Seismic interpretation below tuning with multiattribute analysis

Roden, Rocky; Smith, Thomas; Santogrossi, Patricia; Sacrey, Deborah; Jones, Gary L.

doi:10.1190/tle36040330.1

Cited by 23 publications

(7 citation statements)

References 17 publications

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“…Several utilizations of unsupervised learning are clustering, dimensionality reduction, and anomaly detection. In the geosciences field of study, unsupervised learning has been applied to aid interpretation, such as multi-attribute analysis [6][7][8]16], AVO cross-plotting, and classification [3,17].…”

Section: Unsupervised Learningmentioning

confidence: 99%

Pay Zone Determination Using Enhanced Workflow and Neural Network

Syahputra

Hermana

Satti³

2022

Applied Sciences

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Amplitude versus offset (AVO) analysis and attributes are frequently utilized during the early stages of exploration when no well has been drilled. However, there are still some drawbacks to this method, including the fact that it involves a substantial amount of time and experience, as well as the subjectivity of manual analysis. By utilizing unsupervised learning, this process can be done more objectively and faster. Unsupervised learning can detect anomalies and identify patterns to understand more about the datasets since, at this early stage of exploration, there is still a lack of information and labelling. A type of unsupervised learning referred to as self-organizing maps (SOM) is applied in this study to delineate hydrocarbons from given AVO properties that were used to detect hydrocarbons. SOM is also used to eliminate redundancy in the selection of attributes prior to the delineation procedure. The investigation began with well log data and progressed ahead into multiple fluid conditions to evaluate the model’s ability to identify hydrocarbons. The analysis can then be extended to the seismic dataset. By combining SOM, correlation coefficient, and mean–median, a method is devised for filtering features to remove redundancy. On the hydrocarbon delineation process, the model managed to detect hydrocarbons using well log simulations and was confirmed using water saturation logs. Additionally, the model is validated using real seismic data, demonstrating a promising performance in defining probable hydrocarbons. The proposed method enables early detection of hydrocarbon content during the preliminary stage of exploration when no well is accessible.

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Section: Unsupervised Learningmentioning

confidence: 99%

Pay Zone Determination Using Enhanced Workflow and Neural Network

Syahputra

Hermana

Satti³

2022

Applied Sciences

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“…Recent studies have shown promising applications of machine learning techniques to reflection seismic data to aid in the interpretation of geologic patterns [8][9][10][11]. Some of these applications are at the sub-seismic resolution scale since machine learning works on a sample interval basis, as opposed to being limited by the seismic wavelet [8,12].…”

Section: Figurementioning

confidence: 99%

Unsupervised Machine Learning, Multi-Attribute Analysis for Identifying Low Saturation Gas Reservoirs within the Deepwater Gulf of Mexico, and Offshore Australia

Chenin

Bedle

2022

Geosciences

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An effective method of identifying and discriminating undersaturated gas accumulations remains unresolved, resulting in uncertainty in hydrocarbon exploration. To address this problem, an unsupervised machine learning multi-attribute analysis is performed on 3D post-stack seismic data over several blocks within the deepwater Gulf of Mexico and within the Carnarvon Basin, offshore Australia. Results reveal that low-saturation gas (LSG) reservoirs can be discriminated from high-saturation gas (HSG) reservoirs by using a combination of instantaneous attributes that are sensitive to small amplitude, frequency, and phase anomalies with self-organizing maps (SOMs). This methodology shows promise for de-risking prospects, even if it is not quantitative, particularly in frontier and exploration basins where wells may not exist or be very limited. However, this method only proved to be successful within the Gulf of Mexico and yielded limited results in the Carnarvon Basin. This difference is most likely due to the Carnarvon Basin having a different amplitude response resulting from a different burial history and fluid saturations when compared to the Gulf of Mexico. Therefore, this method is non-transferrable, and a different combination of attributes may be needed in other LSG-prone basins.

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“…Flexer 52 discussed the application of SOM for clustering and data visualization. Roden et al, 53 implemented SOM to analyze several seismic attributes to identify natural patterns for stratigraphic interpretation. Meanwhile, Köhler et al 54 used SOMs to detect and classify events in continuous seismic wavefield records also the SOM was able to visualize the 24-hour human activity cycle.…”

Section: Self-organizing Map Clustering (Som)mentioning

confidence: 99%

Discrimination between Small Earthquakes and Local Quarry Blasts Using Committee Machine

Lethy

Othman

Gabry

et al. 2021

Preprint

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A combination of multiple discrimination artificial neural networks using different seismic source parameters is suggested using a committee machine. In this work, a committee machine was used to combine supervised and unsupervised artificial neural networks to discriminate between earthquakes and quarry blasts using data from the Egyptian National Seismological Network (ENSN). The unsupervised network is used as a measure of accuracy for the results of the supervised neural network. The unsupervised Self-Organized Map (SOM) and the k-means clustering algorithms are used to estimate support and confidence measures for the results. Meanwhile, the supervised neural network is used to discriminate between earthquakes and explosions. The artificial neural networks are trained using different input parameters which are the P wave spectrum corner frequency (PcF), S wave corner frequency (ScF), and the ratio (Rcf) of PcF to Scf. The combined approach succeeds to discriminate between earthquakes and quarry blasts in Northern Egypt. The method provides the results with a measure of confidence which eliminates false discrimination. The current paper represents an idea to implement artificial intelligence to assist experts in decision-making situations. The committee machine could identify the nature of a particular event, using the aid of several discrimination methods. The proposed committee machine could combine the results of several algorithms and expert opinions to form one single output with a confidence measure.

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Seismic interpretation below tuning with multiattribute analysis

Cited by 23 publications

References 17 publications

Pay Zone Determination Using Enhanced Workflow and Neural Network

Pay Zone Determination Using Enhanced Workflow and Neural Network

Unsupervised Machine Learning, Multi-Attribute Analysis for Identifying Low Saturation Gas Reservoirs within the Deepwater Gulf of Mexico, and Offshore Australia

Discrimination between Small Earthquakes and Local Quarry Blasts Using Committee Machine

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