Reservoir Geostatistical Estimation of Imprecise Information Using Fuzzy Kriging Approach
- X. Zhao (University of Southern California) | A. S. Popa (Chevron Corporation) | I. Ershaghi (University of Southern California) | F. Aminzadeh (University of Southern California) | Y. Li (University of Southern California) | S. D. Cassidy (Chevron Corporation)
- Document ID
- Society of Petroleum Engineers
- SPE Western Regional Meeting, 22-26 April, Garden Grove, California, USA
- Publication Date
- Document Type
- Conference Paper
- 2018. Society of Petroleum Engineers
- 1.6 Drilling Operations, 7 Management and Information, 5.1.5 Geologic Modeling, 2 Well completion, 2.7 Completion Fluids, 7.6 Information Management and Systems, 7.6.6 Artificial Intelligence, 2.7.1 Completion Fluids
- Fuzzy Logic, Kriging, Uncertinty, Fuzzy Sets, Fuzzy Kriging
- 2 in the last 30 days
- 167 since 2007
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This paper presents a methodology for geostatistical estimation of reservoir properties to handle uncertainties in observation and modeling. Given certain known data in a geological region, the Kriging methodology is used to estimate or predict spatial phenomenon at non-sampled locations from the estimated random function. The approach assumes that the data is accurate and precise and the random function is generated from a thorough descriptive analysis of the known dataset. Regarding the assumptions considered in classic Kriging, it is realistic to assume that spatial data contains a certain amount of imprecision mostly due to measurement errors, and the information is lacking to properly assess a unique random function model. This paper presents a methodology for geostatistical estimation of reservoir properties to handle uncertainties in observation and modeling. Combination of regular or classic Kriging and Fuzzy Logic method is proposed. As such, imprecise variogram parameters are modeled based on Fuzzy Logic theory, while the predictions and variances are computed from Kriging analysis characterized by membership functions. Lastly, an optimization method is included to solve the constrained fuzzy non-linear equation system. The proposed methodology was implemented into a friendly integrated tool, which enables the user to create a preferred grid, conduct statistical analysis and run fuzzy kriging for various problems. The tool was validated using the SPE-10 porosity data. Additionally, a case study has been conducted using measured core permeability data in a heterogeneous reservoir to demonstrate the viability of the technology.
|File Size||1 MB||Number of Pages||15|
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