PREDICTION OF DIFFERENTIAL PIPE STICKING BY USING PROBABILISTIC ARTIFICIAL NEURAL NETWORK IN OFFSHORE PERSIAN GULF OIL FIELDS

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

JAHANBAKHSHI R.; EMAMZADEH S.A.; ALIYARI SHOOREHDELI M.; HASHEMI A.; MIRI R.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: PETROLEUM RESEARCH Year:2011 | Volume:21 | Issue:65 Page(s): 47-57

Download Full-Text

Persian Verion

View:

1,167

Download:

Cites:

Information Journal Paper

Title

PREDICTION OF DIFFERENTIAL PIPE STICKING BY USING PROBABILISTIC ARTIFICIAL NEURAL NETWORK IN OFFSHORE PERSIAN GULF OIL FIELDS

Author(s)

Keywords

DIFFERENTIAL PIPE STICKINGQ3

PROBABILISTIC NEURAL NETWORK (PNN)Q2

MUD PROPERTIESQ3

Abstract

DIFFERENTIAL PIPE STICKING is one of the usual and hazardous problems during drilling operation that leads to increasing the total cost. Nowadays minimizing the risk of stuck occurrence is one of the priorities and main goals in petroleum industry. In the past, statistical methods were applied to investigate DIFFERENTIAL PIPE STICKING, but these methods cannot remarkably predict the non-linear behaviors. Artificial neural network is a novel method for solving engineering problems. This method is capable of considering the effective parameters at the same time and has the ability of direct generalization and learning from the field data (due to the errors and uncertainties). In this paper, the data from 63 wells of the offshore Persian Gulf oil fields were applied and by using a probabilistic neural network, a predictive model has been developed. High accuracy of this model in predicting DIFFERENTIAL PIPE STICKING allows it to be applied in well planning as well as real time drilling operations.Analyzing the result of neural network, associated with engineering viewpoint leads to preventing DIFFERENTIAL PIPE STICKING by optimizing the effective parameters.

Cites

COMBINING THE KNOWLEDGE-BASED AND DATA-BASED METHODS IN DECISION MAKING LEVEL BY USING GIS ( BY EMPHASIS ON ECONOMICAL ZONING OF TEHRAN)

References

INTELLIGENT PREDICTION OF AFFLUX DUE TO BRIDGE PIER USING ARTIFICIAL NEURAL NETWORKS BASED ON RADIAL BASIS FUNCTIONS

Cite

APA: Copy

JAHANBAKHSHI, R., EMAMZADEH, S.A., ALIYARI SHOOREHDELI, M., HASHEMI, A., & MIRI, R.. (2011). PREDICTION OF DIFFERENTIAL PIPE STICKING BY USING PROBABILISTIC ARTIFICIAL NEURAL NETWORK IN OFFSHORE PERSIAN GULF OIL FIELDS. PETROLEUM RESEARCH, 21(65), 47-57. SID. https://sid.ir/paper/115061/en

Vancouver: Copy

JAHANBAKHSHI R., EMAMZADEH S.A., ALIYARI SHOOREHDELI M., HASHEMI A., MIRI R.. PREDICTION OF DIFFERENTIAL PIPE STICKING BY USING PROBABILISTIC ARTIFICIAL NEURAL NETWORK IN OFFSHORE PERSIAN GULF OIL FIELDS. PETROLEUM RESEARCH[Internet]. 2011;21(65):47-57. Available from: https://sid.ir/paper/115061/en

IEEE: Copy

R. JAHANBAKHSHI, S.A. EMAMZADEH, M. ALIYARI SHOOREHDELI, A. HASHEMI, and R. MIRI, “PREDICTION OF DIFFERENTIAL PIPE STICKING BY USING PROBABILISTIC ARTIFICIAL NEURAL NETWORK IN OFFSHORE PERSIAN GULF OIL FIELDS,” PETROLEUM RESEARCH, vol. 21, no. 65, pp. 47–57, 2011, [Online]. Available: https://sid.ir/paper/115061/en

Related Journal Papers