PENGELOMPOKAN TITIK GEMPA DI PULAU SULAWESI MENGGUNAKAN ALGORITMA ST-DBSCAN (Spatio Temporal-Density Based Spatial Clustering Application with Noise)

Earthquake is a natural disaster which is quite serious in Indonesia, especially on Sulawesi Island. Earthquake is fearful because it can’t be predicted when it will come, where it will come, and how strong the vibration, that often causes fatal damage and casualties. In effort to minimize losses caused by earthquake, it is necessary to divide areas which are easily affected by earthquake. One of the methods that can be used in dividing the area is by using the clustering technique. This research by using a clustering method with the ST-DBSCAN (Spatial Temporal-Density Based Spatial Clustering Application with Noise) algorithm on dataset of earthquake points in Sulawesi Island in 2019. This method by using the spatial distance parameters (Eps1 = 0.45), the temporal distance parameters (Eps2 = 7), and minimum number of cluster members (MinPts = 4), resulting in a total of 60 clusters with 8 large clusters and 216 noises

A Play-Wide Performance Review of Plug and Perf, Ball and Seat, and Pinpoint Fracturing Methods in the Montney Tight Siltstone Reservoir

The Montney reservoir is one of the most prolific unconventional multi-stacked dry and liquid-rich gas plays in North America. The type of fracturing method and fluid has a significant impact on water-phase trapping, casing deformation, and well performance in the Montney. Different fracturing methods (plug and perf/plug and perf with ball/ball and seat/single-entry pinpoint) and fluids (slickwater/hybrid/oil-based/energized/foam) have been tested in 4000+ Montney wells to find optimal fracturing method and fluid for different reservoir qualities and fluid windows and to minimize water-phase trapping and casing deformation. The previous studies reviewing the performance of fracturing methods in Montney do not represent a holistic evaluation of these methods, due to some limitations, including: (1) Using a small sample size, (2) Having a limited scope by focusing on a specific aspect of fracturing (method/fluid), (3) Relying on data analytics approaches that offer limited subsurface insight, and (4) Generating misleading results (e.g., on optimum fracturing method/fluid) through using disparate data that are unstructured and untrustworthy due to significant regional variation in true vertical depth (TVD), geological properties, fluid windows, completed lateral length, fracturing method/fluid/date, and drawdown rate management strategy. The present study eliminates these limitations by rigorously clustering the 4000+ Montney wells based on the TVD, geological properties, fluid window, completed lateral length, fracturing method/fluid/date, and drawdown strategy. This clustering technique allows for isolating the effect of each fracturing method by comparing each well's production (normalized by proppant tonnage, fluid volume, and completed length) to that of its offsets that use different fracturing methods but possess similar geology and fluid window. With similar TVD and fracturing fluid/date, wells completed with pinpoint fracturing outperform their offsets completed with ball and seat and plug and perf fracturing. However, wells completed with ball and seat and plug and perf methods that outperform their offset pinpoint wells have either: (1) Been fractured 1 to 4 years earlier than pinpoint wells and/or (2) Used energized oil-based fluid, hybrid fluid, and energized slickwater versus slickwater used in pinpoint offsets, suggesting that the water-phase trapping is more severe in these pinpoint wells due to the use of slickwater. Previous studies often favored one specific fracturing method or fluid without highlighting these complex interplays between the type of fracturing method/fluid, completion date (regional depletion), and the reservoir properties and hydrodynamics. This clustering technique shows how proper data structuring in disparate datasets containing thousands of wells with significant variations in geological properties, fluid windows, fracturing method/fluid, regional depletion, and drawdown strategy permits a consistent well performance comparison across a play by isolating the impact of any given parameter (e.g., fracturing methods, depletion) that is deemed more crucial to fracturing design and field development planning.

Hierarchical clustering for multiple nominal data streams with evolving behaviour

Over the decade, a number of attempts have been made towards data stream clustering, but most of the works fall under clustering by example approach. There are a number of applications where clustering by variable approach is required which involves clustering of multiple data streams as opposed to clustering data examples in a data stream. Furthermore, a few works have been presented for clustering multiple data streams and these are applicable to numeric data streams only. Hence, this research gap has motivated current research work. In the present work, a hierarchical clustering technique has been proposed to cluster multiple data streams where data are nominal. To address the concept changes in the data streams splitting and merging of the clusters in the hierarchical structure are performed. The decision to split or merge is based on the entropy measure, representing the cluster’s degree of disparity. The performance of the proposed technique has been analysed and compared to Agglomerative Nesting clustering technique on synthetic as well as a real-world dataset in terms of Dunn Index, Modified Hubert $$\varGamma $$ Γ statistic, Cophenetic Correlation Coefficient, and Purity. The proposed technique outperforms Agglomerative Nesting clustering technique for concept evolving data streams. Furthermore, the effect of concept evolution on clustering structure and average entropy has been visualised for detailed analysis and understanding.

Clustering Users to Determine the Most Suitable Gamification Elements

The use of gamification elements has extended from being a complement for a product to being integrated into multiple public services to motivate the user. The first drawback for service designers is choosing which gamification elements are appropriate for the intended audience, in addition to the possible incompatibilities between gamification elements. This work proposes a clustering technique that enables mapping different user profiles in relation to their preferred gamification elements. Additionally, by mapping the best cluster for each gamification element, it is possible to determine the preferred game genre. The article answered the following research questions: What is the relationship between the genre of the game and the element of gamification? Different user groups (profiles) for each gamification element? Results indicate that there are cases where the users are divided between those who agree or disagree. However, other elements present a great heterogeneity in the number of groups and the levels of agreement.

Reduction of linear dynamic systems using generalized approach of pole clustering method

A new model order abatement method based on the clustering of poles and zeros of a large-scale continuous time system is proposed. The clustering of poles and zeros are used for finding the cluster centres. The abated model is identified from the cluster centres, which reflect the effectiveness of the dominant poles of the clusters. The cluster centre is determined by taking [Formula: see text] root of the sum of the inverse of [Formula: see text] power of poles (zeros) in a particular cluster. It is famous that the magnitude of the pole cluster centre plays an important role in the clustering technique for the simplification of large-scale systems. The magnitude of the cluster centres computed by the modified pole clustering method or some other methods based on the pole clustering techniques is large as compared to the proposed technique. The less magnitude of pole cluster centre reflects the better approximations and proper matching of the abated model with the original system. Therefore, the proposed method offers better approximations matching between actual and abated systems during the transient period compared to some other clustering methods, which supports the replacement of large-scale systems by proposed abated systems. The proposed technique is a generalized version of the standard pole clustering technique. The proposed method guarantees the retention of dominant poles, stability and other fundamental control properties of the actual plant in the abated model. The proposed algorithm is illustrated by the five standard systems taken from the literature. The accuracy and effectiveness of the proposed method are verified by comparing the time responses and various performance error indices.

Clustering technique for interpretation of cloudburst over Uttarakhand

Data Mining has been used extensively in various business and scientific applications for last few years. Data mining has been found to be providing a deep insight into understanding the hidden facts in huge databases. Data mining is an interdisciplinary subfield of computer science that discovers patterns in large data sets by using methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. In this paper, data mining technique for Interpretation of Weather Forecasts for one of the most disastrous weather phenomenon viz. cloudburst has been applied. Every year, cloudburst over hilly areas and coastal regions causes loss of lives and property. The forecasting and warning of these events is very difficult. There is no satisfactory technique for anticipating the occurrence of cloudbursts because of their small scale. A very fine network of radars is required to be able to detect the likelihood of a cloudburst and this would be prohibitively expensive. The warning of cloudburst could only be provided at a small lead time say a few hours in advance based on the interpretation of latest satellite imagery data, powerful radar (Doppler category), if available, or by using Model Output Statistics (MOS) models. Another dimension to forecasting this weather event has been identified by applying clustering technique on primary data forecasted by global and regional models of weather forecasting. A recent case of Cloudburst over Uttarakhand that caused a huge loss has been analyzed using k-means clustering technique of data mining. It has been observed that with the mining of Numerical Weather Prediction model forecast data, the signals of formation of cloudburst can be found3-4 days in advance.