Tunu and Tambora gas fields are located in the Mahakam river delta in the province of East Kalimantan, Indonesia. The fields consist of wet gas bearing sand bodies over a height of 13000 ft. The main producing zones are developed by intensive drilling with wells simply completed to allow a bottom up perforation strategy. The main objective is gas production from the deeper Main Zone layers. The shallow reservoirs prone to sand production are not primarily targeted. When sand production after additional perforation is observed, gas production is normally limited to maximum sand free rates or the wells are shut in to avoid damage to surface equipment. Sand consolidation has been used as a sand control method since the 1940’s. However, it had never been attempted in operator’s fields in Indonesia. To author’s knowledge sand consolidation is not commonly used in South East Asia, in general. Unlike widely used conventional sand control methods this alternative method allows production from sand prone reservoirs while maintaining full wellbore access below treated zones. The treatments presented in this paper were to validate sand consolidation as a viable sand control option in operator’s fields in the Mahakam Delta, utilizing new internally catalyzed epoxy consolidation fluid. The treatments were performed with 1.75’’ coil tubing and a packer. To date three Tunu/Tambora wells have been treated. The treated reservoirs have been producing without sand production after treatment. This paper describes candidate selection, job execution and treatment results.

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Social media has a significant role in natural disaster management, namely as an early warning and monitoring when natural disasters occur. Artificial intelligence can maximize the use of natural disaster social media messages for natural disaster management. The artificial intelligence system will classify social media message texts into three categories: eyewitness, non-eyewitness and don't-know. Messages with the eyewitness category are essential because they can provide the time and location of natural disasters. A common problem in text classification research is that feature extraction techniques ignore word meanings, omit word order information and produce high-dimensional data. In this study, a feature extraction technique can maintain word order information and meaning by using three-word embedding techniques, namely word2vec, fastText, and Glove. The result is data with 1D, 2D, and 3D dimensions. This study also proposes a data formation technique with new features by combining data from all word embedding techniques. The classification model is made using three Convolutional Neural Network (CNN) techniques, namely 1D CNN, 2D CNN and 3D CNN. The best accuracy results in this study were in the case of earthquakes 78.33%, forest fires 81.97%, and floods 78.33%. The calculation of the average accuracy shows that the 2D and 3D v1 data formation techniques work better than other techniques. Other results show that the proposed technique produces better average accuracy.

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Tanaman jarak pagar merupakan tanaman multi fungsi yang memiliki banyak manfaat dari daun hingga buah. Tanaman jarak pagar sering digunakan untuk produk kecantikan hingga pengganti biodiesel. Penyakit yang menyerang tanaman jarak pagar dapat mengganggu hasil dari tanaman jarak pagar. Kurangnya pakar dibidang ini dan pengetahuan yang dimiliki petani menyebabkan sesuatu yang buruk. Persoalan ini dapat diselesaikan dengan metode Deep Learning. Metode Deep Learning yang digunakan adalah H2O. H2O digunakan karena dapat memberikan hasil komputasi yang cepat dan bisa memberikan akurasi yang baik. Pada penelitian ini bisa kita lihat bahwa H2O memberikan akurasi rata-rata maksimal sebesar 96,066% dengan parameter uji kombinasi data latih dan data uji 60:40, menggunakan satu layer dan jumlah epoch sebanyak 100. Pada penelitian ini membuktikan bahwa H2O bisa digunakan untuk identifikasi penyakit tanaman jarak pagar