustering of the SOM. We tested both methods using synthetic and real seismic data from the Namorado deepwater giant oilfield in Campos Basin, offshore Brazil. The results confirm that we can estimate the appropriate number of seismic facies through the clustering of the SOM. We also showed that we can improve the seismic facies analysis by using trace singularities detected by the wavelet transform technique. This workflow presents the advantage of being less sensitive to horizon interpretation errors, thus resulting in an improved seismic facies analysis.


2016 ◽  
Vol 4 (1) ◽  
pp. SB79-SB89 ◽  
Author(s):  
Tao Zhao ◽  
Jing Zhang ◽  
Fangyu Li ◽  
Kurt J. Marfurt

Recent developments in seismic attributes and seismic facies classification techniques have greatly enhanced the capability of interpreters to delineate and characterize features that are not prominent in conventional 3D seismic amplitude volumes. The use of appropriate seismic attributes that quantify the characteristics of different geologic facies can accelerate and partially automate the interpretation process. Self-organizing maps (SOMs) are a popular seismic facies classification tool that extract similar patterns embedded with multiple seismic attribute volumes. By preserving the distance in the input data space into the SOM latent space, the internal relation among data vectors on an SOM facies map is better presented, resulting in a more reliable classification. We have determined the effectiveness of the modified algorithm by applying it to a turbidite system in Canterbury Basin, offshore New Zealand. By incorporating seismic attributes and distance-preserving SOM classification, we were able to observe architectural elements that are overlooked when using a conventional seismic amplitude volume for interpretation.


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