Improved reservoir characterization by means of supervised machine learning and model-based seismic impedance inversion in the Penobscot field, Scotian Basin

Narayan, Satya; Sahoo, Soumyashree Debasis; Kar, Soumitra; Pal, Sanjit Kumar; Kangsabanik, Subhra

doi:10.1016/j.engeos.2023.100180

Cited by 8 publications

(3 citation statements)

References 67 publications

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“…Subsequently, various seismic attributes, including sweetness, instantaneous frequency, and amplitude, were employed to delineate the sweet spots. The instantaneous frequency attribute offers the best indication of hydrocarbon presence, with low-frequency values marked as anomalies and extensively used to identify fracture zones, which appear as low-frequency zones. , Instantaneous amplitude refers to a seismic attribute that quantifies the maximum displacement or magnitude of the seismic waves at a specific point in time. It provides information about the energy or strength of the seismic signal at each sample point along a seismic trace, as well as the reflectivity vigor, which is proportional to the square root of the total energy of the seismic signal at an instant of time .…”

Section: Methodsmentioning

confidence: 99%

Accentuating the Reservoir Potentials of Balkassar Field by Delineating the Fracture Trends and Hydrocarbon Prospects with the Aid of Seismic Attributes

Waqas,

Shazia,

Muyyassar

2024

ACS Omega

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Section: Methodsmentioning

confidence: 99%

Accentuating the Reservoir Potentials of Balkassar Field by Delineating the Fracture Trends and Hydrocarbon Prospects with the Aid of Seismic Attributes

Waqas,

Shazia,

Muyyassar

2024

ACS Omega

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“…On the other hand, several other factors affect the accuracy of the models. Relatively lower frequency bandwidth in seismic data caused the overlapping responses from different lithofacies, which caused the misclassification problem in facies estimation (Narayan et al, 2023). Models' performance was also affected by delineating thin and discrete inter-bedded facies.…”

Section: Figure 10mentioning

confidence: 99%

Accuracy assessment of various supervised machine learning algorithms in litho-facies classification from seismic data in the Penobscot field, Scotian Basin

Narayan,

Konka,

Chandra

et al. 2023

Front. Earth Sci.

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Litho-facies classification is an essential task in characterizing the complex reservoirs in petroleum exploration and subsequent field development. The lithofacies classification at borehole locations is detailed but lacks in providing larger coverage areas. The acquired 3D seismic data provides global coverage for studying the reservoir facies heterogeneities in the study area. This study applies six supervised machine learning techniques (Random Forest, Support Vector Machine, Artificial Neural Network, Adaptive Boosting, Xtreme Gradient Boosting, and Multilayer Perceptron) to 3D post-stack seismic data to accurately estimate different litho-facies in inter-well regions and compares their performance. Initially, the efficacy of the said models was critically examined via the confusion matrix (accuracy and misclass) and evaluation matrix (precision, recall, F1-score) on the test data. It was found that all the machine learning models performed best in classifying the shale facies (87%–94%) followed by the sand (65%–79%) and carbonate facies (60%–78%) in the Penobscot field, Scotian Basin. On an overall accuracy scale, we found the multilayer perceptron method the best-performing tool, whereas the adaptive boosting method was the least-performing tool in classifying all three litho-facies in the current analysis. While other methods also performed moderately good for the classification of all three litho-facies. The predicted litho-facies using seismic attributes matched well with the log data interpreted facies on the borehole locations. It indicates that the facies estimated in inter-well regions are accurate and reliable. Furthermore, we validated the estimated results with the other seismic attributes to ascertain the accuracy and reliability of the predicted litho-facies between the borehole locations. This study recommends machine learning applications for litho-facies classification to reduce the risk associated with reservoir characterization.

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“…Commonly used methods include nonlinear regression analysis (BPANN), nearest neighbor (KNN), decision tree (DT), and vector machine (SVM). Naive Bayes (NB) is less frequently used due to difficulties in handling interfering data sets [18][19][20][21]. Naive Bayes (NB) is seldom used because it is difficult to deal with data sets that interfere with each other [22,23].…”

Section: Introductionmentioning

confidence: 99%

Application and Comparison of Machine Learning Methods for Mud Shale Petrographic Identification

et al. 2023

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Machine learning is the main technical means for lithofacies logging identification. As the main target of shale oil spatial distribution prediction, mud shale petrography is subjected to the constraints of stratigraphic inhomogeneity and logging information redundancy. Therefore, choosing the most applicable machine learning method for different geological characteristics and data situations is one of the key aspects of high-precision lithofacies identification. However, only a few studies have been conducted on the applicability of machine learning methods for mud shale petrography. This paper aims to identify lithofacies using commonly used machine learning methods. The study employs five supervised learning algorithms, namely Random Forest Algorithm (RF), BP Neural Network Algorithm (BPANN), Gradient Boosting Decision Tree Method (GBDT), Nearest Neighbor Method (KNN), and Vector Machine Method (SVM), as well as four unsupervised learning algorithms, namely K-means, DBSCAN, SOM, and MRGC. The results are evaluated using the confusion matrix, which provides the accuracy of each algorithm. The GBDT algorithm has better accuracy in supervised learning, while the K-means and DBSCAN algorithms have higher accuracy in unsupervised learning. Based on the comparison of different algorithms, it can be concluded that shale lithofacies identification poses challenges due to limited sample data and high overlapping degree of type distribution areas. Therefore, selecting the appropriate algorithm is crucial. Although supervised machine learning algorithms are generally accurate, they are limited by the data volume of lithofacies samples. Future research should focus on how to make the most of limited samples for supervised learning and combine unsupervised learning algorithms to explore lithofacies types of non-coring wells.

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Improved reservoir characterization by means of supervised machine learning and model-based seismic impedance inversion in the Penobscot field, Scotian Basin

Cited by 8 publications

References 67 publications

Accentuating the Reservoir Potentials of Balkassar Field by Delineating the Fracture Trends and Hydrocarbon Prospects with the Aid of Seismic Attributes

Accentuating the Reservoir Potentials of Balkassar Field by Delineating the Fracture Trends and Hydrocarbon Prospects with the Aid of Seismic Attributes

Accuracy assessment of various supervised machine learning algorithms in litho-facies classification from seismic data in the Penobscot field, Scotian Basin

Application and Comparison of Machine Learning Methods for Mud Shale Petrographic Identification

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