Deriving Permeability and Reservoir Rock Typing Supported with Self-Organized Maps SOM and Artificial Neural Networks ANN - Optimal Workflow for Enabling Core-Log Integration
- Luigi Saputelli (ADNOC) | Rafael Celma (ADNOC) | Douglas Boyd (ADNOC) | Hesham Shebl (ADNOC) | Jorge Gomes (ADNOC) | Fahmi Bahrini (Frontender Corporation) | Alvaro Escorcia (Frontender Corporation) | Yogendra Pandey (Prabuddha)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Characterisation and Simulation Conference and Exhibition, 17-19 September, Abu Dhabi, UAE
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- Permeability and rock typing, Artificial Neural Networks, Petrophysical workflow, Machine Learning, Self-Organized Maps SOM
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- 138 since 2007
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Permeability and rock typing are two of the main outputs generated from the petrophysical domain and are particularly contributors to the highest degree of uncertainty during the history matching process in reservoir modeling, with the subsequent high impact in field development decisions. Detailed core analysis is the preferred main source of information to estimate permeability and to assign rock types; however, since there are generally more un-cored than cored wells, logs are the most frequently applied source of information to predict permeability and rock types in each data point of the reservoir model.
The approach of this investigation is to apply data analytics and machine learning to move from the core domain to the log domain and to determine relationships to then generate properties for the three-dimensional reservoir model with proper simulation for history matching. All wells have a full set of logs (Gamma Ray, Resistivity, Density and Neutron) and few have routine core analysis (Permeability, Porosity and MICP). On a first pass, logs from selected wells are classified into Self Organizing Maps (SOM) without analytical supervision. Then, core data is used to define petrophysical groups (PG), followed by linking the PG's to NMR pore-size distribution analysis results into pre-determined standard pore geometry groups, in this step supervised PGs are generated from the log response constrained by the relationship between pore-throat geometry (MICP) and pose-size distribution (NMR). Permeability-porosity core relationships are reviewed by sorting and eliminating the outliers or inconsistent samples (damaged or chipped, fractures or with local features). After that, the supervised PGs are used to train and calibrate a supervised neural network (NN) and permeability and rock type's relationships can be captured at log scale. Using dimensionality reduction improves the neural network relationships and thus data population into the petrophysical wells.
The result is a more robust model capable to capture over 80% of the core relationships and able to predict permeability and rock types while preserving the geological features of the reservoir. The application of this method makes possible to determine the relevance of core and log data sources to address rock typing and permeability prediction uncertainties. The applied workflows also show how to break the autocorrelation of variables and maximize the usage of logs.
This work demonstrates that the introduced data-driven methods are useful for rock typing determination and address several of the challenges related to core to log properties derivation.
|File Size||3 MB||Number of Pages||24|
Al-Mudhafar, W. J. (2016, May 5). Incorporation of Bootstrapping and Cross-Validation for Efficient Multivariate Facies and Petrophysical Modeling. Society of Petroleum Engineers. doi:10.2118/180277-MS
Aliakbardoust E., Rahimpour-Bonab H., (2013). Integration of rock typing methods for carbonate reservoir characterization. Journal of Geophysics and Engineering, Volume 10, Issue 5, October 2013, 055004, https://doi.org/10.1088/1742-2132/10/5/055004
Farooq, U., Iskandar, R., Radwan, E. S. M., & Hozayen, M. A. H. (2014, November 10). Linking Diagenesis, NMR, and Dynamic Data for Accurate Flow Characterization of Heterogeneous Carbonate Reservoir. Society of Petroleum Engineers. doi:10.2118/171932-MS
Farooq, U., Caetano, H., & Radwan, E. S. (2017, May 5). The Impact of Dolomitization on Reservoir Quality Evolution of the Fractured Carbonate Reservoir Upper Cretaceous, Onshore Abu Dhabi Oilfield, U.A.E. Society of Petroleum Engineers. doi:10.2118/186035-MS
Gupta, I., Rai, C., Sondergeld, C. H., & Devegowda, D. (2018, August 1). Rock Typing in Eagle Ford, Barnett, and Woodford Formations. Society of Petroleum Engineers. doi:10.2118/189968-PA
Kadkhodaie A., Kadkhodaie-Ilkhchi R., (December 2018). A Review of Reservoir Rock Typing Methods in Carbonate Reservoirs: Relation between Geological, Seismic, and Reservoir Rock Types. DOI: 10.22050/IJOGST.2019.136243.1461.
Karri, S., Al Amari, K., & Vissapragada, B. (2003, January 1). Permeability and RRT Estimation from Conventional Logs in a Middle East Carbonate Reservoir Using Neural Network Approach. Society of Petroleum Engineers. doi:10.2118/81473-MS
Hassall, J., Johnston, J., & Reboul, R. (2015, November 9). A Pragmatic Approach to Rock Typing in Carbonate Formations with Limited MICP Data and Several Vintages of Wireline Logs. Society of Petroleum Engineers. doi:10.2118/177507-MS
Hozayen, M. A. H., & Al Saadi, H. A. (2010, January 1). Integrated Reservoir Characterization Using Facies, Rock Texture and Flow Units Applying Rock Typing Approach, Upper Cretaceous Reservoir, Abu Dhabi Onshore Oil Field, UAE. Society of Petroleum Engineers. doi:10.2118/137557-MS
Nashawi, I. S., & Malallah, A. (2010, January 1). Permeability Prediction from Wireline Well Logs Using Fuzzy Logic and Discriminant Analysis. Society of Petroleum Engineers. doi:10.2118/133209-MS
Moghadasi, L., Ranaee, E., Inzoli, F., & Guadagnini, A. (2017, April 5). Petrophysical Well Log Analysis through Intelligent Methods. Society of Petroleum Engineers. doi:10.2118/185922-MS
Salahuddin, A. A. B., Al Naqbi, S., Syofyan, S., Yousef Alklih, M., & Al Hammadi, K. E. (2018, November 12). Heavily Compartmentalized Reservoir: From Structural Synthesis to Optimum Development Plan. Society of Petroleum Engineers. doi:10.2118/193136-MS
Salman, S. M., & Bellah, S. (2009, January 1). Rock Typing: An Integrated Reservoir Characterization Tool to Construct a Robust Geological Model in Abu Dhabi Carbonate Oil Field. Society of Petroleum Engineers. doi:10.2118/125498-MS
Skalinski M., Kenter J. (January 2014). Carbonate petrophysical rock typing: Integrating geological attributes and petrophysical properties while linking with dynamic behavior. Geological Society London Special Publications 406(1):229–259. DOI: 10.1144/SP406.6
Tang, H. (2009, January 1). Successful Carbonate Well Log Facies Prediction Using an Artificial Neural Network Method: Wafra Maastrichtian Reservoir, Partitioned Neutral Zone (PNZ), Saudi Arabia and Kuwait. Society of Petroleum Engineers. doi:10.2118/123988-MS
Xu, C., Heidari, Z., & Torres-Verdin, C. (2012, January 1). Rock Classification in Carbonate Reservoirs based on Static and Dynamic Petrophysical Properties Estimated from Conventional Well Logs. Society of Petroleum Engineers. doi:10.2118/159991-MS
Wong, P. M., Henderson, D. J., & Brooks, L. J. (1998, April 1). Permeability Determination Using Neural Networks in the Ravva Field, Offshore India. Society of Petroleum Engineers. doi:10.2118/38034-PA