The Use of Association-Rule Mining and High-Dimensional Visualization To Explore the Impact of Geological Features on Dynamic-Flow Behavior
- Satomi Suzuki (ExxonMobil Upstream Research Company) | Dave Stern (ExxonMobil Upstream Research Company) | Tom Manzocchi (University College Dublin)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- December 2016
- Document Type
- Journal Paper
- 1,996 - 2,009
- 2016.Society of Petroleum Engineers
- high-dimensional visualization, association rule mining, reservoir simulation, reservoir modeling, geological uncertainty
- 0 in the last 30 days
- 178 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 12.00|
|SPE Non-Member Price:||USD 35.00|
Because of computational advances in reservoir simulation with high-performance computing, it is now possible to simulate more than thousands of reservoir-simulation cases in a practical time frame. This opens a new avenue to reservoir-simulation studies, enabling exhaustive exploration of subsurface uncertainty and development/depletion options. However, analyzing the results of a large number of simulation cases still remains a challenging and overwhelming task. We propose a new method that enables the efficient analysis of massive reservoir-simulation results, often consisting of thousands of cases, by discovering interesting patterns of relationships among variables in large data sets. The method uses a well-known data-mining method, called association-rule mining, together with a high-dimensional visualization technique. We demonstrate the capability of the proposed method by using it to analyze the reservoir-simulation results from the Sensitivity Analysis of the Impact of Geological Uncertainty on Production (SAIGUP) project, which is an interdisciplinary reservoir-modeling project carried out earlier by Manzocchi et al. (2008a). To investigate the influence of geological features on oil recovery in shallow marine reservoirs, numerous reservoir models were built and flow-simulated in the SAIGUP project. In this paper, we analyze the simulation results from an ensemble of 9,072 models, which cover all possible combinations of several structural and sedimentological parameters individually varied to describe geological uncertainty. To be able to analyze the simulation results from such exhaustive sampling from high-dimensional model parameter space, it is crucial to efficiently decompose complex interactions between model parameters and to discover hidden impacts on flow response. By coupling the association-rule mining algorithm and high-dimensional visualization, such interactions and impacts are rapidly extracted and visualized in such a way that engineers and geoscientists can interpret meaningful sensitivities “at a glance.” This methodology provides a novel way to rapidly interpret flow response from a large ensemble of reservoir models without being overwhelmed by massive data.
|File Size||1 MB||Number of Pages||14|
Aanonsen, S. I., Nævdal, G., Oliver, D. S. et al. 2009. The Ensemble Kalman Filter in Reservoir Engineering—A Review. SPE J. 14 (3): 393–412. SPE-117274-PA. http://dx.doi.org/10.2118/117274-PA.
Abdollahzadeh, A., Reynolds, A., Christie, M. et al. 2012. Bayesian Optimization Algorithm Applied to Uncertainty Quantification. SPE J. 17 (3): 865–873. SPE-143290-PA. http://dx.doi.org/10.2118/143290-PA.
Agbalaka, C. C., Stern, D., and Oliver, D. S. 2013. Two-stage Ensemble-based History Matching With Multiple Modes in the Objective Function. Computers and Geoscience 55: 28–43. http://dx.doi.org/10.1016/j.cageo.2012.05.030.
Agrawal, R., Imielin´ski, T., and Swami, A. 1993. Mining Association Rules Between Sets of Items in Large Databases. Presented at the 1993 ACM SIGMOD International Conference on Management of Data, Washington, DC, 26–28 May.
Agrawal, R. and Srikant, R. 1994. Fast Algorithms for Mining Association Rules in Large Databases. Proc., the 20th International Conference on Very Large Data Bases (VLDB), Santiago, Chile, September.
Borgelt, C. and Kruse, R. 2002. Induction of Association Rules: Apriori Implementation. Presented at the 15th Conference on Computational Statistics (COMPSTAT 2002, Berlin, Germany) Physica Verlag, Heidelberg, Germany.
Carter, J. N. and Matthews, J. D. 2008. Optimization of a Reservoir Development Plan Using a Parallel Genetic Algorithm. Petroleum Geoscience 14 (1): 85–90. http://dx.doi.org/10.1144/1354-079307-788.
Hahsler, M., Buchta, C., Gruen, B. et al. 2005. Introduction to Arules— A Computational Environment for Mining Association Rules and Frequent Item Sets. Journal of Statistical Software 14 (15): 1–25. URI: http://epub.wu.ac.at/id/eprint/132.
Hahsler, M., Buchta, C., Gruen, B. et al. 2013. Arules: Mining Association Rules and Frequent Itemsets. http://CRAN.R-project.org/.
Hajizadeh, Y., Amorim, E., and Costa Sousa, M. 2012. Building Trust in History Matching: The Role of Multidimensional Projection. Presented at the EAGE Annual Conference and Exhibition, Copenhagen, Denmark, 4–7 June. SPE-152754-MS. http://dx.doi.org/10.2118/152754-MS.
Han, J., Pei, J., and Yin, Y. 2000. Mining Frequent Patterns Without Candidate Generation. Presented at the 2000 ACM SIGMOD International Conference on Management of Data and Symposium on Principles of Database Systems, Dallas, 15–18 May.
Howell, J. A., Skorstad, A., MacDonald, A. et al. 2008. Sedimentological Parameterization of Shallow-Marine Reservoirs. Petroleum Geoscience 14 (1): 17–34. http://dx.doi.org/10.1144/1354-079307-787.
Inselberg, A. 1985. The Plane With Parallel Coordinates. Special Issue on Computational Geometry. The Visual Computer 1: 69–91. http://dx.doi.org/10.1007/BF01898350.
Langton, J. T., Prinz, A. A., and Hickey, T. J. 2006. Combining Pixelization and Dimensional Stacking. Lecture Notes in Computer Science 4292: 617–626. http://dx.doi.org/10.1007/11919629_62.
LeBlanc, J., Ward, M. O., and Wittels, N. 1990. Exploring N-dimensional Databases. Presented at the 1st IEEE Conference on Visualization ’90, San Francisco, 23–26 October. http://dx.doi.org/10.1109/VISUAL.1990.146386.
Manzocchi, T., Carter, J. N., Skorstad, A. et al. 2008a. Sensitivity of the Impact of Geological Uncertainty on Production From Faulted and Unfaulted Shallow-Marine Oil Reservoirs: Objectives and Methods. Petroleum Geoscience 14 (1): 3–15. http://dx.doi.org/10.1144/1354-079307-790.
Manzocchi, T., Matthews, J. D., Strand, J. A. et al. 2008b. A Study of the Structural Controls on Oil Recovery From Shallow-Marine Reservoirs. Petroleum Geoscience 14 (1): 55–70. http://dx.doi.org/10.1144/1354-079307-786.
Manzocchi, T., Heath, A. E., Palananthakumar, B. et al. 2008c. Faults in Conventional Flow Simulation Models: A Consideration of Representational Assumptions and Geological Uncertainties. Petroleum Geoscience 14 (1): 91–110. http://dx.doi.org/10.1144/1354-079306-775.
Matthews, J. D., Carter, J. N., Stephen, K. D. et al. 2008. Assessing the Effect of Geological Uncertainty on Recovery Estimates in Shallow-Marine Reservoirs: The Application of Reservoir Engineering to the SAIGUP Project. Petroleum Geoscience 14 (1): 35–44. http://dx.doi.org/10.1144/1354-079307-791.
Michalski, R. S. 1978. A Planar Geometric Model for Representing Multidimensional Discrete Spaces and Multiple-valued Logic Functions. In Technical Report UIUCDCS-R-78-897. University of Illinois at Urbana-Champaign.
Mohamed, L., Christie, M. A., and Demyanov, V. 2010. Comparison of Stochastic Sampling Algorithms for Uncertainty Quantification. SPE J. 15 (1): 31–38. SPE-119139-PA. http://dx.doi.org/10.2118/119139-PA.
Oliver, D. S. and Chen, Y. 2011. Recent Progress on Reservoir History Matching: A Review. Computational Geosciences 15 (1): 185–221. http://dx.doi.org/10.1007/s10596-010-9194-2.
Peng, W., Ward, M. O., and Rundensteiner, E. A. 2004. Clutter Reduction in Multi-Dimensional Data Visualization Using Dimension Reordering. Presented at the IEEE Symposium on Information Visualization InfoVis 2004, Austin, Texas, October.
R Core Team. 2014. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. http://www.R-project.org.
Sarkar, D. 2008. Lattice, Multivariate Data Visualization With R. New York: Springer
Scheidt, C. and Caers, J. 2009a. Uncertainty Quantification in Reservoir Performance Using Distances and Kernel Methods–Application to a West Africa Deepwater Turbidite Reservoir. SPE J. 14 (4): 680–692. SPE-118740-PA. http://dx.doi.org/10.2118/118740-PA.
Scheidt, C. and Caers, J. 2009b. Representing Spatial Uncertainty Using Distances and Kernels. Mathematical Geosciences 41 (4): 397–410. http://dx.doi.org/10.1007/s11004-008-9186-0.
Skorstad, A., Kolbjørnsen, O., Manzocchi, T. et al. 2008. Combined Effects of Structural, Stratigraphic and Well Controls on Production Variability in Faulted Shallow-Marine Reservoirs. Petroleum Geoscience 14 (1): 45–54. http://dx.doi.org/10.1144/1354-079307-789.
Stephen, K. D., Yang, C., Carter, J. N. et al. 2008. Upscaling Uncertainty Analysis in a Shallow-Marine Environment. Petroleum Geoscience 14 (1): 71–84. http://dx.doi.org/10.1144/1354-079307-792.
Sugai, K. and Nishikiori, N. 2006. An Integrated Approach to Reservoir Performance Monitoring and Analysis. Presented at the SPE Asia Pacific Oil & Gas Conference and Exhibition, Adelaide, Australia, 11–13 September. SPE-100995-MS. http://dx.doi.org/10.2118/100995-MS.
Tavakoli, R., Srinivasan, S., and Wheeler, M. F. 2014. Rapid Updating of Stochastic Models by Use of an Ensemble-Filter Approach. SPE J. 19 (3): 500–513. SPE-163673-PA. http://dx.doi.org/10.2118/163673-PA.
Taylor, A. L., Hickey, T. J., Prinz, A. A. et al. 2006. Structure and Visualization of High-Dimensional Conductance Spaces. Journal of Neurophysiology 96 (2): 891–905. http://dx.doi.org/10.1152/jn.00367.2006.
Wang, B. 2012. Reservoir Characterization and Horizontal Well Placement Guidance Acquisition by Using GIS and Data Mining Methods. MS thesis, University of Calgary, Alberta (June 2012).
Yeh, T., Jimenez, E., Van Essen, G. et al. 2014. Reservoir Uncertainty Quantification Using Probabilistic History Matching Workflow. Presented at the SPE Annual Technical Conference and Exhibition, Amsterdam, 27–29 October. SPE-170893-MS. http://dx.doi.org/10.2118/170893-MS.
Zaki, M. J. 2000. Scaleable Algorithms for Association Mining. IEEE Trans. on Knowledge and Data Engineering 12 (3): 372–390. http://dx.doi.org/10.1109/69.846291.
Zhao, Y., Zhang, C., and Cao, L. 2009. Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction, first edition. Hershey, New York: Information Science Reference.
Zhao, Y. 2012. R and Data Mining–Examples and Case Studies. Academic Press, Amsterdam: Elsevier.