SONIC WAVE VELOCITY ESTIMATION USING INTELLIGENT SYSTEM AND MULTI RESOLUTION GRAPH BASE CLUSTERING: A CASE STUDY FROM ONE OF IRANIAN SOUTH FIELD

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NOORI TALEGHANI M.; KARIMI KHALEDI M.; MEHRGINI B.

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Journal: IRANIAN JOURNAL OF PETROLEUM GEOLOGY Year:2013 | Volume:3 | Issue:6 Page(s): 33-44

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Title

SONIC WAVE VELOCITY ESTIMATION USING INTELLIGENT SYSTEM AND MULTI RESOLUTION GRAPH BASE CLUSTERING: A CASE STUDY FROM ONE OF IRANIAN SOUTH FIELD

Author(s)

NOORI TALEGHANI M. | KARIMI KHALEDI M. | MEHRGINI B. | Issue Writer Certificate

Keywords

COMPRESSIONAL VELOCITYQ2

SHEAR VELOCITYQ2

DIPOLE SONIC IMAGINGQ2

NEURAL NETWORKQ2

FUZZY LOGICQ2

MULTI RESOLUTION GRAPH BASE CLUSTERINGQ2

Abstract

Compressional and SHEAR VELOCITY are two fundamental parameters, which have many applications in petrophysical, geophysical, and geomechanical operations. These two parameters can be obtained using DIPOLE SONIC IMAGING tool (DSI), but unfortunately this tool is run just in few wells of a field. Therefore it is important to predict compressional and SHEAR VELOCITY indirectly from the other conventional well logs that have good correlation with these parameters in wells without these logs. Classical methods to predict the mentioned parameters are utilizing correlations and regression analysis. However, the best tool is intelligent systems including Artificial NEURAL NETWORK, FUZZY LOGIC, Adaptive Neuro Fuzzy Inference System, and MULTI RESOLUTION GRAPH BASE CLUSTERING for performing such tasks. In this paper 1321 data points from Kangan and Dalan formations which have compressional and SHEAR VELOCITY are used. These data are divided into two groups: 995 and 326 data points were used for construction of intelligent systems and model testing, respectively. The results showed that despite differences in concept, all of the intelligent techniques were successful for estimation of compressional and shear velocities. The MULTI RESOLUTION GRAPH BASE CLUSTERING. The method had the best performance among the others due to precise clustering the data points. Using this method, the compressional and SHEAR VELOCITY were correlated with correlation factor of 0.9505 and 0.9407, respectively. The developed model does not incorporate depth or lithological data as a part of the inputs to the network. This means that utilized methodology is applicable to any field.

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APA: Copy

NOORI TALEGHANI, M., KARIMI KHALEDI, M., & MEHRGINI, B.. (2013). SONIC WAVE VELOCITY ESTIMATION USING INTELLIGENT SYSTEM AND MULTI RESOLUTION GRAPH BASE CLUSTERING: A CASE STUDY FROM ONE OF IRANIAN SOUTH FIELD. IRANIAN JOURNAL OF PETROLEUM GEOLOGY, 3(6), 33-44. SID. https://sid.ir/paper/253084/en

Vancouver: Copy

NOORI TALEGHANI M., KARIMI KHALEDI M., MEHRGINI B.. SONIC WAVE VELOCITY ESTIMATION USING INTELLIGENT SYSTEM AND MULTI RESOLUTION GRAPH BASE CLUSTERING: A CASE STUDY FROM ONE OF IRANIAN SOUTH FIELD. IRANIAN JOURNAL OF PETROLEUM GEOLOGY[Internet]. 2013;3(6):33-44. Available from: https://sid.ir/paper/253084/en

IEEE: Copy

M. NOORI TALEGHANI, M. KARIMI KHALEDI, and B. MEHRGINI, “SONIC WAVE VELOCITY ESTIMATION USING INTELLIGENT SYSTEM AND MULTI RESOLUTION GRAPH BASE CLUSTERING: A CASE STUDY FROM ONE OF IRANIAN SOUTH FIELD,” IRANIAN JOURNAL OF PETROLEUM GEOLOGY, vol. 3, no. 6, pp. 33–44, 2013, [Online]. Available: https://sid.ir/paper/253084/en

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