Clustering of Geological Objects Using FCM-algorithm and Evaluation of the Rate of Lost Circulation

Efendiyev, G. M.; Mammadov, P.Z.; Piriverdiyev, I. A.; Mammadov, V.

doi:10.1016/j.procs.2016.09.383

Cited by 14 publications

(8 citation statements)

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“…The clustering Fitness (CF) was also used as performance measurement metric, and CF tests were performed. Again, the three algorithms were tested, and the values of CF were calculated for MR images from the dataset with varying number of clusters (c = 6,7,8,9,10,11,12,13,14). The same cluster seed and termination conditions are used for all tests.…”

Section: Discussionmentioning

confidence: 99%

“…To show this, we choose a diversely illuminated images "neck, mid back, low back, and tailbone" from the real dataset, which contains differently illuminated layers. All the algorithms; FKM, FCM, and the proposed CFKCM are executed on each image data with variable number of clusters (c = 6,7,8,9,10,11,12,13,14). For all algorithms, the same cluster seed and termination conditions are used.…”

Section: Clustering Fitness Testmentioning

confidence: 99%

“…With reference to the related published work, many researches in literature investigated with the Fuzzy C-Means (FCM) clustering algorithm in an extensive variety of ways aiming to achieve improved image segmentation results [1][2][3]. The FCM is an unsupervised method, which has been applied successfully to perform operations such as feature analysis [4], clustering [5][6][7][8][9], and classifier designs in fields such as astronomy [10], geology [11,12], medical imaging, target recognition [13], and image segmentation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enhanced Image Segmentation: Merging Fuzzy K-Means and Fuzzy C-Means Clustering Algorithms for Medical Applications

Aljebory¹,

Mohammed²,

Zainal³

2021

csit

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Separating an image into regions according to some criterion is called image segmentation. This paper presents an algorithm that combines the fuzzy k-means (FKM) and fuzzy c-means (FCM) clustering strategies. The proposed algorithm combines the FKM and the FCM algorithms mathematical features, which is referred to as (CFKCM). The FKM and FCM clustering algorithms are adopted to compare the performance and hence evaluate the proposed clustering algorithm. Tests are conducted, and performance parameters are calculated for validation. The comparison and assessment analysis are based on metrics related to the image clustering process, such as the Segmentation Accuracy (SA), Clustering Fitness (CF), and cluster Validity function (V pc and V pe ). A dataset of MR images is used by this research for the application, test, and evaluation of the image clustering. The results for clustering backbone MRI images show that CFKCM algorithm is featured by being more effective, and comparatively independent of the noise, where it can process the "clean" images and noisy images without knowing the type of the noise, which is the most difficult task in image segmentation.

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Section: Discussionmentioning

confidence: 99%

Section: Clustering Fitness Testmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhanced Image Segmentation: Merging Fuzzy K-Means and Fuzzy C-Means Clustering Algorithms for Medical Applications

Aljebory¹,

Mohammed²,

Zainal³

2021

csit

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“…Therefore, it is arduous to predict. Therefore, many researchers applied the artificial intelligence to solve problems related to lost circulation such as Anifowose et al [32], Castillo [33], Moazzeni et al [34], Toreifi et al [35], Efendiyev et al [36], Far and Hosseini [37], Solomon et al [38], Manshad et al [39], Al-Hameedi et al [40], Alkinani et al [41], Abbas et al [42], Cristofaro et al [43], and Jahanbakhshi and Keshavarzi [44]. All these studies applied a single technique of AI to predict either the type of losses, the amount of losses, or the loss treatment, besides using many input parameters that are difficult to access in every well.…”

Section: Functional Network (Fn)mentioning

confidence: 99%

Application of Artificial Intelligence Techniques in Predicting the Lost Circulation Zones Using Drilling Sensors

et al. 2020

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Drilling a high-pressure, high-temperature (HPHT) well involves many difficulties and challenges. One of the greatest difficulties is the loss of circulation. Almost 40% of the drilling cost is attributed to the drilling fluid, so the loss of the fluid considerably increases the total drilling cost. There are several approaches to avoid loss of return; one of these approaches is preventing the occurrence of the losses by identifying the lost circulation zones. Most of these approaches are difficult to apply due to some constraints in the field. The purpose of this work is to apply three artificial intelligence (AI) techniques, namely, functional networks (FN), artificial neural networks (ANN), and fuzzy logic (FL), to identify the lost circulation zones. Real-time surface drilling parameters of three wells were obtained using real-time drilling sensors. Well A was utilized for training and testing the three developed AI models, whereas Well B and Well C were utilized to validate them. High accuracy was achieved by the three AI models based on the root mean square error (RMSE), confusion matrix, and correlation coefficient (R). All the AI models identified the lost circulation zones in Well A with high accuracy where the R is more than 0.98 and RMSE is less than 0.09. ANN is the most accurate model with R=0.99 and RMSE=0.05. An ANN was able to predict the lost circulation zones in the unseen Well B and Well C with R=0.946 and RMSE=0.165 and R=0.952 and RMSE=0.155, respectively.

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“…As a result of the applying of algorithm, five clusters were obtained [7], and each of them is characterized by petrophysical characteristics matched rate of lost circulation of rocks as following fuzzy rules:…”

Section: Fig 1 Comparison Between the Fcm And K-means Algorithmsmentioning

confidence: 99%

Estimation of the Lost Circulation Rate Using Fuzzy Clustering of Geological Objects by Petrophysical Properties

Efendiyev¹,

Mammadov²,

Piriverdiyev³

et al. 2018

VKNUGEOL

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Рекомендовано членом редакційної колегії д-ром геол. наук, проф. С.А. Вижвою) Currently, cluster-analysis or automatic classification problems are widely used in various fields, in particular economics, sociology, medicine, geology, and other sectors, where there are sets of arbitrary kinds of objects to be automatically divided into groups of similar objects based on their "similarities-differences" features. In recent years, these methods have been widely used in data analysis problems. Conventional methods of cluster-analysis suggest a clear partition of the original set into subsets, in which each point is included only in one cluster after the partition. However, it is well known that such a restriction is not always true. It is often necessary to make such kind of partition, which allows determining the degree of membership of each object for each set. In this case it is advisable to use fuzzy clusteranalysis methods. Problems in this formulation arouse interest of specialists dealing with geology, geophysics, oil-and gaswell drilling and oil and gas production. One of the most important results of the study of lost circulation zones is determination of the coefficient of lost circulation intensity. Purpose. Estimation of drilling mud lost circulation during drilling and emerging risks. Methodology. The solution of the problems posed in the work was carried out using methods known from mathematical statistics and the theory of fuzzy sets. The technique of processing the results, as well as fuzzy cluster analysis, was used for that purpose. Findings. As a result of the research, 5 classes were obtained, each of which characterizes the rate of mud lost circulation, expressed by linguistic variables. On the basis of this, fuzzy models are constructed, expressing the relationship between the indices of petrophysical properties and the volume of the absorbed solution. Originality. A method based on fuzzy cluster analysis has been developed, which makes it possible to predict drilling mud lost circulation of different rate at an early stage during drilling. Practical value. The obtained results allow making decisions on prevention of lost circulation and timely liquidation of their consequences.

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Clustering of Geological Objects Using FCM-algorithm and Evaluation of the Rate of Lost Circulation

Cited by 14 publications

References 1 publication

Enhanced Image Segmentation: Merging Fuzzy K-Means and Fuzzy C-Means Clustering Algorithms for Medical Applications

Enhanced Image Segmentation: Merging Fuzzy K-Means and Fuzzy C-Means Clustering Algorithms for Medical Applications

Application of Artificial Intelligence Techniques in Predicting the Lost Circulation Zones Using Drilling Sensors

Estimation of the Lost Circulation Rate Using Fuzzy Clustering of Geological Objects by Petrophysical Properties

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