Application of Conventional Well Logs To Characterize Spatial Heterogeneity in Carbonate Formations Required for Prediction of Acid-Fracture Conductivity
- Mehrnoosh Saneifar (Texas A&M University) | Zoya Heidari (Texas A&M University) | Alfred D. Hill (Texas A&M University)
- Document ID
- Society of Petroleum Engineers
- SPE Production & Operations
- Publication Date
- August 2015
- Document Type
- Journal Paper
- 243 - 256
- 2015.Society of Petroleum Engineers
- Heterogeneity Characterization, Rock Classification, Carbonate Formations, Permeability, Fracture Conductivity Performance
- 2 in the last 30 days
- 661 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 10.00|
|SPE Non-Member Price:||USD 30.00|
Acid etching, as the consequence of heterogeneous distribution of petrophysical and compositional properties, results in the conductivity of acid fractures in carbonate reservoirs. Reliable characterization of small-scale formation spatial heterogeneity by use of geostatistical analysis (i.e., variogram analysis) can improve prediction of acid-fracture conductivity significantly. Previous publications suggest that permeability correlation length can be used to assimilate spatial heterogeneity in prediction of acid-fracture conductivity. Well logs are good candidates to provide information about petrophysical and compositional properties of the formation with the required resolution for prediction of acid-fracture conductivity. However, the assessment of permeability and mineralogy from conventional well logs is challenging because of high spatial heterogeneity and complex pore structure. Rock typing has been suggested in the literature to improve permeability assessment in carbonates. Most of the previously introduced rock-typing methods are dependent on core measurements. However, core data are generally sparse and not available with the sampling rate required for prediction of acid-fracture conductivity.
The main objective of this paper is to quantify formation spatial heterogeneity with variogram analysis of well logs and well-log-based estimates of petrophysical and compositional properties in carbonate reservoirs. We introduce an iterative permeability-assessment technique that is based on well logs, which takes into account characteristics of different rock classes in the reservoir. Furthermore, we propose three rock-classification techniques that are based on conventional well logs and that take into account static and dynamic petrophysical properties of the formation as well as mineral composition.
We applied the proposed techniques successfully in two carbonate formations--Happy Spraberry oil field and Hugoton gas field. The petrophysical rock classification is in good agreement with identified core-derived rock classes. The results show approximately 54% improvement in permeability assessment compared with conventional permeability-assessment techniques, which can improve prediction of acid-stimulation jobs significantly. Finally, we investigated the direct application of well logs and well-log-based estimates of petrophysical and compositional properties for variogram analysis required to characterize formation spatial heterogeneity. We conducted variogram analysis in both field examples. The results show that the direct application of well logs and well-log-based estimates of petrophysical/compositional properties is reliable to characterize formation spatial heterogeneity. We also showed that application of well logs can enhance assessment of spatial heterogeneity compared with core measurements.
|File Size||2 MB||Number of Pages||14|
Al-Farisi, O., Elhami, M., Al-Felasi, A. et al. 2009. Revelation of Carbonate Rock Typing—the Resolved Gap. Presented at the SPE/EAGE Reservoir Characterization and Simulation Conference, Abu Dhabi, 19–21 October. SPE-125576-MS. http://dx.doi.org/10.2118/125576-MS.
Babadagli, T. and Al-Salmi, S. 2002. Improvement of Permeability Prediction for Carbonate Reservoirs Using Well Log Data. Presented at the SPE Asia Pacific Oil and Gas Conference and Exhibition, Melbourne, Australia, 8–10 October. SPE-77889-MS. http://dx.doi.org/10.2118/77889-MS.
Backus, G.E. 1962. Long-Wave Elastic Anisotropy Produced by Horizontal Layering. Journal of Geophysical Research 67 (11): 4427–4440. http://dx.doi.org/10.1029/JZ067i011p04427.
Beatty, C.V. 2010. Characterization of Small Scale Heterogeneity for Prediction of Acid Fracture Performance. MS thesis, Texas A&M University, College Station, Texas (August 2010).
Buiting, J.J.M. and Clerke, E.A. 2013. Permeability from porosimetry measurements: derivation for a tortuous and fractal tubular bundle. Journal of Petroleum Science and Engineering 108: 267–278. http://dx.doi.org/10.1016/j.petrol.2013.04.016.
Clerke, E.A. 2009. Permeability, Relative Permeability, Microscopic Displacement Efficiency, and Pore Geometry of M_1 Bimodal Pore Systems in Arab-D Lime-Stone. SPE J. 14 (3): 524–531. SPE-105259-PA. http://dx.doi.org/10.2118/105259-PA.
Clerke, E.A., Allen, D.F., Crary, S.C. et al. 2014. Wireline Spectral Porosity Analysis of the Arab Limestone—From Rosetta Stone to Cipher. Presented at the SPWLA 55th Annual Logging Symposium, Abu Dhabi, 18–22 May. SPWLA-2014-D.
Clerke, E.A., Mueller III, H.W., Phillips, E.C. et al. 2008. Application of Thomeer Hyperbolas to decode the pore systems, facies and reservoir properties of the Upper Jurassic Arab D Limestone, Ghawar field, Saudi Arabia: A “Rosetta Stone” Approach. GeoArabia 13 (4): 113–160.
Coates, G. and Denoo, S. 1981. The Producibility Answer Product. The Technical Review 29 (2): 55–63. Schlumberger.
Deng, J., Hill, A.D., and Zhu, D. 2011. A Theoretical Study of Acid-Fracture Conductivity Under Closure Stress. SPE Prod & Oper 26 (1): 9–17. SPE-124755-PA. http://dx.doi.org/10.2118/124755-PA.
Deng, J., Mou, J., Hill, A.D. et al. 2012. A New Correlation of Acid-Fracture Conductivity Subject to Closure Stress. SPE Prod & Oper 27 (2): 158–169. SPE-140402-PA. http://dx.doi.org/10.2118/140402-PA.
Dubois, M.K., Byrnes, A.P., Bhattacharya, S. et al. 2006. Hugoton Asset Management Project (HAMP): Hugoton Geomodel Final Report. Open File Report, Kansas Geological Survey, Lawrence, Kansas. http://www.kgs.ku.edu/PRS/publication/2007/OFR07_06/KGS_2007-06-1_Chapter01_Introduction.pdf.
Gandhi, A., Torres-Verdín, C., Voss, B. et al. 2010. Construction of Reliable Static and Dynamic Multi-Layer Petrophysical Models in Camisea Gas Reservoirs, Peru. Presented at the SPWLA 51st Annual Logging Symposium, Perth, Australia, 19–23 June. SPWLA-2010-40710.
Gentry, M.D. 2003. Applications of Artificial Neural Networks in the Identification of Flow Units, Happy Spraberry field, Garza County, Texas. MS thesis, Texas A&M University, College Station, Texas (December 2003).
George, B.K., Torres-Verdín, C., Delshad, M. et al. 2003. A Case Study Integrating the Physics of Mud-filtrate Invasion with the Physics of Induction Logging: Assessment of In-situ Hydrocarbon Saturation in the Presence of Deep Invasion and Highly Saline Connate Water. Presented at the SPWLA 44th Annual Logging Symposium, Galveston, Texas, 22–25 June. SPWLA-2003-K.
Goggin, D.J., Chandler, M.A., Kocurek, G. et al. 1992. Permeability Transects of Eolian Sands and Their Use in Generating Random Permeability Fields. SPE Form Eval 7 (1): 7–16. SPE-19586-PA. http://dx.doi.org/10.2118/19586-PA.
Gottlib-Zeh, S. 2000. Synthèse Des Données Géologiques et Pétrophysiques Acquises en Forages Pétroliers à L’Aide de Traitements Statistiques et Neuronaux. Applications aux dépôts turbiditiques et aux plates-formes mixtes. PhD dissertation, Montpellier 2 University, Carcassonne, France, 262.
Gringarten, E. and Deutsch, C.V. 1999. Methodology for Variogram Interpretation and Modeling for Improved Reservoir Characterization. Presented at the SPE Annual technical Conference and Exhibition, Houston, 3–6 October. SPE-56654-MS. http://dx.doi.org/10.2118/56654-MS.
Guha, S., Rastogi, R., and Shim, K. 1998. Cure: An Efficient Clustering Algorithm for Large Databases. In SIGMOD ’98: Proceedings of the 1998 ACM SIGMOD international conference on Management of data, ed. A. Tiwary and M. Franklin, Vol. 27, 73–84. New York, New York: ACM. http://dx.doi.org/10.1145/276304.2763132.
Hammel, B.S. 1996. High Resolution Reservoir Characterization of the Permian (upper Leonardian) Spraberry Formation, Happy Spraberry Field, Garza County, Texas. MS thesis, Texas A&M University, College Station, Texas.
Heidari, Z. and Torres-Verdín, C. 2012. Estimation of dynamic petrophysical properties of water-bearing sands invaded with oil-base mud from the interpretation of multiple borehole geophysical measurements. Geophysics 77 (6): D209–D227. http://dx.doi.org/10.1190/GEO2012-0006.1.
Heidari, Z., Hamman, J.G., Day, P.I. et al. 2011. Assessment of Movable Gas Saturation and Rock Typing Based on the Combined Simulation of Petrophysical Borehole Measurements. Presented at the SPWLA 52nd Annual Logging Symposium, Colorado Springs, Colorado, 14–18 May. SPWLA-2011-NN.
Isaaks, E.H. and Srivastava, R.M. 1989. An Introduction to Applied Geostatistics. New York: Oxford University Press, Inc.
Jennings, J.W. and Lucia, F.J. 2003. Predicting Permeability From Well Logs in Carbonates With a Link to Geology for Interwell Permeability Mapping. SPE Res Eval & Eng 6 (4): 215–225. SPE-84942-PA. http://dx.doi.org/10.2118/84942-PA.
Katz, A.J. and Thompson, A.H. 1986. Quantitative Prediction of Permeability in Porous Rock. Phys. Rev. B 34: 8179(R). http://dx.doi.org/10.1103/PhysRevB.34.8179.
Kittridge, M.G., Lake, L.W., Lucia, J.F. et al. 1990. Outcrop/Subsurface Comparisons of Heterogeneity in the San Andres Formation. SPE Form Eval 5 (3): 233–240. SPE-19596-PA. http://dx.doi.org/10.2118/19596-PA.
Kohonen, T. 2001. Self-Organizing Maps, third edition, No. 30. Berlin: Springer Series in Information Sciences, Springer-Verlag.
Layman, J.M. 2002. Porosity Characterization Utilizing Petrographic Image Analysis: Implications for Identifying and Ranking Reservoir Flow Units, Happy Spraberry Field, Garza County, Texas. MS thesis, Texas A&M University, College Station, Texas.
Lee, S.H., Kharghoria, A., and Datta-Gutpa, A. 2002. Electrofacies Characterization and Permeability Predictions in Complex Reservoirs. SPE Res Eval & Eng 5 (3): 237–248. SPE-78662-PA. http://dx.doi.org/10.2118/78662-PA.
Levenberg, K. 1944. A method for the solution of certain non-linear problems in least squares. Quarterly Journal of Applied Mathematics 2 (2): 164–168.
Leverett, M.C. 1941. Capillary Behavior in Porous Solids. Transactions of the AIME 142 (1): 159–172. http://dx.doi.org/10.2118/941152-G.
Lucia, F.J. 1999. Carbonate Reservoir Characterization. Berlin: Springer-Verlag.
Mathisen, T., Lee, S.H., and Datta-Gutpa, A. 2003. Improved Permeability Estimates in Carbonate Reservoirs Using Electrofacies Characterization: A Case Study of the North Robertson Unit, West Texas. SPE Res Eval & Eng 6 (3): 176–184. SPE-84920-PA. http://dx.doi.org/10.2118/84920-PA.
MathSoft. 2000. S-Plus 2000 Guide to Statistics. Vol. 2, Data Analysis Products Division. Seattle, Washington: MathSoft, Inc.
Mazingue-Desailly, V.P. 2004. Assessing the Influence of Diagenesis on Reservoir Quality: Happy Spraberry Field, Garza County, Texas. MS thesis, Texas A&M University, College Station, Texas (May 2004).
Miranda, L.J., Torres-Verdín, C., and Lucia, F.J. 2009. Modeling Mud-Filtrate Invasion Effects on Resistivity Logs to Estimate Permeability of Vuggy and Fractured Carbonate Formations. Presented at the EUROPEC/EAGE Conference and Exhibition, Amsterdam, The Netherlands, 8–11 June. SPE-121136-MS. http://dx.doi.org/10.2118/121136-MS.
Mohaghegh, S., Balan, B., and Ameri, S. 1997. Permeability Determination From Well Log Data. SPE Form Eval 12 (3): 170–174. SPE-30978-PA. http://dx.doi.org/10.2118/30978-PA.
Mou, J., Zhu, D., and Hill, A.D. 2011. New Correlations of Acid-Fracture Conductivity at Low Closure Stress Based on the Spatial Distributions of Formation Properties. SPE Prod & Oper 26 (2): 195–202. SPE-131591-PA. http://dx.doi.org/10.2118/131591-PA.
Oeth, C., Hill, A.D., Zhu, D. et al. 2011. Characterization of Small Scale Heterogeneity to Predict Acid Fracture Performance. Presented at the SPE Hydraulic Fracturing Technology Conference, The Woodlands, Texas, 24–26 January. SPE-140336-MS. http://dx.doi.org/10.2118/140336-MS.
Oeth, C.V., Hill, A.D., and Zhu, D. 2013. Acid Fracturing: Fully 3D Simulation and Performance Prediction. Presented at the SPE Hydraulic Fracturing Technology Conference, The Woodlands, Texas, USA, 4–6 February. SPE-163840-MS. http://dx.doi.org/10.2118/163840-MS.
Olson, T.M. 1998. Porosity and Permeability Prediction in Low-Permeability Gas Reservoirs From Well Logs Using Neural Networks. Presented at the SPE Rocky Mountain Regional/Low-Permeability Reservoirs Symposium, Denver, 5–8 April. SPE-39964-MS. http://dx.doi.org/10.2118/39964-MS.
Olson, T.M., Babcock, J.A., Prasad, K.V.K. et al. 1997. Reservoir Characterization of the Giant Hugoton Gas Field, Kansas. AAPG Bulletin 81 (11): 1785–1803.
Pittman, E.D. 1992. Relationship of Porosity and Permeability to Various Parameters Derived from Mercury Injection-Capillary Pressure Curves for Sandstone. AAPG Bulletin 76 (2): 191–198.
Remy, N., Boucher, A., Wu, J. et al. 2008. SGeMS: Stanford Geostatistical Modeling Software Version 2.1. Palo Alto, California: Board of Trustees of Stanford University.
Roy, E. 1998. High Resolution Mapping of Flow Units for Enhanced Recovery Program Planning, Happy Spraberry Lime Field, Garza County, Texas. MS thesis, Texas A&M University, College Station, Texas.
Salazar, J.M., Torres-Verdín, C., Alpak, F.O. et al. 2006. Estimation of permeability from array induction measurements: Applications to the petrophysical assessment of tight-gas sands. Petrophysics 47 (6): 527–544.
Saner, S., Kissami, M., and Al Nufaili, S. 1997. Estimation of Permeability From Well Logs Using Resistivity and Saturation Data. SPE Form Eval 12 (1): 27–31. SPE-26277-PA. http://dx.doi.org/10.2118/26277-PA.
Schlumberger. 1991. Log Interpretation Principles/Applications. Schlumberger Educational Services.
Silva, F.P.T, Ghani, A.A., Al Mansoori, A. et al. 2002. Rock Type Constrained 3D Reservoir Characterization and Modeling. Presented at the Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, 13–16 October. SPE-78504-MS. http://dx.doi.org/10.2118/78504-MS.
Skalinski, M., Gottlib-Zeh, S., and Moss, B. 2005. Defining and Predicting Rock Types in Carbonates—An Integrated Approach using Core and Log Data in Tengiz Field. Presented at the SPWLA 46th Annual Logging Symposium, New Orleans, 26–29 June. SPWLA-2005-Z.
Timur, A. 1968. An Investigation of Permeability, Porosity, and Residual Water Saturation Relationships for Sandstone Reservoirs. The Log Analyst 9 (4). SPWLA-1968-vlXn4a2.
Wyllie, M.R.J. and Rose, W.D. 1950. Some Theoretical Considerations Related to the Quantitative Evaluation of the Physical Characteristics of Reservoir Rock From Electrical Log Data. J Pet Technol 2 (4): 105–118. http://dx.doi.org/10.2118/950105-G.
Xu, C. and Torres-Verdín, C. 2012. Saturation-Height and Invasion Consistent Hydraulic Rock Typing Using Multi-Well Conventional Logs. Presented at the SPWLA 53rd Annual Logging Symposium, Cartagena, Colombia, 16–20 June. SPWLA-2012-071.
Xu, C., Heidari, Z., and Torres-Verdín, C. 2012. Rock Classification in Carbonate Reservoirs based on Static and Dynamic Petrophysical Properties Estimated from Conventional Well Logs. Presented at the SPE Annual technical conference and Exhibition, San Antonio, Texas, 8–10 October. SPE-159991-MS. http://dx.doi.org/10.2118/159991-MS.
Xue, G., Datta-Gupta, A., Valko, P. et al. 1997. Optimal Transformations for Multiple Regression: Application to Permeability Estimation From Well Logs. SPE Form Eval 12 (2): 85–94. SPE-35412-PA. http://dx.doi.org/10.2118/35412-PA.
Yao, C.Y. and Holditch, S.A. 1993. Estimating Permeability Profiles Using Core and Log Data. Presented at the SPE Eastern Regional Meeting, Pittsburgh, Pennsylvania, 2–4 November. SPE-26921-MS. http://dx.doi.org/10.2118/26921-MS.
Ye, S.J., Rabiller, P., and Keskes, N. 1998. Automatic High Resolution texture Analysis on Borehole Imagery. Presented at the SPWLA 39th Annual Logging Symposium, Keystone, Colorado, USA, 26–28 May. SPWLA-1998-M.