Predicting and 3D modeling of karst zones using seismic facies analysis in Ordovician carbonates of the Tahe oilfield, China

2020 ◽  
Vol 8 (2) ◽  
pp. T293-T307
Author(s):  
José N. Méndez ◽  
Qiang Jin ◽  
María González ◽  
Wei Hehua ◽  
Cyril D. Boateng
Keyword(s):  
Cross Sections ◽  
Input Data ◽  
Optimal Number ◽  
Seismic Facies ◽  
Rock Properties ◽  
Seismic Attribute ◽  
Clustering Technique ◽  
Yingshan Formation ◽  
Facies Classification ◽  
Seismic Facies Analysis

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.

Quantifying the sensitivity of seismic facies classification to seismic attribute selection: An explainable machine learning study

2022 ◽  
pp. 1-90
Author(s):  
David Lubo-Robles ◽  
Deepak Devegowda ◽  
Vikram Jayaram ◽  
Heather Bedle ◽  
Kurt J. Marfurt ◽  
...  
Keyword(s):  
Machine Learning ◽  
Input Data ◽  
Global Analysis ◽  
Random Noise ◽  
Seismic Attributes ◽  
Attribute Selection ◽  
Seismic Facies ◽  
Attribute Importance ◽  
Seismic Attribute ◽  
Facies Classification

During the past two decades, geoscientists have used machine learning to produce a more quantitative reservoir characterization and to discover hidden patterns in their data. However, as the complexity of these models increase, the sensitivity of their results to the choice of the input data becomes more challenging. Measuring how the model uses the input data to perform either a classification or regression task provides an understanding of the data-to-geology relationships which indicates how confident we are in the prediction. To provide such insight, the ML community has developed Local Interpretable Model-agnostic Explanations (LIME), and SHapley Additive exPlanations (SHAP) tools. In this study, we train a random forest architecture using a suite of seismic attributes as input to differentiate between mass transport deposits (MTDs), salt, and conformal siliciclastic sediments in a Gulf of Mexico dataset. We apply SHAP to understand how the model uses the input seismic attributes to identify target seismic facies and examine in what manner variations in the input such as adding band-limited random noise or applying a Kuwahara filter impact the models’ predictions. During our global analysis, we find that the attribute importance is dynamic, and changes based on the quality of the seismic attributes and the seismic facies analyzed. For our data volume and target facies, attributes measuring changes in dip and energy show the largest importance for all cases in our sensitivity analysis. We note that to discriminate between the seismic facies, the ML architecture learns a “set of rules” in multi-attribute space and that overlap between MTDs, salt, and conformal sediments might exist based on the seismic attribute analyzed. Finally, using SHAP at a voxel-scale, we understand why certain areas of interest were misclassified by the algorithm and perform an in-context interpretation to analyze how changes in the geology impact the model’s predictions.

Independent component analysis for reservoir geomorphology and unsupervised seismic facies classification in the Taranaki Basin, New Zealand

2019 ◽  
Vol 7 (3) ◽  
pp. SE19-SE42 ◽  
Author(s):  
David Lubo-Robles ◽  
Kurt J. Marfurt
Keyword(s):  
New Zealand ◽  
Independent Component Analysis ◽  
Order Statistics ◽  
Input Data ◽  
Component Analysis ◽  
Independent Component ◽  
Seismic Facies ◽  
Blue Color ◽  
Independent Components ◽  
Facies Classification

During the past two decades, the number of volumetric seismic attributes has increased to the point at which interpreters are overwhelmed and cannot analyze all of the information that is available. Principal component analysis (PCA) is one of the best-known multivariate analysis techniques that decompose the input data into second-order statistics by maximizing the variance, thus obtaining mathematically uncorrelated components. Unfortunately, projecting the information in the multiple input data volumes onto an orthogonal basis often mixes rather than separates geologic features of interest. To address this issue, we have implemented and evaluated a relatively new unsupervised multiattribute analysis technique called independent component analysis (ICA), which is based on higher order statistics. We evaluate our algorithm to study the internal architecture of turbiditic channel complexes present in the Moki A sands Formation, Taranaki Basin, New Zealand. We input 12 spectral magnitude components ranging from 25 to 80 Hz into the ICA algorithm and we plot 3 of the resulting independent components against a red-green-blue color scheme to generate a single volume in which the colored independent components correspond to different seismic facies. The results obtained using ICA proved to be superior to those obtained using PCA. Specifically, ICA provides improved resolution and separates geologic features from noise. Moreover, with ICA, we can geologically analyze the different seismic facies and relate them to sand- and mud-prone seismic facies associated with axial and off-axis deposition and cut-and-fill architectures.

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
Keyword(s):  
Ground Truth ◽  
Seismic Facies ◽  
Seismic Survey ◽  
Computer Assisted ◽  
Seismic Attribute ◽  
Self Organizing Maps ◽  
Seismic Facies Analysis ◽  
Tripolitic Chert ◽  
Mississippi Lime ◽  
Osage County

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.

scholarly journals Seismic facies analysis using machine learning

Geophysics ◽  
2018 ◽  
Vol 83 (5) ◽  
pp. O83-O95 ◽  
Author(s):  
Thilo Wrona ◽  
Indranil Pan ◽  
Robert L. Gawthorpe ◽  
Haakon Fossen
Keyword(s):  
Machine Learning ◽  
Seismic Facies ◽  
Machine Learning Techniques ◽  
Seismic Section ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Facies Classification ◽  
Learning Techniques ◽  
Seismic Facies Analysis ◽  
Northern North Sea

Seismic interpretations are, by definition, subjective and often require significant time and expertise from the interpreter. We are convinced that machine-learning techniques can help address these problems by performing seismic facies analyses in a rigorous, repeatable way. For this purpose, we use state-of-the-art 3D broadband seismic reflection data of the northern North Sea. Our workflow includes five basic steps. First, we extract seismic attributes to highlight features in the data. Second, we perform a manual seismic facies classification on 10,000 examples. Third, we use some of these examples to train a range of models to predict seismic facies. Fourth, we analyze the performance of these models on the remaining examples. Fifth, we select the “best” model (i.e., highest accuracy) and apply it to a seismic section. As such, we highlight that machine-learning techniques can increase the efficiency of seismic facies analyses.

Seismic facies analysis based on deep convolutional embedded clustering

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. IM87-IM97
Author(s):  
Yanting Duan ◽  
Xiaodong Zheng ◽  
Lianlian Hu ◽  
Luping Sun
Keyword(s):  
Seismic Data ◽  
Facies Analysis ◽  
Clustering Algorithms ◽  
Feature Space ◽  
Seismic Facies ◽  
Clustering Methods ◽  
Model Data ◽  
Geophysical Model ◽  
Facies Classification ◽  
Seismic Facies Analysis

Seismic facies classification takes a two-step approach: attribute extraction and seismic facies analysis by using clustering algorithms, sequentially. In general, it is clear that the choice of feature extraction is critical for successful seismic facies analysis. However, the choice of features is customarily determined by the seismic interpreters, and so the clustering result is affected by the difference in the seismic interpreters’ experience levels. It becomes challenging to extract features and identify seismic facies simultaneously. We have introduced deep convolutional embedded clustering (DCEC), which aims to simultaneously learn feature representations and cluster assignments by using deep neural networks. Our method learns mapping from the data space to a lower dimensional feature space in which it iteratively optimizes a clustering objective by building a specific loss function. We apply the method to the Modified National Institute of Standards and Technology (MNIST) data, geophysical model data, and field seismic data. In the MNIST data, the DCEC method shows better latent space of clustering results than traditional clustering methods. In the geophysical model data, the accuracy of waveform classification based on DCEC method is higher than traditional clustering methods. The results from the seismic data demonstrate that selection of input data and method has an important effect on the clustering result. In addition, our method is helpful for improving the resolution of seismic facies edges and offers the richer depositional information than the traditional clustering methods.

Whither seismic stratigraphy?

2013 ◽  
Vol 1 (1) ◽  
pp. SA3-SA20 ◽  
Author(s):  
Bruce S. Hart
Keyword(s):  
Feature Detection ◽  
Seismic Analysis ◽  
Collaborative Work ◽  
Seismic Stratigraphy ◽  
Seismic Facies ◽  
Rock Properties ◽  
Seismic Attribute ◽  
Rock Property ◽  
Seismic Geomorphology ◽  
Reflection Geometry

Here, I provide an historical summary of seismic stratigraphy and suggest some potential avenues for future collaborative work between sedimentary geologists and geophysicists. Stratigraphic interpretations based on reflection geometry- or shape-based approaches have been used to reconstruct depositional histories and to make qualitative and (sometimes) quantitative predictions of rock physical properties since at least the mid-1970s. This is the seismic stratigraphy that is usually practiced by geology-focused interpreters. First applied to 2D seismic data, interest in seismic stratigraphy was reinvigorated by the development of seismic geomorphology on 3D volumes. This type of reflection geometry/shape-based interpretation strategy is a fairly mature science that includes seismic sequence analysis, seismic facies analysis, reflection character analysis, and seismic geomorphology. Rock property predictions based on seismic stratigraphic interpretations usually are qualitative, and reflection geometries commonly may permit more than one interpretation. Two geophysics-based approaches, practiced for nearly the same length of time as seismic stratigraphy, have yet to gain widespread adoption by geologic interpreters even though they have much potential application. The first is the use of seismic attributes for “feature detection,” i.e., helping interpreters to identify stratigraphic bodies that are not readily detected in conventional amplitude displays. The second involves rock property (lithology, porosity, etc.) predictions from various inversion methods or seismic attribute analyses. Stratigraphers can help quality check the results and learn about relationships between depositional features and lithologic properties of interest. Stratigraphers also can contribute to a better seismic analysis by helping to define the effects of “stratigraphy” (e.g., laminations, porosity, bedding) on rock properties and seismic responses. These and other seismic-related pursuits would benefit from enhanced collaboration between sedimentary geologists and geophysicists.

Characterizing a turbidite system in Canterbury Basin, New Zealand, using seismic attributes and distance-preserving self-organizing maps

2016 ◽  
Vol 4 (1) ◽  
pp. SB79-SB89 ◽  
Author(s):  
Tao Zhao ◽  
Jing Zhang ◽  
Fangyu Li ◽  
Kurt J. Marfurt
Keyword(s):  
New Zealand ◽  
Seismic Attributes ◽  
Seismic Facies ◽  
Seismic Attribute ◽  
Self Organizing Maps ◽  
Seismic Amplitude ◽  
Facies Classification ◽  
Turbidite System ◽  
Reliable Classification ◽  
Self Organizing

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.

Seismic attribute selection for unsupervised seismic facies analysis using user-guided data-adaptive weights

Geophysics ◽  
2018 ◽  
Vol 83 (2) ◽  
pp. O31-O44 ◽  
Author(s):  
Tao Zhao ◽  
Fangyu Li ◽  
Kurt J. Marfurt
Keyword(s):  
Unsupervised Learning ◽  
Facies Analysis ◽  
Learning Algorithm ◽  
Selection Process ◽  
Rapid Development ◽  
Attribute Selection ◽  
Seismic Facies ◽  
Seismic Attribute ◽  
Data Adaptive ◽  
Seismic Facies Analysis

With the rapid development in seismic attribute and interpretation techniques, interpreters can be overwhelmed by the number of attributes at their disposal. Pattern recognition-driven seismic facies analysis provides a means to identify subtle variations across multiple attributes that may only be partially defined on a single attribute. Typically, interpreters intuitively choose input attributes for multiattribute facies analysis based on their experience and the geologic target of interest. However, such an approach may overlook unsuspected or subtle features hidden in the data. We therefore augment this qualitative attribute selection process with quantitative measures of candidate attributes that best differentiate features of interest. Instead of selecting a group of attributes and assuming all the selected attributes contribute equally to the facies map, we weight the interpreter-selected input attributes based on their response from the unsupervised learning algorithm and the interpreter’s knowledge. In other words, we expect the weights to represent “which attribute is ‘favored’ by an interpreter as input for unsupervised learning” from an interpretation perspective and “which attribute is ‘favored’ by the learning algorithm” from a data-driven perspective. Therefore, we claim the weights are user guided and data adaptive, as the derivation of weight for each input attribute is embedded into the learning algorithm, providing a specific measurement tailored to the selected learning algorithm, while still taking the interpreter’s knowledge into account. We develop our workflow using Barnett Shale surveys and an unsupervised self-organizing map seismic facies analysis algorithm. We found that the proposed weighting-based attribute selection method better differentiates features of interest than using equally weighted input attributes. Furthermore, the weight values provide insights into dependency among input attributes.

A knowledge-integration framework for interpreting seismic facies

2014 ◽  
Vol 2 (1) ◽  
pp. SA1-SA9 ◽  
Author(s):  
Iván D. Marroquín
Keyword(s):  
Spatial Distribution ◽  
Knowledge Integration ◽  
Optimal Number ◽  
Seismic Facies ◽  
Reservoir Properties ◽  
Integration Framework ◽  
Facies Classification ◽  
Combining Data ◽  
Seismic Anomalies ◽  
Selection Of

In recent years, the size of seismic data volumes and the number of seismic attributes available have increased. As a result, the task of recognizing seismic anomalies for the prediction of stratigraphic features or reservoir properties can be overwhelming. One way to evaluate a large amount of data and understand potential geologic trends is to automate seismic facies classification. However, the interpretation of seismic facies remains an elusive issue. Interpreters are confronted with the selection of the clustering technique and the optimal number of seismic facies that best uncover the spatial distribution of seismic facies. An interpretation framework combining data visualization with the results from various clustering techniques was evaluated. The framework allows interpreters to be directly involved in the seismic facies classification process. Because of the active participation, interpreters (1) gain insight into the detected seismic facies, (2) verify hypotheses with respect to the spatial distribution of seismic facies, (3) compare different seismic facies classification, and (4) gain more confidence with the seismic facies interpretation.

Latest developments in seismic texture analysis for subsurface structure, facies, and reservoir characterization: A review

Geophysics ◽  
2011 ◽  
Vol 76 (2) ◽  
pp. W1-W13 ◽  
Author(s):  
Dengliang Gao
Keyword(s):  
Texture Analysis ◽  
Reservoir Characterization ◽  
Preliminary Test ◽  
Seismic Facies ◽  
Least Squares Regression ◽  
Reservoir Properties ◽  
Seismic Amplitude ◽  
Texture Model ◽  
Facies Classification ◽  
Analysis Window

In exploration geology and geophysics, seismic texture is still a developing concept that has not been sufficiently known, although quite a number of different algorithms have been published in the literature. This paper provides a review of the seismic texture concepts and methodologies, focusing on latest developments in seismic amplitude texture analysis, with particular reference to the gray level co-occurrence matrix (GLCM) and the texture model regression (TMR) methods. The GLCM method evaluates spatial arrangements of amplitude samples within an analysis window using a matrix (a two-dimensional histogram) of amplitude co-occurrence. The matrix is then transformed into a suite of texture attributes, such as homogeneity, contrast, and randomness, which provide the basis for seismic facies classification. The TMR method uses a texture model as reference to discriminate among seismic features based on a linear, least-squares regression analysis between the model and the data within an analysis window. By implementing customized texture model schemes, the TMR algorithm has the flexibility to characterize subsurface geology for different purposes. A texture model with a constant phase is effective at enhancing the visibility of seismic structural fabrics, a texture model with a variable phase is helpful for visualizing seismic facies, and a texture model with variable amplitude, frequency, and size is instrumental in calibrating seismic to reservoir properties. Preliminary test case studies in the very recent past have indicated that the latest developments in seismic texture analysis have added to the existing amplitude interpretation theories and methodologies. These and future developments in seismic texture theory and methodologies will hopefully lead to a better understanding of the geologic implications of the seismic texture concept and to an improved geologic interpretation of reflection seismic amplitude.

Sign in / Sign up

Export Citation Format

Share Document