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Generating Porosity Spectrum of Carbonate Reservoirs using Ultrasonic Imaging Log

100%

Zhang J. , Nie X. , Xiao S. , Zhang C. , Zhang C. , Zhang Z.

Acta Geophysica

2018

tom Vol. 66, no. 2

191--201

Imaging logging tools can provide us the borehole wall image. The micro-resistivity imaging logging has been used to obtain borehole porosity spectrum. However, the resistivity imaging logging cannot cover the whole borehole wall. In this paper, we propose a method to calculate the porosity spectrum using ultrasonic imaging logging data. Based on the amplitude attenuation equation, we analyze the factors affecting the propagation of wave in drilling fluid and formation and based on the bulk-volume rock model, Wyllie equation and Raymer equation, we establish various conversion models between the reflection coefficient β and porosity φ. Then we use the ultrasonic imaging logging and conventional wireline logging data to calculate the near-borehole formation porosity distribution spectrum. The porosity spectrum result obtained from ultrasonic imaging data is compared with the one from the microresistivity imaging data, and they turn out to be similar, but with discrepancy, which is caused by the borehole coverage and data input difference. We separate the porosity types by performing threshold value segmentation and generate porosity-depth distribution curves by counting with equal depth spacing on the porosity image. The practice result is good and reveals the efficiency of our method.

Evaluation of fractured–vuggy reservoir by electrical imaging logging based on a de noising method

84%

Xu F. , Wang Z. , Wang W.

tom Vol. 69, no. 3

761--772

The existence of fractures and vugs in igneous formation is a key factor to determine the productivity of oil and gas reservoirs. Fracture–vug plane porosity and porosity spectrum (fracture–vug parameters) are important parameters to evaluate the development of fractures and vugs. In the process of drilling, the bit forms shallow holes and scratches on the borehole wall which is characterized by pitting, strip and block noise in the electrical imaging logging static image. The background noise afects the identifcation of fractures and vugs and the extraction of parameters. It is found that the background noise mainly exists in the high-frequency conductivity data. In order to suppress the background noise, empirical mode decomposition is applied to conductivity data of electrical imaging logging, and the wavelet hard threshold de-noising is applied to high-frequency intrinsic mode function components. The de-noising fracture-vug parameters have a good correspondence with the electrical imaging logging static image, and have a better linear relationship with the core porosity. These illustrate that the application of the de-noising method in the electrical imaging logging is reasonable and efective. The de-noising porosity spectrum becomes narrower in the reservoir with poor fractures and vugs, which can reveal the development of secondary pores more clearly. In reservoir interpretation, the de-noising fracture-vug plane porosity and porosity spectrum have good consistency with conventional and acoustic logging data, which can efectively evaluate the fractures and vugs in reservoirs.