Method To Generate Full-Bore Images Using Borehole Images and Multipoint Statistics
- Neil F. Hurley (Schlumberger-Doll Research) | Tuanfeng Zhang (Schlumberger-Doll Research)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- April 2011
- Document Type
- Journal Paper
- 204 - 214
- 2011. Society of Petroleum Engineers
- 3.3.2 Borehole Imaging and Wellbore Seismic, 6.1.5 Human Resources, Competence and Training
- Borehole image logs, Fullbore images
- 0 in the last 30 days
- 1,196 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 12.00|
|SPE Non-Member Price:||USD 35.00|
Borehole-image logs, which are produced by tools being lowered into a well, provide oriented electrical and acoustic maps of the rocks and fluids encountered in the borehole. Electrical borehole images in water-based (conducting) and oil-based (nonconducting) muds are generated from electrodes arranged in fixed patterns on pads that are pressed against the borehole wall. Depending on the borehole diameter, gaps nearly always occur between pads. Because of these gaps, it is common to have nonimaged parts of the borehole wall.
Full-bore images are complete, 360° views of the borehole wall. They are generated by "filling in the gaps" between the pads in borehole-image logs. This method uses the Filtersim algorithm of multipoint statistics (MPS) to generate models, or realizations. Measured (incomplete) borehole images themselves are used as "training images." Recorded data are perfectly honored (i.e., the models are conditioned to the real data). Gaps are filled with patterns similar to those seen elsewhere in the log. Patterns in the gaps match the edges of the pads. The frequency distribution of continuously variable pixel colors in the gaps matches the distribution of pixel colors in the measured images.
Full-bore images facilitate visualization and interpretation of borehole-image logs in any lithology, although case studies shown in this paper are developed in vuggy and fractured rocks. These images can be used to draw closed contours around electrically resistive or nonresistive patches in the borehole wall. Full-bore images can be used to repair logs with bad electrodes, low pad pressure, or poor acoustic reflections. Therefore, they can be used to enhance any commercially available electrical or acoustic borehole images.
|File Size||11 MB||Number of Pages||11|
Adams, J., Bourke, L., and Frisinger, R. 1987. Strategies for dipmeterinterpretations: Part 2. Technical Review 35 (4):20-31.
Caers, J. and Zhang, T. 2002. Multiple-point geostatistics: A quantitativevehicle for integrating geological analogs into multiple reservoir models. InAAPG Memoir 80: Integration of outcrop and modern analog data in reservoirmodels, ed. G.M. Grammer, P.M. Harris, and G.P. Eberli, 383-394.
Delhomme, J.P. 1992. A Quantitative Characterization of FormationHeterogeneities Based on Borehole Image Analysis. Paper T presented at theSPWLA Thirty-Third Annual Logging Symposium, Oklahoma City, Oklahoma, USA,14-17 June, Paper T.
Gilreath, J.A. 1987. Strategies for dipmeter interpretation: part I. TheTechnical Review 35 (3): 28-41.
Guardiano, F. and Srivastava, R.M. 1993. Multivariate geostatistics: Beyondbivariate moments. In Geostatistic Tróia 92, ed. A. Soares, Vol. 1,133-144. Dordrecht, The Netherlands: Kluwer Academic Publishers.
Hassall, J.K., Ferraris, P., Al-Raisi, M., Hurley, N.F., Boyd, A., andAllen, D.F. 2004. Comparison ofPermeability Predictors from NMR, Formation Image and Other Logs in a CarbonateReservoir. Paper SPE 88683 presented at the Abu Dhabi InternationalPetroleum Conference and Exhibition, Abu Dhabi, UAE, 10-13 October. doi:10.2118/88683-MS.
Hurley, N.F. 2004. Borehole Images. In Basic Well Log Analysis,second edition, ed. G. Asquith and D. Krygowski, No. 16, 151-164. Tulsa:Methods in Exploration Series, AAPG.
Strebelle, S. 2002. Conditional Simulation ofComplex Geological Structures Using Multiple-Point Statistics.Mathematical Geology 34 (1): 1-22. doi:10.1023/A:1014009426274.
Strebelle, S. and Zhang, T. 2005. Non-Stationary Multiple-pointGeostatistical Models. In Geostatistics Banff 2004, ed. O. Leuangthongand C.V. Deutsch, Vol. 1, 235-244. Dordrecht, The Netherlands: QuantitativeGeology and Geostatistics, Springer.
Tran, T.T. 1994. Improving variogramreproduction on dense simulation grids. Computers & Geosciences 20 (7-8): 1161-1168. doi:10.1016/0098-3004(94)90069-8.
Wu, J., Boucher, A., and Zhang, T. 2008. A SGeMS code for patternsimulation of continous and categorical variables: FILTERSIM. Computers& Geosciences 34 (12): 1863-1876. doi:10.1016/j.cageo.2007.08.008.
Zemanek, J., Glenn, E.E., Norton, L.J., and Caldwell, R.L. 1970. Formation evaluation by inspectionwith the borehole televiewer. Geophysics 35 (2):254-269. doi:10.1190/1.1440089.
Zhang, T. 2002. Multiple-point simulation of multiple reservoir facies. MSthesis, Stanford University, Stanford, California, USA.
Zhang, T. 2006. Filter-based training image pattern classification forspatial pattern simulation. PhD dissertation, Stanford University, Stanford,California, USA.
Zhang, T., Bombarde, S., Strebelle, S., and Oatney, E. 2006b. 3D Porosity Modeling of a CarbonateReservoir Using Continuous Multiple-Point Statistics Simulation. SPEJ. 11 (3): 375-379. SPE-96308-PA. doi: 10.2118/96308-PA.
Zhang, T., Hurley, N.F., and Zhao, W. 2009. Numerical Modeling ofHeterogeneous Carbonates and Multi-Scale Dynamics. Presented at the SPWLAAnnual Well Logging Symposium, Houston, 21-24 June.
Zhang, T., Switzer P., and Journel A. 2006a. Filter-Based Classificationof Training Image Patterns for Spatial Pattern Simulation. MathematicalGeology 38 (1): 63-80. doi: 10.1007/s11004-005-9004-x.