Pressure Prediction Technology of the Deep Strata Based on BP Neural Network

Wei Li; Tie Yan; Ying Jie Liang

doi:10.4028/www.scientific.net/AMR.143-144.28

Paper Titles

A Scheduling Strategy Suitable for Industrial Ethernet and Mine WSN Scheduling System
p.6

Concurrency Control Protocol for Real-Time Database and the Analysis Base on Petri Net
p.12

The Study on Personalized Guidance in Intelligent Library Based on Multi-Agent
p.18

A Automation Library Management Method Based on Knowledge Management Tools
p.23

Pressure Prediction Technology of the Deep Strata Based on BP Neural Network
p.28

Experimental Study on Moving Train Loads Identification from Bridge Responses
p.32

Measuring User Trajectory Privacy in LBSs with Adversarial Background Knowledge
p.38

The Study on Knowledge Service Models in Digital Library
p.43

The Study on Knowledge Organization for Digital Library
p.48

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 143-144Pressure Prediction Technology of the Deep Strata...

Pressure Prediction Technology of the Deep Strata Based on BP Neural Network

Abstract:

. The accurate prediction of strata pressure is the base for safely, quality and efficiently drilling, decreasing hole problems and reasonable development of the reservoir. Because of the high cost, long cycle of the formation pressure measured method, which may influence the safety of drilling operation, thus a new method for predicting strata pressure, based on the BP neural network, is presented in this paper, and establishing process of the neural network forecast model are discussed in detail. This method takes the acoustic time, natural potential, natural gamma ray log data and pipe pressure test data as study sample, which has a very high accuracy. The paper predicts strata pressure of the Saertu oil field and Xingshugang oil field in Daqing, and the results show that relative error between the predicted data and experimental data is less than ±8.9%.

You might also be interested in these eBooks

Smart Materials and Intelligent Systems, SMIS2010

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 143-144)

Pages:

28-31

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.143-144.28

Citation:

Cite this paper

Online since:

October 2010

Authors:

Wei Li, Tie Yan, Ying Jie Liang

Keywords:

Logging Curve, Neural Network (NN), RFT Stress Test, Strata Pressure

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Xisheng Liu. Drilling Technology(Volume One) [M]. Beijing: Petroleum Industry Press(1998).

Google Scholar

[2] TinggenChen, Zhichuanguan. DrillingEngineeringTheory&Technology[M]. Dongying: University of Petroleum Press(2000).

Google Scholar

[3] Xiangjun Liu, Pingya Luo. Rock Mechanics&Petroleum Engineering [M]. Beijing: Petroleum Industry Press(2004).

Google Scholar

[4] Wenhui Chang, Xuying Qin. Reservoir Pressure Prediction Technology[J]. Progress in Exploration Geophysics, (2005), 28(5): 314-321.

Google Scholar

[5] Xiaoli Zhang, Yuqun Cheng. An Effective Way to Analyze Formation Pressure[J]. Journal of Xi'an Petroleum Institute. (1999), 14(6): 28-30.

Google Scholar

[6] Yinao Su, Yansheng Liu. METHODS TO ESTIMATE FORMATION PRESSURE ON THE SCIENTIFIC EXPLORATORY WELL[J]. ACTA PETROLEI SINICA, (2001), 22(1): 78-82.

Google Scholar

[7] Zhuowu Che. Logging Interpretation Handbook[M]. Beijing: Petroleum Industry Press(1995).

Google Scholar