FORECAST OF TIME-PERIODS BETWEEN EMERGENCY SITUATIONS AND FAILURES DURING OIL AND GAS WELLS DRILLING BY USING AN ARTIFICIAL NEURAL NETWORK

2019 ◽  
pp. 52-61
Author(s):  
O.V. Zakharov ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oil And Gas ◽  
Gas Wells ◽  
Emergency Situations ◽  
Oil And Gas Wells ◽  
Time Periods ◽  
Artificial Neural

scholarly journals Analisis Jaringan Syaraf Tiruan untuk prediksi volume ekspor dan impor migas di Indonesia

2018 ◽  
Vol 4 (1) ◽  
pp. 30
Author(s):  
Yuli Andriani ◽  
Hotmalina Silitonga ◽  
Anjar Wanto
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Crude Oil ◽  
Oil And Gas ◽  
Oil Products ◽  
Oil Exports ◽  
Directorate General ◽  
Architectural Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Analisis pada penelitian penting dilakukan untuk tujuan mengetahui ketepatan dan keakuratan dari penelitian itu sendiri. Begitu juga dalam prediksi volume ekspor dan impor migas di Indonesia. Dilakukannya penelitian ini untuk mengetahui seberapa besar perkembangan ekspor dan impor Indonesia di bidang migas di masa yang akan datang. Penelitian ini menggunakan Jaringan Syaraf Tiruan (JST) atau Artificial Neural Network (ANN) dengan algoritma Backpropagation. Data penelitian ini bersumber dari dokumen kepabeanan Ditjen Bea dan Cukai yaitu Pemberitahuan Ekspor Barang (PEB) dan Pemberitahuan Impor Barang (PIB). Berdasarkan data ini, variabel yang digunakan ada 7, antara lain: Tahun, ekspor minyak mentah, impor minyak mentah, ekspor hasil minyak, impor hasil minyak, ekspor gas dan impor gas. Ada 5 model arsitektur yang digunakan pada penelitian ini, 12-5-1, 12-7-1, 12-8-1, 12-10-1 dan 12-14-1. Dari ke 5 model yang digunakan, yang terbaik adalah 12-5-1 dengan menghasilkan tingkat akurasi 83%, MSE 0,0281641257 dengan tingkat error yang digunakan 0,001-0,05. Sehingga model ini bagus untuk memprediksi volume ekspor dan impor migas di Indonesia, karena akurasianya antara 80% hingga 90%.   Analysis of the research is Imporant used to know precision and accuracy of the research itself. It is also in the prediction of Volume Exports and Impors of Oil and Gas in Indonesia. This research is conducted to find out how much the development of Indonesia's exports and Impors in the field of oil and gas in the future. This research used Artificial Neural Network with Backpropagation algorithm. The data of this research have as a source from custom documents of the Directorate General of Customs and Excise (Declaration Form/PEB and Impor Export Declaration/PIB). Based on this data, there are 7 variables used, among others: Year, Crude oil exports, Crude oil Impors, Exports of oil products, Impored oil products, Gas exports and Gas Impors. There are 5 architectural models used in this study, 12-5-1, 12-7-1, 12-8-1, 12-10-1 and 12-14-1. Of the 5 models has used, the best models is 12-5-1 with an accuracy 83%, MSE 0.0281641257 with error rate 0.001-0.05. So this model is good to predict the Volume of Exports and Impors of Oil and Gas in Indonesia, because its accuracy between 80% to 90%.

Prediction of Pipe Wrinkling Using Artificial Neural Network

2010 ◽  
Author(s):  
Z. L. Chou ◽  
J. J. R. Cheng ◽  
Joe Zhou
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oil And Gas ◽  
Warning System ◽  
Complex Loading ◽  
Normal Operation ◽  
Ann Model ◽  
Buried Pipelines ◽  
Artificial Neural ◽  
Distributed Strain

As the demand for oil and gas resources increases pipeline construction pushes further into the geologically unstable Arctic and sub-Arctic regions. Consequently, these buried pipelines suffer much harsh environmental and complex loading conditions. In addition, higher strength and larger size pipes with higher operation pressure are used gradually. These severe and unknown conditions increase the risk of pipeline failure, especially, local buckling (wrinkling) failure. The wrinkling failure and sequential pipe fracture can cause enormous cost loss as well as high risk in safety and environmental impact. In the past, to prevent the buried pipelines from buckling failure, the pipeline maintenance was processed by periodical inspections and excavations in the field. The whole procedure is expansive and time consuming, and has no active warning system for possible failures between the inspection periods. Therefore, to overcome these problems, an automatic warning system for monitoring pipeline buckling is developed. A damage detection model (DDM) with artificial neural network (ANN) is a kern of the warning system and discussed in this paper. The proposed DDM will allow engineers to diagnose the pipe condition reliably and continuously without interrupt the normal operation of buried pipelines. The proposed DDM successfully identifies the distributed strain patterns in local characteristics as well as global trend. Some significant findings in the ANN model working with distributed strain patterns of the pipes are discussed, and a guideline of applying the DDM to the field pipe is also presented in this paper.

scholarly journals Lithofacial Zoning of Producing Horizons of Oil and Gas Fields Using Artificial Neural

2020 ◽  
pp. 96-105
Author(s):  
І. О. Fedak ◽  
Ya. М. Koval
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oil And Gas ◽  
Pore Space ◽  
Development Project ◽  
Core Material ◽  
Geological Model ◽  
Reservoir Properties ◽  
Gas Field ◽  
Artificial Neural

The quality of an oil and gas field development project depends greatly on the accuracy of forecasting the processes that occur in the pore space of reservoirs during the extraction of hydrocarbons under certain technolo-gical conditions in production wells. The forecasting is possible if there is a geological model of the field. The more detailed the model is, the more accurate the prediction will be. The whole amount of information used to create a geological model of a field is of discrete nature, and its level of detail is determined by the number of wells that have discovered pay formations. One of the most important elements of the geological model is the nature of changes in reservoir properties of productive formations along their stretch and perpendicular to bedding. The creation of elements of this type requires information from laboratory studies of core material, interpretation of the wells logging results and methods for predicting the nature of changes in reservoir properties in the interwell space. The presence of these elements makes it possible to investigate the situation in which sedimentation (within the existing wells) took place and what types of facies the geological sections of the drilled producing intervals correspond to. Lithofacial zoning of the productive formation according to this information makes it possible to trace the regularities of distribution of facies of various types, to establish their mutual location, and accordingly to predict the nature of changes in reservoir properties in the interwell space. The lack of a sufficient amount of core material is a typical problem that makes it difficult to identify facies. There is another way to solve this problem – this is the identification of facies according to the morphology of logging curves. Nowadays, this problem is solved at a qualitative level. In this paper, it is proposed to apply a quantitative method for identifying facies using an artificial neural network. In particular, the morphology of curves is formalized by a number of parameters that form the input vector of an artificial neural network. At the output of the network, the clusters of logging curves with a similar morpho-logy are formed. The authors refer these clusters to a certain type of facies analytically. On the basis of the information obtained, lithofacial zoning of the productive formations is carried out.

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