Application of Tracer-Based Workflow for Calibrating Reservoir Heterogeneity
- Shashvat Doorwar (Chevron Energy Technology Company) | Prakash Purswani (Pennsylvania State University) | Anil Ambastha (Chevron Energy Technology Company) | Lokendra Jain (Chevron Energy Technology Company) | Sophany Thach (Chevron Energy Technology Company)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- October 2020
- Document Type
- Journal Paper
- 2020.Society of Petroleum Engineers
- tracers, static heterogeneity, dynamic heterogeneity
- 22 in the last 30 days
- 29 since 2007
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Capturing the correct reservoir heterogeneity in a geological model is critical for designing and accurately forecasting expected production benefits from improved/enhanced oil recovery processes. In simple terms, reservoir heterogeneity is often considered as a measure of statistical variation of static properties, such as porosity and permeability. The Lorenz coefficient and Dykstra-Parsons coefficient are two such measures of reservoir heterogeneity that account for these static effects. These measures are considered simplistic because the spatial distribution and arrangement of these properties is more critical for reservoir characterization than its statistical variation. The dynamic Lorenz coefficient is one such measure that accounts for the spatial distribution and arrangement of porosity and permeability. The dynamic Lorenz coefficient cannot be directly measured in the field but can be implicitly inferred from tracer data. This inferred heterogeneity will be influenced by the spatial distribution of static properties and can also be significantly influenced by multiphase flow effects, such as viscous fingering and gravity over/underride, which often arise from the differences in the viscosities and densities of the different phases involved. The dynamic Lorenz coefficient interpreted from a tracer test response lumps both static and multiphase (dynamic) effects into one measure of heterogeneity. During geological model construction, the dynamic Lorenz coefficient is also used to rank the model in terms of heterogeneity to select the most representative model. However, the geological models are ranked using fast single-phase/streamline simulations and are devoid of complications caused by adverse mobility or density effects. This creates a disconnect between the heterogeneity measures used to characterize a geological model and those available to characterize a reservoir. Thus, calibrating a geological model to field data is a laborious task.
In this paper, we present a workflow that bridges this gap by decoupling the effect of adverse mobility to obtain an approximate measure of heterogeneity that can be cross-checked against the geological realizations to select the most representative model. The workflow developed in this paper is for inverted, seven-spot patterns and is mainly focused on water-wet reservoirs.
|File Size||13 MB||Number of Pages||16|
Arya, A., Hewett, T. A., Larson, R. G. et al. 1988. Dispersion and Reservoir Heterogeneity. SPE Res Eng 3 (1): 139–148. SPE-14364-PA. https://doi.org/10.2118/14364-PA.
Cheng, H., Shook, G. M., Malik, T. et al. 2012. Interwell Tracer Tests To Optimize Operating Conditions for a Surfactant Field Trial: Design, Evaluation, and Implications. SPE Res Eval & Eng 15 (2): 229–242. SPE-144899-PA. https://doi.org/10.2118/144899-PA.
Clemens, T., Lueftenegger, M., Laoroongroj, A. et al. 2016. The Use of Tracer Data To Determine Polymer-Flooding Effects in a Heterogeneous Reservoir, 8 Torton Horizon Reservoir, Matzen Field, Austria. SPE Res Eval & Eng 19 (4): 655–663. SPE-174349-PA. https://doi.org/10.2118/174349-PA.
Craig, F. F. 1993. The Reservoir Engineering Aspects of Waterflooding. Richardson, Texas, USA: Society of Petroleum Engineers.
Doorwar, S. and Mohanty, K. K. 2015. Fingering Function for Unstable Immiscible Flows. Paper presented at the SPE Reservoir Simulation Symposium, Houston, Texas, USA, 23–25 February. SPE-173290-MS. https://doi.org/10.2118/173290-MS.
Du, Y. and Guan, L. 2005. Interwell Tracer Tests: Lessons Learned from Past Field Studies. Paper presented at the SPE Asia Pacific Oil and Gas Conference and Exhibition, Jakarta, Indonesia, 5–7 April. SPE-93140-MS. https://doi.org/10.2118/93140-MS.
Dykstra, H. and Parsons, R. 1950. The Prediction of Oil Recovery by Water Flood. In Secondary Recovery of Oil in the United States, second edition. Washington, DC, USA: American Petroleum Institute.
Hagoort, J. 1974. Displacement Stability of Water Drives in Water-Wet Connate-Water-Bearing Reservoirs. SPE J. 14 (1): 63–74. SPE-4268-PA. https://doi.org/10.2118/4268-PA.
Iheanacho, P. C., Tiab, D., and Igbokoyi, A. O. 2012. Vertical-Horizontal Permeability Relationships for Sandstone Reservoirs. Paper presented at the Nigerian Annual International Conference and Exhibition, Lagos, Nigeria, 6–8 August. SPE-163011-MS. https://doi.org/10.2118/163011-MS.
Jensen, J. L. and Lake, L. W. 1988. The Influence of Sample Size and Permeability Distribution on Heterogeneity Measures. SPE Res Eng 3 (2): 629–637. SPE-15434-PA. https://doi.org/10.2118/15434-PA.
Koval, E. J. 1963. A Method for Predicting the Performance of Unstable Miscible Displacement in Heterogeneous Media. SPE J. 3 (2): 145–154. SPE-450-PA. https://doi.org/10.2118/450-PA.
Kumar, M., Akshay, S., Alvarez, J. M. et al. 2001. Evaluation of IOR Methods for the Boscán Field. Paper presented at the SPE International Thermal Operations and Heavy Oil Symposium, Porlamar, Margarita Island, Venezuela, 12–14 March. SPE-69723-MS. https://doi.org/10.2118/69723-MS.
Lake, L. W., Johns, R. T., Rossen, W. R. et al. 2014. Fundamentals of Enhanced Oil Recovery. Richardson, Texas, USA: Society of Petroleum Engineers.
Land, C. S. 1968a. The Optimum Gas Saturation for Maximum Oil Recovery from Displacement by Water. Paper presented at the Fall Meeting of the Society of Petroleum Engineers of AIME, Houston, Texas, USA, 29 September–2 October. SPE-2216-MS. https://doi.org/10.2118/2216-MS.
Land, C. S. 1968b. Calculation of Imbibition Relative Permeability for Two- and Three-Phase Flow from Rock Properties. SPE J. 8 (2): 149–156. SPE-1942-PA. https://doi.org/10.2118/1942-PA.
Rashid, B., Muggeridge, A., Bal, A.-L. et al. 2012. Quantifying the Impact of Permeability Heterogeneity on Secondary-Recovery Performance. SPE J. 17 (2): 455–468. SPE-135125-PA. https://doi.org/10.2118/135125-PA.
Ravalec, M. L., Noetinger, B., and Hu, L. Y. 2000. The FFT Moving Average (FFT-MA) Generator: An Efficient Numerical Method for Generating and Conditioning Gaussian Simulations. Math Geol 32 (6): 701–723. https://doi.org/10.1023/A:1007542406333.
Schmalz, J. P. and Rahme, H. D. 1950. The Variation of Waterflood Performance with Variation in Permeability Profile. Prod. Monthly, 9–12.
Shook, G. M. and Mitchell, K. M. 2009. A Robust Measure of Heterogeneity for Ranking Earth Models: The F PHI Curve and Dynamic Lorenz Coefficient. Paper presented at the SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, USA, 4–7 October. SPE-124625-MS. https://doi.org/10.2118/124625-MS.
Shook, G. M., Pope, G. A., and Asakawa, K. 2009. Determining Reservoir Properties and Flood Performance from Tracer Test Analysis. Paper presented at the SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, USA, 4–7 October. SPE-124614-MS. https://doi.org/10.2118/124614-MS.