Oil and gas properties (PVT) correlation using neural network

2019 ◽  
pp. 104-106
Author(s):  
F. Hadavimoghaddam ◽  
◽  
I.T. Mishchenko ◽  
Keyword(s):  
Neural Network ◽  
Oil And Gas ◽  
Gas Properties

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%.

Deformation characterization of oil and gas pipeline by ACM technique based on SSA-BP neural network model

Measurement ◽  
2021 ◽  
pp. 110654
Author(s):  
Jiaxing Xin ◽  
Jinzhong Chen ◽  
Chunyu Li ◽  
Runkun Lu ◽  
Xiaolong Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Oil And Gas ◽  
Gas Pipeline ◽  
Bp Neural Network Model

Assessing Wellbore Stability With a Modified Lade Failure Criterion

2020 ◽  
Vol 142 (8) ◽  
Author(s):  
Babak Bahrami ◽  
Ali Sadatshojaie ◽  
David A Wood
Keyword(s):  
Neural Network ◽  
Rock Mass ◽  
Failure Criterion ◽  
Oil And Gas ◽  
Poisson Ratio ◽  
Failure Criteria ◽  
Wellbore Stability ◽  
Depth Interval ◽  
Gas Reservoirs ◽  
Operational Processes

Abstract The importance of evaluating wellbore stability in analyzing and estimating the efficiency of drilling directionally into oil and gas reservoirs is well known. Geomechanical data and failure criterion can be used to model and control rock mass behavior in response to the stresses imposed upon it. Understanding and managing the risks of rock mass deformation significantly improve operational processes such as wellbore stability, sand production, and hydraulic fracturing. The modified Lade failure criterion is established as the most precise failure criterion based on previous studies. By combining it with tensions around the wellbore, a novel relationship is derived for determining the stable mud window. To investigate the accuracy of the new relationship, two geomechanical models (neural network and empirical correlations) for a one-directional wellbore are developed and their performance compared with two other failure criteria (Hoek–Brown and Mogi–Coulomb). The geomechanical parameters (Young’s modulus, Poisson ratio, uniaxial compressive strength, and internal friction coefficient) obtained from the models show that neural network configurations perform better than those built with the empirical equation. The horizontal minimum and maximum stress values across the depth interval of interest (2347–2500 m) are established for a case study reservoir. The model provides an accurate prediction of wellbore instability when applying the modified Lade criterion; the stable mud weight is derived with improved precision compared to the other failure criteria evaluated. A key advantage of the developed method is that it does not require input knowledge of the reservoir’s structural boundaries (e.g., the fault regime) or core test data.

A field-programmable gate array system for sonar image recognition based on convolutional neural network

2020 ◽  
pp. 095965182093934
Author(s):  
Chong Wang ◽  
Yu Jiang ◽  
Kai Wang ◽  
Fenglin Wei
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Convolutional Neural Network ◽  
Image Recognition ◽  
Field Programmable Gate Array ◽  
Oil And Gas ◽  
Sonar Image ◽  
Field Programmable ◽  
Sonar Images ◽  
Gate Array

Subsea pipeline is the safest, most reliable, and most economical way to transport oil and gas from an offshore platform to an onshore terminal. However, the pipelines may rupture under the harsh working environment, causing oil and gas leakage. This calls for a proper device and method to detect the state of subsea pipelines in a timely and precise manner. The autonomous underwater vehicle carrying side-scan sonar offers a desirable way for target detection in the complex environment under the sea. As a result, this article combines the field-programmable gate array, featuring high throughput, low energy consumption and a high degree of parallelism, and the convolutional neural network into a sonar image recognition system. First, a training set was constructed by screening and splitting the sonar images collected by sensors, and labeled one by one. Next, the convolutional neural network model was trained by the set on the workstation platform. The trained model was integrated into the field-programmable gate array system and applied to recognize actual datasets. The recognition results were compared with those of the workstation platform. The comparison shows that the computational precision of the designed field-programmable gate array system based on convolutional neural network is equivalent to that of the workstation platform; however, the recognition time of the designed system can be saved by more than 77%, and its energy consumption can also be saved by more than 96.67%. Therefore, our system basically satisfies our demand for energy-efficient, real-time, and accurate recognition of sonar images.

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