Automatic labeling strategy in semisupervised seismic-facies classification by integrating well logs and seismic data

2018 ◽  
Author(s):  
Sigue Lee ◽  
Junhwan Choi ◽  
Daeung Yoon ◽  
Joongmoo Byun
Keyword(s):  
Seismic Data ◽  
Seismic Facies ◽  
Well Logs ◽  
Facies Classification

Applications of Artificial Neural Networks for Seismic Facies Classification

2021 ◽  
Vol 73 (02) ◽  
pp. 68-69
Author(s):  
Chris Carpenter
Keyword(s):  
Neural Network ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Probabilistic Neural Network ◽  
Oil Field ◽  
Seismic Facies ◽  
Data Set ◽  
Full Field ◽  
Facies Classification

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 200577, “Applications of Artificial Neural Networks for Seismic Facies Classification: A Case Study From the Mid-Cretaceous Reservoir in a Supergiant Oil Field,” by Ali Al-Ali, Karl Stephen, SPE, and Asghar Shams, Heriot-Watt University, prepared for the 2020 SPE Europec featured at the 82nd EAGE Conference and Exhibition, originally scheduled to be held in Amsterdam, 1-3 December. The paper has not been peer reviewed. Facies classification using data from sources such as wells and outcrops cannot capture all reservoir characterization in the interwell region. Therefore, as an alternative approach, seismic facies classification schemes are applied to reduce the uncertainties in the reservoir model. In this study, a machine-learning neural network was introduced to predict the lithology required for building a full-field Earth model for carbonate reservoirs in southern Iraq. The work and the methodology provide a significant improvement in facies classification and reveal the capability of a probabilistic neural network technique. Introduction The use of machine learning in seismic facies classification has increased gradually during the past decade in the interpretation of 3D and 4D seismic volumes and reservoir characterization work flows. The complete paper provides a literature review regarding this topic. Previously, seismic reservoir characterization has revealed the heterogeneity of the Mishrif reservoir and its distribution in terms of the pore system and the structural model. However, the main objective of this work is to classify and predict the heterogeneous facies of the carbonate Mishrif reservoir in a giant oil field using a multilayer feed-forward network (MLFN) and a probabilistic neural network (PNN) in nonlinear facies classification techniques. A related objective was to find any domain-specific causal relationships among input and output variables. These two methods have been applied to classify and predict the presence of different facies in Mishrif reservoir rock types. Case Study Reservoir and Data Set Description. The West Qurna field is a giant, multibillion-barrel oil field in the southern Mesopotamian Basin with multiple carbonate and clastic reservoirs. The overall structure of the field is a north/south trending anticline steep on the western flank and gentle on the eastern flank. Many producing reservoirs developed in this oil field; however, the Mid- Cretaceous Mishrif reservoir is the main producing reservoir. The reservoir consists of thick carbonate strata (roughly 250 m) deposited on a shallow water platform adjacent to more-distal, deeper-water nonreservoir carbonate facies developing into three stratigraphic sequence units in the second order. Mishrif facies are characterized by a porosity greater than 20% and large permeability contrast from grainstones to microporosity (10-1000 md). The first full-field 3D seismic data set was achieved over 500 km2 during 2012 and 2013 in order to plan the development of all field reservoirs. A de-tailed description of the reservoir has been determined from well logs and core and seismic data. This study is mainly based on facies log (22 wells) and high-resolution 3D seismic volume to generate seismic attributes as the input data for the training of the neural network model. The model is used to evaluate lithofacies in wells without core data but with appropriate facies logs. Also, testing was carried out in parallel with the core data to verify the results of facies classification.

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.

Seismic facies classification using supervised convolutional neural networks and semisupervised generative adversarial networks

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. O47-O58 ◽  
Author(s):  
Mingliang Liu ◽  
Michael Jervis ◽  
Weichang Li ◽  
Philippe Nivlet
Keyword(s):  
Neural Networks ◽  
Seismic Data ◽  
Hydrocarbon Exploration ◽  
Training Data ◽  
Seismic Facies ◽  
Generative Adversarial Networks ◽  
Complex Data ◽  
Facies Classification ◽  
Good Ability ◽  
Well Data

Mapping of seismic and lithologic facies from 3D reflection seismic data plays a key role in depositional environment analysis and reservoir characterization during hydrocarbon exploration and development. Although a variety of machine-learning methods have been developed to speed up interpretation and improve prediction accuracy, there still exist significant challenges in 3D multiclass seismic facies classification in practice. Some of these limitations include complex data representation, limited training data with labels, imbalanced facies class distribution, and lack of rigorous performance evaluation metrics. To overcome these challenges, we have developed a supervised convolutional neural network (CNN) and a semisupervised generative adversarial network (GAN) for 3D seismic facies classification in situations with sufficient and limited well data, respectively. The proposed models can predict 3D facies distribution based on actual well log data and core analysis, or other prior geologic knowledge. Therefore, they provide a more consistent and meaningful implication to seismic interpretation than commonly used unsupervised approaches. The two deep neural networks have been tested successfully on a realistic synthetic case based on an existing reservoir and a real case study of the F3 seismic data from the Dutch sector of the North Sea. The prediction results show that, with relatively abundant well data, the supervised CNN-based learning method has a good ability in feature learning from seismic data and accurately recovering the 3D facies model, whereas the semisupervised GAN is effective in avoiding overfitting in the case of extremely limited well data. The latter seems, therefore, particularly adapted to exploration or early field development stages in which labeled data from wells are still very scarce.

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.

Quantitative uncertainty evaluation of seismic facies classification: A case study from northeast China

Geophysics ◽  
2016 ◽  
Vol 81 (3) ◽  
pp. B87-B99 ◽  
Author(s):  
Cheng Yuan ◽  
Jingye Li ◽  
Xiaohong Chen ◽  
Ying Rao
Keyword(s):  
Seismic Data ◽  
Northeast China ◽  
Reservoir Characterization ◽  
Early Stage ◽  
Uncertainty Propagation ◽  
Seismic Facies ◽  
Uncertainty Evaluation ◽  
Computational Time ◽  
Facies Classification ◽  
Well Data

Reservoir characterization in the early stage of oilfield exploration generally has enormous uncertainty because few geophysical and well data are typically available. The uncertainty when classifying the facies with seismic data propagates throughout the processes of seismic facies classification, causing errors in the final evaluation of geologic features in an area. To quantitatively evaluate the uncertainty in seismic facies classification, we have analyzed prestack seismic data and well observations in a tight reservoir from northeast China and calculated the uncertainties throughout the process. To achieve this, the facies probabilities conditioned on different properties in each step of seismic facies classification were first derived using a probabilistic multistep inversion. Second, the associated uncertainty and maximum a posterior (MAP) of facies probabilities were evaluated by means of entropy and reconstruction rate, which assessed the degree of similarity between MAP and facies sequence within the range [0, 1]. This enabled us to investigate the influence of the uncertainty propagation on seismic facies classification. The uncertainty of the inversion results for the target reservoir was finally characterized by the calculated entropy and its indicator transform. Additionally, parameter spaces of well-log and upscaled elastic properties were restricted according to the data distribution characteristics in the crossplot. Parameter vectors that were outside the restricted scopes were excluded, reducing the computational time and uncertainty. We determined that quantitative uncertainty evaluation by entropy with a probabilistic multistep approach enabled us to explore much more details of the uncertainty propagation in the processes of seismic reservoir characterization. It should be the method of choice for risk of management and decision making in reservoir assessment.

scholarly journals Seismic Sequence Stratigraphic Sub-Division Using Well Logs and Seismic Data of Taranaki Basin, New Zealand

2021 ◽  
Vol 11 (3) ◽  
pp. 1226
Author(s):  
Abd Al-Salam Al-Masgari ◽  
Mohamed Elsaadany ◽  
Abdul Halim Abdul Latiff ◽  
Maman Hermana ◽  
Umar Bin Hamzah ◽  
...  
Keyword(s):  
Sequence Stratigraphy ◽  
Seismic Data ◽  
Depositional Environment ◽  
Seismic Facies ◽  
Well Logs ◽  
Seismic Section ◽  
Sequence Stratigraphic ◽  
New Perspective ◽  
Potential Traps ◽  
Bottom To Top

This study focuses on the sequence stratigraphy and the dominated seismic facies in the Central Taranaki basin. Four regional seismic sequences namely SEQ4 to SEQ1 from bottom to top and four boundaries representing unconformities namely H4 to H1 from bottom to top have been traced based on the reflection terminations. This was validated using well logs information. An onlapping feature on the seismic section indicates a new perspective surface separated between the upper and lower Giant formation, which indicates a period of seawater encroachment. This study focused extensively on deposition units from SEQ4 to SEQ1. The seismic facies, isochron map, and depositional environment were determined, and the system tract was established. This study was also able to propose a new perspective sequence stratigraphy framework of the basin and probable hydrocarbon accumulations and from the general geological aspect, SA-Middle Giant Formation (SEQ3) could act as potential traps.

scholarly journals Seismic facies classification and identification by competitive neural networks

Geophysics ◽  
2003 ◽  
Vol 68 (6) ◽  
pp. 1984-1999 ◽  
Author(s):  
M. M. Saggaf ◽  
M. Nafi Toksöz ◽  
M. I. Marhoon
Keyword(s):  
Neural Networks ◽  
Seismic Data ◽  
Effective Means ◽  
Synthetic Data ◽  
Low Frequency ◽  
Real Data ◽  
Seismic Facies ◽  
Automatic Identification ◽  
Facies Classification ◽  
Geologic Model

We present an approach based on competitive neural networks for the classification and identification of reservoir facies from seismic data. This approach can be adapted to perform either classification of the seismic facies based entirely on the characteristics of the seismic response, without requiring the use of any well information, or automatic identification and labeling of the facies where well information is available. The former is of prime use for oil prospecting in new regions, where few or no wells have been drilled, whereas the latter is most useful in development fields, where the information gained at the wells can be conveniently extended to the interwell regions. Cross‐validation tests on synthetic and real seismic data demonstrated that the method can be an effective means of mapping the reservoir heterogeneity. For synthetic data, the output of the method showed considerable agreement with the actual geologic model used to generate the seismic data; for the real data application, the predicted facies accurately matched those observed at the wells. Moreover, the resulting map corroborates our existing understanding of the reservoir and shows substantial similarity to the low‐frequency geologic model constructed by interpolating the well information, while adding significant detail and enhanced resolution to that model.

scholarly journals The application of volume texture extraction to three-dimensional seismic data – lithofacies structures exploration within the Miocene deposits of the Carpathian Foredeep

2021 ◽  
Vol 46 (4) ◽  
pp. 301-313
Author(s):  
Mariusz Łukaszewski
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Seismic Analysis ◽  
Seismic Facies ◽  
Machine Learning Techniques ◽  
Small Scale ◽  
Facies Classification ◽  
Carpathian Foredeep ◽  
Texture Attributes ◽  
Self Learning

There are numerous conventional fields of natural gas in the Carpathian Foredeep, and there is also evidence to suggest that unconventional gas accumulations may occur in this region. The different seismic sig-natures of these geological forms, the small scale of amplitude variation, and the large amount of data make the process of geological interpretation extremely time-consuming. Moreover, the dispersed nature of information in a large block of seismic data increasingly requires automatic, self-learning cognitive processes. Recent developments with Machine Learning have added new capabilities to seismic interpretation, especially to multi-attribute seismic analysis. Each case requires a proper selection of attributes. In this paper, the Grey Level Co-occurrence Matrix method is presented and its two texture attributes Energy and Entropy. Haralick’s two texture parameters were applied to an advanced interpretation of the interval of Miocene deposits in order to discover the subtle geological features hidden between the seismic traces. As a result, a submarine-slope channel system was delineated leading to the discovery of unknown earlier relationships between gas boreholes and the geological environment. The Miocene deposits filling the Carpathian Foredeep, due to their lithological and facies diversity, provide excellent conditions for testing and implementing Machine Learning techniques. The presented texture attributes are the desired input components for self-learning systems for seismic facies classification.

Multiattribute framework analysis for the identification of carbonate mounds in the Brazilian presalt zone

2019 ◽  
Vol 7 (2) ◽  
pp. T467-T476 ◽  
Author(s):  
Carlos Jesus ◽  
Maria Olho Azul ◽  
Wagner Moreira Lupinacci ◽  
Leandro Machado
Keyword(s):  
Seismic Data ◽  
High Density ◽  
Seismic Facies ◽  
Reservoir Quality ◽  
Framework Analysis ◽  
Carbonate Mounds ◽  
Facies Classification ◽  
Static Modeling ◽  
Well Data ◽  
Low Amplitude

Carbonate mounds, as described herein, often present seismic characteristics such as low amplitude and a high density of faults and fractures, which can easily be oversampled and blur other rock features in simple geobody extraction processes. We have developed a workflow for combining geometric attributes and hybrid spectral decomposition (HSD) to efficiently identify good-quality reservoirs in carbonate mounds within the complex environment of the Brazilian presalt zone. To better identify these reservoirs within the seismic volume of carbonate mounds, we divide our methodology into four stages: seismic data acquisition and processing overview, preconditioning of seismic data using structural-oriented filtering and imaging enhancement, calculation of seismic attributes, and classification of seismic facies. Although coherence and curvature attributes are often used to identify high-density fault and fracture zones, representing one of the most important features of carbonate mounds, HSD is necessary to discriminate low-amplitude carbonate mounds (good reservoir quality) from low-amplitude clay zones (nonreservoir). Finally, we use a multiattribute facies classification to generate a geologically significant outcome and to guide a final geobody extraction that is calibrated by well data and that can be used as a spatial indicator of the distribution of good reservoir quality for static modeling.

scholarly journals Unsupervised Machine Learning Applied to Seismic Interpretation: Towards an Unsupervised Automated Interpretation Tool

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6347
Author(s):  
Alimed Celecia ◽  
Karla Figueiredo ◽  
Carlos Rodriguez ◽  
Marley Vellasco ◽  
Edwin Maldonado ◽  
...  
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Clustering Algorithms ◽  
Seismic Interpretation ◽  
Hydrocarbon Exploration ◽  
Seismic Facies ◽  
Well Logs ◽  
Unsupervised Machine Learning ◽  
High Data ◽  
Novel Approach

Seismic interpretation is a fundamental process for hydrocarbon exploration. This activity comprises identifying geological information through the processing and analysis of seismic data represented by different attributes. The interpretation process presents limitations related to its high data volume, own complexity, time consumption, and uncertainties incorporated by the experts’ work. Unsupervised machine learning models, by discovering underlying patterns in the data, can represent a novel approach to provide an accurate interpretation without any reference or label, eliminating the human bias. Therefore, in this work, we propose exploring multiple methodologies based on unsupervised learning algorithms to interpret seismic data. Specifically, two strategies considering classical clustering algorithms and image segmentation methods, combined with feature selection, were evaluated to select the best possible approach. Additionally, the resultant groups of the seismic data were associated with groups obtained from well logs of the same area, producing an interpretation with aggregated lithologic information. The resultant seismic groups correctly represented the main seismic facies and correlated adequately with the groups obtained from the well logs data.

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