scholarly journals Artificial Neural Network Pada Industri Non Migas Sebagai Langkah Menuju Revolusi Industri 4.0

2019 ◽  
Vol 4 (1) ◽  
pp. 155-160
Author(s):  
Iin Parlina ◽  
Anjar Wanto ◽  
Agus Perdana Windarto
Keyword(s):  
Sensitivity Analysis ◽  
Oil And Gas ◽  
Oil Industry ◽  
Oil Processing ◽  
Software Model ◽  
Beverage Industry ◽  
Architectural Model ◽  
Architectural Models ◽  
Artificial Neural ◽  
The Republic

The research conducted aims to make predictions with artificial neural metwork (backpopagation) and sensitivity analysis in the non-oil processing industry for the value of industrial exports. Data was obtained from the Badan Pusat Statistik (BPS) in collaboration with the Ministry of Industry of the Republic of Indonesia in the last 7 years (2011-2017). The process is carried out by dividing the data into 2 parts (training and testing) to obtain the best architectural model. The data processing uses the help of Matlab 6.0 software. Model selection is done by try and try to get the best architectural model. In this study using 7 architectural models (15-2-1; 15-5-1; 15-10-1; 15-15-1; 15-2-5-1; 15-5-10-1 and 15- 10-5-1) who have been trained and tested. By using the help of Matlab 6.0 software, the best architectural model is obtained 15-2-1 with an accuracy rate of 93%, epoch training = 189,881, MSE testing = 0.001167108 and MSE training = 0,000999622. The best architecture will be continued to predict the non-oil industry based on the most dominant export value using sensitivity analysis. From the architectural model a prediction of 5 out of 15 non-oil and gas industries contributes: Food & Beverage Industry, Textile & Apparel Industry, Basic Metal Industry, Rubber Industry, Rubber and Plastic Goods and Metal Goods Industry, Not Machines and Equipment , Computers, Electronics and Optics.

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%.

JARINGAN SYARAF TIRUAN DALAM MEMPREDIKSI JUMLAH PRODUKSI DAGING SAPI BERDASARKAN PROVINSI

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Ahmad Revi ◽  
Solikhun Solikhun ◽  
M Safii
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Meat Production ◽  
Beef Production ◽  
Architectural Model ◽  
Architectural Models ◽  
Artificial Neural ◽  
Input Variables ◽  
The Government ◽  
Target Data

Prediction is a process for estimating how many needs will be in the future. This study aims to predict the amount of beef production by province. Beef is one source of protein which is also a high value comodities. Meat production in Indonesia in general tends to increase by around 2.76% per year. But along with the increase in beef production in Indonesia, the level of meat consumption in Indonesia tends to fluctuate in recent years. Imports are the most common step taken by the government to meet domestic beef needs. By using the Artificial Neural Network and backpropagation algorithm, it will be predicted the amount of beef production based on the province in order to determine the steps to meet domestic beef demand based on the amount of beef consumption in the community. This study uses 11 input variables, namely data from 2005 to 2016 with 1 target, data of 2017. Using 5 architectural models to test the data to be used for prediction, the 11-4-1 model, 11-8-1 , 11-18-1, 11-20-1 and 11-28-1. Obtained the results of the best architectural model is the 11-28-1 architectural model with truth accuracy of 100%, the number of epochs 15 and MSE is 0.008623197. This model will be used in predicting the amount of beef production by province.Keywords : Beef production, prediction, backpropagatin, Artificial Neural Network

Drilling and Service Contracts in Offshore Oil and Gas Operations

1973 ◽  
Vol 11 (3) ◽  
pp. 480
Author(s):  
J. M. Killey
Keyword(s):  
Oil And Gas ◽  
Oil Industry ◽  
Offshore Drilling ◽  
Oil Companies ◽  
Drilling Rig ◽  
Service Contracts ◽  
Oil And Gas Operations ◽  
Offshore Oil And Gas ◽  
Offshore Oil ◽  
Offshore Operations

As onshore oil and gas deposits are becoming more difficult to locate, and as the world demands for energy continue to increase at an alarming rate, oil companies are channeling much of their exploration activities towards offshore operations, and in particular, towards operations centered off Canada's coast lines. Because of the environment, offshore drilling presents problems which are novel to the onshore-geared oil industry. J. M. Killey discusses in detail many of the considerations involved in drafting the offshore drilling contract, concentrating on problems such as the liability of the various parties; costs; scheduling; pollution; conflict of laws; etc. Similarly, he discusses service contracts (such as supply boat charters; towing services; helicopter services; etc.^ which are necessity to the operation of an offshore drilling rig. To complement his paper, the author has included number of appendices which list the various considerations lawyer must keep in mind when drafting contracts for offshore operations.

THE PROBLEMS OF CRIMINAL LAW PROTECTION OF THE OIL INDUSTRY OF THE RUSSIAN FEDERATION

2020 ◽  
pp. 14-20
Author(s):  
Сергей Иванович Вележев ◽  
Антон Михайлович Седогин
Keyword(s):  
Public Relations ◽  
Russian Federation ◽  
Criminal Law ◽  
Oil Industry ◽  
Legal Analysis ◽  
The Russian Federation ◽  
The Republic ◽  
Illegal Activities ◽  
Business Entities ◽  
Criminal Groups

В статье рассмотрены актуальные вопросы уголовно-правовой охраны нефтяной отрасли Российской Федерации от преступных посягательств корыстной направленности. Иллюстрирован существенный ущерб, причиняемый преступными группами охраняемым общественным отношениям на национальном и международном уровнях. Проведен статистический и сравнительно-правовой анализ наиболее эффективных норм законодательства России и Казахстана, применяемых в ходе борьбы с подобной противоправной деятельностью. Предложено направление дальнейшего совершенствования российского уголовного закона. Нефтяная промышленность является одной из ведущих отраслей Российской Федерации, структурными сегментами которой являются в том числе объекты добычи, хранения, переработки и транспортировки нефти, а также объекты транспортировки, хранения и сбыта нефтепродуктов. Данные обстоятельства требуют принятия мер по ее защите от противоправных действий по хищению нефти и нефтепродуктов. Наряду с охранными, режимными и организационными мерами, которые осуществляют хозяйствующие субъекты, немаловажное значение имеет защита отрасли от преступных посягательств уголовно-правовым способом. В статье указывается необходимость совершенствования законодательства по обеспечению безопасности деятельности нефтяной отрасли, учитывая ее значение для экономики страны. Отмечается, что положительные результаты в поиске возможных путей совершенствования законодательства дает применение сравнительно-правового анализа уголовных норм СНГ по борьбе с преступностью в этой сфере деятельности. The article examines current issues of the criminal law protection of the oil industry of the Russian Federation from criminal attacks for mercenary reasons. The considerable damage caused by criminal groups to protected public relations at the national and international levels is illustrated. A statistical and comparative legal analysis of the most effective norms of the legislation of Russia and the Republic of Kazakhstan applied in the fight against such illegal activities has been carried out. The direction of further improvement of the Russian criminal law is proposed. The oil industry is one of the leading industries of the Russian Federation, the structural segments of that are the objects of oil production, storage, refining and transportation, as well as the objects of transportation, storage and marketing of oil product. Under these circumstances it is required totake measures for protection it from unlawful actions connected with stealing of oil and oil products. Along with security, safeguards and organizational measures that are implemented by business entities, protection of the industry from criminal attacks by a criminal law method is of no small importance. The article indicates the need to improve legislation to ensure the safety of the oil industry, based on its importance for the country's economy. It is noted that positive results in the search for possible ways to improve the legislation are provided by the use of a comparative legal analysis of the criminal norms of the CIS in the fight against crime in this area of activity.

Ranking and Sensitivity Analysis of Key Factors for Successful Project Management Performance: An Application of AHP for Oil and Gas Sector

2018 ◽  
Vol 6 (2) ◽  
Author(s):  
Farhaj Ishtiaq ◽  
Mirza Jahanzaib
Keyword(s):  
Sensitivity Analysis ◽  
Project Management ◽  
Analytical Hierarchy Process ◽  
Oil And Gas ◽  
Planning Process ◽  
Project Planning ◽  
Key Factors ◽  
Management Performance ◽  
Hierarchy Process ◽  
Oil And Gas Sector

<p>Complexities faced by oil and gas projects due to uncertainty and risk, demand the implementation of project management techniques for their successful completion. Therefore, this is made by using analytical hierarchy process, to identify and prioritize the key factors for successful project management performance of oil and gas projects. These factors are categorized into three groups which include attributes of project staff, project planning process and assessment of project quality. Using expert choice, a hierarchy is developed followed by pairwise comparison based upon data collection from industrial experts of oil and gas sector. Results of analytical hierarchy process (AHP) concluded that, project completion within estimated time and budget, clarity of objectives and involvement of top management are most crucial elements for improvement in project management performance of oil and gas projects. Whereas sensitivity analysis being carried out according to three different scenarios highlighted factors according to their relative importance.</p>

Field Implementation of RBI for Jacket Structures

2003 ◽  
Author(s):  
Michael H. Faber ◽  
Daniel Straub ◽  
John D. So̸rensen ◽  
Jesper Tychsen
Keyword(s):  
Sensitivity Analysis ◽  
Data Base ◽  
Stress Ratio ◽  
Oil And Gas ◽  
Inspection Planning ◽  
The North ◽  
Large Numbers ◽  
Structural Details ◽  
Jacket Structures ◽  
The North Sea

The present paper first gives a brief outline of the simplified and generic approach to reliability and risk based inspection planning and thereafter sets focus on a recent application of the methodology for planning of in-service NDT inspections of the fixed offshore steel jacket structures in the DUC concession area in the Danish part of the North-Sea. The platforms are operated by Maersk Oil and Gas on behalf of DUC partners A.P. Mo̸ller, Shell and Texaco. The study includes a sensitivity analysis performed for the identification of relevant generic parameters such as the bending to membrane stress ratio, the design fatigue life and the material thickness. Based on the results of the sensitivity analysis a significant number of inspection plans were computed for fixed generic parameters (pre-defined generic plans) and a data-base named iPlan was developed from which inspection plans may be obtained by interpolation between the pre-defined generic plans. The iPlan data-base facilitates the straightforward production of large numbers of inspection plans for structural details subject to fatigue deterioration. In the paper the application of the generic inspection plan database iPlan is finally illustrated on an example.

Core Scale FEM Modeling of Thermochemical Fracturing on Cement Cube Samples

2021 ◽  
Author(s):  
Zeeshan Tariq ◽  
Ayman AlNakhli ◽  
Abdulazeez Abdulraheem ◽  
Mohamed Mahmoud
Keyword(s):  
Sensitivity Analysis ◽  
Global Economy ◽  
Oil And Gas ◽  
Peak Pressure ◽  
Exothermic Reaction ◽  
Cohesive Zone Modeling ◽  
Fem Modeling ◽  
Breakdown Pressure ◽  
Multiple Fractures ◽  
Micro Cracks

Abstract Brownfields and depleting conventional resources of fossil fuel energy are not enough to fulfill the tremendously increasing energy demands around the globe. Unconventional oil and gas resources are creating a huge impact on the enhancement of the global economy. Tight rocks are usually located in deep and high-strength formations. In this study, numerical simulation results on a new thermochemical fracturing approach is presented. The new fracturing approach was implemented to reduce the breakdown pressure of the unconventional tight formations. The hydraulic fracturing experiments presented in this study were carried out on ultra-tight cement block samples. The permeability of the block samples was less than 0.005mD. Thermochemical fracturing was carried out by a thermochemical fluids that caused a rapid exothermic reaction which resulted in the instantaneous generation of heat and pressure. Different salts of nitrogen such as sodium nitrite and ammonium chloride were used as a thermochemical fluid. The instantaneous generation of the heat and pressure caused the creation of micro-cracks. The fracturing results revealed that the novel thermochemical fracturing was able to reduce the breakdown pressure in ultra-tight cement from 1095 psi to 705 psi. The reference breakdown pressure was recorded from the conventional fracturing technique. A finite element (FEM) analysis was conducted using commercial software ABAQUS. In FEM, two approaches were used to model the thermochemical fractures namely, cohesive zone modeling (CZM) and concrete damage plasticity models (CDP). The sensitivity analysis of peak pressure and time to reach the peak pressure is also presented in this study. The sensitivity analysis can help in better designing thermochemical fluids that could lead to the maximum generation of micro-cracks and multiple fractures.

Nanoemulsions: A Versatile Technology for Oil and Gas Applications

2021 ◽  
Author(s):  
Nouf AlJabri ◽  
Nan Shi
Keyword(s):  
Droplet Size ◽  
Large Scale ◽  
Oil And Gas ◽  
Volume Fraction ◽  
Oil Industry ◽  
Oil And Gas Industry ◽  
High Energy ◽  
Small Droplet ◽  
Gas Industry ◽  
Wide Range

Abstract Nanoemulsions (NEs) are kinetically stable emulsions with droplet size on the order of 100 nm. Many unique properties of NEs, such as stability and rheology, have attracted considerable attention in the oil industry. Here, we review applications and studies of NEs for major upstream operations, highlighting useful properties of NEs, synthesis to render these properties, and techniques to characterize them. We identify specific challenges associated with large-scale applications of NEs and directions for future studies. We first summarize useful and unique properties of NEs, mostly arising from the small droplet size. Then, we compare different methods to prepare NEs based on the magnitude of input energy, i.e., low-energy and high-energy methods. In addition, we review techniques to characterize properties of NEs, such as droplet size, volume fraction of the dispersed phase, and viscosity. Furthermore, we discuss specific applications of NEs in four areas of upstream operations, i.e., enhanced oil recovery, drilling/completion, flow assurance, and stimulation. Finally, we identify challenges to economically tailor NEs with desired properties for large-scale upstream applications and propose possible solutions to some of these challenges. NEs are kinetically stable due to their small droplet size (submicron to 100 nm). Within this size range, the rate of major destabilizing mechanisms, such as coalescence, flocculation, and Ostwald ripening, is considerably slowed down. In addition, small droplet size yields large surface-to-volume ratio, optical transparency, high diffusivity, and controllable rheology. Similar to applications in other fields (food industry, pharmaceuticals, cosmetics, etc.), the oil and gas industry can also benefit from these useful properties of NEs. Proposed functions of NEs include delivering chemicals, conditioning wellbore/reservoir conditions, and improve chemical compatibility. Therefore, we envision NEs as a versatile technology that can be applied in a variety of upstream operations. Upstream operations often target a wide range of physical and chemical conditions and are operated at different time scales. More importantly, these operations typically consume a large amount of materials. These facts not only suggest efforts to rationally engineer properties of NEs in upstream applications, but also manifest the importance to economically optimize such efforts for large-scale operations. We summarize studies and applications of NEs in upstream operations in the oil and gas industry. We review useful properties of NEs that benefit upstream applications as well as techniques to synthesize and characterize NEs. More importantly, we identify challenges and opportunities in engineering NEs for large-scale operations in different upstream applications. This work not only focuses on scientific aspects of synthesizing NEs with desired properties but also emphasizes engineering and economic consideration that is important in the oil industry.

Artificial Intelligence and Robotics in the Oil Industry: Will it Take My Job?

2021 ◽  
Author(s):  
Armstrong Lee Agbaji
Keyword(s):  
Artificial Intelligence ◽  
Oil And Gas ◽  
New Technologies ◽  
Oil Industry ◽  
Oil And Gas Industry ◽  
Human Robot Interaction ◽  
Gas Industry ◽  
Deep Dive ◽  
The Future ◽  
And Robotics

Abstract Historically, the oil and gas industry has been slow and extremely cautious to adopt emerging technologies. But in the Age of Artificial Intelligence (AI), the industry has broken from tradition. It has not only embraced AI; it is leading the pack. AI has not only changed what it now means to work in the oil industry, it has changed how companies create, capture, and deliver value. Thanks, or no thanks to automation, traditional oil industry skills and talents are now being threatened, and in most cases, rendered obsolete. Oil and gas industry day-to-day work is progressively gravitating towards software and algorithms, and today’s workers are resigning themselves to the fact that computers and robots will one day "take over" and do much of their work. The adoption of AI and how it might affect career prospects is currently causing a lot of anxiety among industry professionals. This paper details how artificial intelligence, automation, and robotics has redefined what it now means to work in the oil industry, as well as the new challenges and responsibilities that the AI revolution presents. It takes a deep-dive into human-robot interaction, and underscores what AI can, and cannot do. It also identifies several traditional oilfield positions that have become endangered by automation, addresses the premonitions of professionals in these endangered roles, and lays out a roadmap on how to survive and thrive in a digitally transformed world. The future of work is evolving, and new technologies are changing how talent is acquired, developed, and retained. That robots will someday "take our jobs" is not an impossible possibility. It is more of a reality than an exaggeration. Automation in the oil industry has achieved outcomes that go beyond human capabilities. In fact, the odds are overwhelming that AI that functions at a comparable level to humans will soon become ubiquitous in the industry. The big question is: How long will it take? The oil industry of the future will not need large office complexes or a large workforce. Most of the work will be automated. Drilling rigs, production platforms, refineries, and petrochemical plants will not go away, but how work is done at these locations will be totally different. While the industry will never entirely lose its human touch, AI will be the foundation of the workforce of the future. How we react to the AI revolution today will shape the industry for generations to come. What should we do when AI changes our job functions and workforce? Should we be training AI, or should we be training humans?

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