Project centers in Chennai

IEEE Final Year Project Topics for CSE

Base Paper Title

Research and application of element logging intelligent identification model based on data mining

Our Title

IEEE Project Abstract

Underground strata are reflected in various information sources in petroleum exploration including well logging and drilling data. Real-time measurement parameters obtained from mud logging can provide data support for the early discovery of oil and gas resources and the prevention of safety accidents. It plays a forward-looking role in the drilling process. In this paper, we aim at the defection of fuzzy and random characteristics of the big data of drilling element parameters in the current drilling process. A new method named GWO-SVM (Grey Wolf Optimization-Support Vector Machine) is proposed by analyzing the relationship between logging data and formation to solve the serious problem of formation misjudgment. Using element content and Gamma-ray value, data mining is performed by a large number of real-time data obtained from the drilling site. The obtained information is used for comprehensive estimation and prediction of strata. Firstly, the data is normalized, and then the best 𝜁 and σ value are found through the optimization of gray wolf algorithm, next the SVM training is carried out, finally, the formation prediction model is established, and the error analysis of the results was conducted. In the paper, the algorithm model is subsequently applied to three actual wells. The GWO-SVM model based on drilling data is used to predict the formation, and the error analysis showed that the error range of the GWO-SVM algorithm is within 10%. Compared with GWO-SVM, the model accuracy of SVM, PSO-SVM (Particle Swarm Optimization-Support Vector Machine) algorithm is lower 53% and 23%, respectively. GWO-SVM has higher robustness, reliability, and achieves faster convergence speed, stronger generalization effect, and improves the identification accuracy of elements for the formation. The average accuracy of GWO-SVM in stratum dynamic identification is 93.5%. This model is implemented to support the logging system to improve application strength.

Existing System

Drawback of Existing System

Proposed System

Advantage of Proposed System

Enhancement from Base Paper

Architecture

Technology Used : Hardware & Software

Existing Algorithm

Proposed Algorithm

Advantages of Proposed Algorithm

Project Modules

Literature Survey

Conclusion

Future Work

To View the Base Paper Abstract Contents

Exclusive
Offer
Refer Your Friend
10%
CASHBACK
Refer Another Friend
Thanks for Referring Your Friend / Relation

Now it is Your Time to Shine.

Great careers Start Here.

We Guide you to Every Step

Success! You're Awesome

Thank you for filling out your information!

We’ve sent you an email with your Final Year Project PPT file download link at the email address you provided. Please enjoy, and let us know if there’s anything else we can help you with.

To know more details Call 900 31 31 555

The WISEN Team