<em>Aiming for productive fault in Suban Agung Rim, Field X Cluster Y Bengkulu Province, Indonesia, PT Pertamina Geothermal Energy (PGE) drilled some wells to discover the potential awaited. However there are challenge awaits each meter ahead especially in reservoir sections where loss circulation is expected. Knowing the risk, PGE decided to drill the well utilizing aerated drilling. The method has known for decades to be the most effective approach in dealing loss circulation. The method applies certain value of compressed air to be injected in fluid stream, so bubbling process can be achieved in order to reduce the mud weight. The method has benefit to maintain ROP and minimize the risk for pipe get stuck due to poor hole cleaning in fractured formation. There are three wells drilled in Field X Cluster Y which has the same problem in 9-7/8” section; all experienced stuck pipe while drilling. During the process aerated drilling was utilized, however it was not sufficient. This paper will discuss and explain on how the occurrence happened and what to do next in similar condition to avoid the problems.</em>

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Violence may happen anywhere. One of the ways to know and oversee the violence in some places is by installing Closed-circuit Television (CCTV). The recorded video captured by CCTV can be used as proof in a law court. Violence video classification is also one of the topics being discussed in deep learning. The latest violence video dataset is RWF-2000. That dataset contains violent and non-violent videos, 5 seconds duration, 30 frames per second, with the amount of 2000 videos. That publication also has the best accuracy of 87.25% by their proposed method. In this study, we will use a Residual Network known to have the advantage of solving the vanishing gradient problem. Beside that, we also implement transfer learning from Kinetics and Kinetics + Moments in Time pre-trained data. We also test the number of frames and the location of the sampling frame range. RGB and optical flow inputs are separately trained with different configurations. The RGB input best accuracy is 89.25% with pre-trained Kinetics + Moments in Time, using frame location 49-149. The optical flow input best accuracy is 88.5% with pre-trained Kinetics, using 74 frames. We also try to sum the output of both inputs making accuracy of 90.5%.

Violence may happen anywhere. One of the ways to know and overseethe violence in some places is by installing Closed-circuit Television(CCTV). The recorded video captured by CCTV can be used as proofin a law court. Violence video classification is also one of the topicsbeing discussed in deep learning. The latest violence video dataset isRWF-2000. That dataset contains violent and non-violent videos, 5 secondsduration, 30 frames per second, with the amount of 2000 videos.That publication also has the best accuracy of 87.25% by their proposedmethod. In this study, we will use a Residual Network known tohave the advantage of solving the vanishing gradient problem. Besidethat, we also implement transfer learning from Kinetics and Kinetics+ Moments in Time pre-trained data. We also test the numberof frames and the location of the sampling frame range. RGB andoptical flow inputs are separately trained with different configurations.The RGB input best accuracy is 89.25% with pre-trained Kinetics +Moments in Time, using frame location 49-149. The optical flow inputbest accuracy is 88.5% with pre-trained Kinetics, using 74 frames. Wealso try to sum the output of both inputs making accuracy of 90.5%.

Violence may happen anywhere. One of the ways to know and oversee the violence in some places is by installing Closed-circuit Television (CCTV). The recorded video captured by CCTV can be used as proof in a law court. Violence video classification is also one of the topics being discussed in deep learning. The latest violence video dataset is RWF-2000. That dataset contains violent and non-violent videos, 5 seconds duration, 30 frames per second, with the amount of 2000 videos. That publication also has the best accuracy of 87.25% by their proposed method. In this study, we will use a Residual Network known to have the advantage of solving the vanishing gradient problem. Beside that, we also implement transfer learning from Kinetics and Kinetics + Moments in Time pre-trained data. We also test the number of frames and the location of the sampling frame range. RGB and optical flow inputs are separately trained with different configurations. The RGB input best accuracy is 89.25% with pre-trained Kinetics + Moments in Time, using frame location 49-149. The optical flow input best accuracy is 88.5% with pre-trained Kinetics, using 74 frames. We also try to sum the output of both inputs making accuracy of 90.5%.