scholarly journals APPLICATION OF ARTIFICIAL NEURAL NETWORKS FOR REDUCING DIMENSIONS OF GEOLOGICAL-GEOPHYSICAL DATA SET'S FOR THE IDENTIFICATION OF PERSPECTIVE OIL AND GAS DEPOSITS

2020 ◽  
Author(s):  
Maxim Krasnyuk ◽  
Svitlana Krasniuk
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Oil And Gas ◽  
Geophysical Data ◽  
Artificial Neural

Drilling Rate of Penetration Prediction of High-Angled Wells Using Artificial Neural Networks

2019 ◽  
Vol 141 (11) ◽  
Author(s):  
Ahmed K. Abbas ◽  
Salih Rushdi ◽  
Mortadha Alsaba ◽  
Mohammed F. Al Dushaishi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Oil And Gas ◽  
Complex Problem ◽  
Real Field ◽  
Operational Parameters ◽  
Rate Of Penetration ◽  
Weight On Bit ◽  
Artificial Neural ◽  
Deviated Wells

Predicting the rate of penetration (ROP) is a significant factor in drilling optimization and minimizing expensive drilling costs. However, due to the geological uncertainty and many uncontrolled operational parameters influencing the ROP, its prediction is still a complex problem for the oil and gas industries. In the present study, a reliable computational approach for the prediction of ROP is proposed. First, fscaret package in a R environment was implemented to find out the importance and ranking of the inputs’ parameters. According to the feature ranking process, out of the 25 variables studied, 19 variables had the highest impact on ROP based on their ranges within this dataset. Second, a new model that is able to predict the ROP using real field data, which is based on artificial neural networks (ANNs), was developed. In order to gain a deeper understanding of the relationships between input parameters and ROP, this model was used to check the effect of the weight on bit (WOB), rotation per minute (rpm), and flow rate (FR). Finally, the simulation results of three deviated wells showed an acceptable representation of the physical process, with reasonable predicted ROP values. The main contribution of this research as compared to previous studies is that it investigates the influence of well trajectory (azimuth and inclination) and mechanical earth modeling parameters on the ROP for high-angled wells. The major advantage of the present study is optimizing the drilling parameters, predicting the proper penetration rate, estimating the drilling time of the deviated wells, and eventually reducing the drilling cost for future wells.

Application of artificial neural networks in oil and gas multiphase metering

2006 ◽  
Author(s):  
Huimin Yang ◽  
Yuxing Li ◽  
Fuxian Zhou ◽  
Jian Zhang ◽  
Shouping Dong
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Oil And Gas ◽  
Artificial Neural

scholarly journals Neural network technique when distribution of vehicle component parts on the technological repair routes, taking into account their technical condition

2018 ◽  
Vol 170 ◽  
pp. 05011
Author(s):  
Valentin Krasovsky ◽  
Nina Krasovskaya ◽  
Victor Poptsov ◽  
Irina Nordman
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Oil And Gas ◽  
Technical Condition ◽  
Probabilistic Methods ◽  
Practical Application ◽  
Repair Work ◽  
Vehicle Component ◽  
Repair Enterprise ◽  
Artificial Neural

Increase of repair efficiency is achieved due to the formation of centralized specialized production facilities which implement the vehicle component parts repair technique with the use of industrial technological processes to restore the technical state of the units and their components. In this case, the establishment of the expediency of sending the unit to repair, as well as the defining of volumes and nomenclature for necessary repair actions, should be performed at the stage of pre-repair diagnosis for each individual unit taking into account its actual technical condition. However, the effectiveness of pre-repair diagnosis using both deterministic and probabilistic methods of processing and analyzing the information obtained is significantly reduced by the presence of errors in the recognition of defects and the distribution of aggregates in accordance with the repair work variety preformed at the repair enterprise. Using promising cognitive technology based on neural networks it is possible to completely avoid the losses associated with the repetition of repair work. Therefore, the formation of scientific and methodological bases for the development, training and practical application of artificial neural networks in the subsystems of the pre-repair diagnosis of the repair fund of automobile vehicle omponent parts is an important and urgent task. The paper presents the results of analytical studies and a number of original techniques for the formation of scientific and methodological foundations for the development, training and practical application of artificial neural networks in the process of diagnosis the car vehicle component parts and special oil and gas equipment entering the centralized repair according to their technical condition

scholarly journals Using of artificial neural networks to assess the residual resource of trunk pipelines

2021 ◽  
Vol 225 ◽  
pp. 02006
Author(s):  
Ilya Lapiga ◽  
Andrey Shchipachev ◽  
Dmitriy Osadchiy
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Oil And Gas ◽  
Residual Life ◽  
Empirical Relationship ◽  
Metal Structure ◽  
Cyclic Fatigue ◽  
Life Assessment ◽  
Diagnostic Parameters ◽  
Artificial Neural

A large number of oil and gas pipelines in the Russian Federation have been in operation for over 20 years. For these pipelines, the issue of assessing the residual resource is relevant. Today, much attention is paid to the problem of long-term durability of pipelines. Trunk pipelines are under the influence of cyclic loads and influences arising during operation. The acting stresses in the pipe wall do not exceed the allowable ones, however, they cause micro-damage to the metal structure. When assessing the cyclic fatigue of a metal, the main criterion is the relative damage to the metal. The use of non-destructive testing methods (ultrasonic and magnetic), as well as the establishment of a relationship between the number of cycles and diagnostic parameters, will improve the accuracy of the residual life assessment. When analyzing several diagnostic parameters, the question of data interconnection becomes relevant. Since establishing an empirical or semi-empirical relationship between ultrasonic and magnetic properties is a complex task, artificial neural networks (ANNs) can be used to solve this problem. The use of ANN in the diagnostics of trunk pipelines will increase the accuracy of the assessment and eliminate the subjectivity of data interpretation.

A NEW TECHNIQUE TO PREDICT THE FRACTURES DIP USING ARTIFICIAL NEURAL NETWORKS AND IMAGE LOGS DATA

2015 ◽  
Vol 75 (11) ◽  
Author(s):  
Mostafa Alizadeh ◽  
Zohreh Movahed ◽  
Radzuan Junin ◽  
Rahmat Mohsin ◽  
Mehdi Alizadeh ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Oil And Gas ◽  
Back Propagation ◽  
Learning Rule ◽  
The Other ◽  
New Technique ◽  
Ann Model ◽  
Artificial Neural ◽  
A New Technique

Fractures provide the place for oil and gas to be reserved and they also can provide the pathway for them to move into the well, so having a proper knowledge of them is essential and every year the companies try to improve the existed softwares in this technology. In this work, the new technique is introduced to be added as a new application to the existed softwares such as Petrel and geoframe softwares. The data used in this work are image logs and the other geological logs data of tree wells located in Gachsaran field, wells number GS-A, GS-B and GS-C. The new technique by using the feed-forward artificial neural networks (ANN) with back-propagation learning rule can predict the fracture dip data of the third well using the data from the other 2 wells. The result obtained showed that the ANN model can simulate the relationship between fractures dips in these 3 wells which the multiple R of training and test sets for the ANN model is 0.95099 and 0.912197, respectively.

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