New Data-Driven Method for Predicting Formation Permeability Using Conventional Well Logs and Limited Core Data
- Mohamad Shabab (University of Waterloo) | Guodong Jin (Baker Hughes Dhahran Global Technology Center) | Ardiansyah Negara (Baker Hughes Dhahran Global Technology Center) | Gaurav Agrawal (Baker Hughes Dhahran Global Technology Center)
- Document ID
- Society of Petroleum Engineers
- SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition, 25-28 April, Dammam, Saudi Arabia
- Publication Date
- Document Type
- Conference Paper
- 2016. Society of Petroleum Engineers
- 5 Reservoir Desciption & Dynamics, 1.6 Drilling Operations, 5.5.2 Core Analysis, 5.6.2 Core Analysis, 6.5.4 Naturally Occurring Radioactive Materials, 5.1 Reservoir Characterisation, 5.6.1 Open hole/cased hole log analysis, 1.6.9 Coring, Fishing, 5.6 Formation Evaluation & Management
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The need for improved data accuracy, cost effectiveness and delivery time to assist in decision making has gained importance in reservoir characterization and evaluation. This paper presents a robust and inexpensive data-driven method for predicting the formation permeability profile from conventional well logs (CWLs) using the support vector regression (SVR) technique with limited core measurements. The method's feasibility and applicability are demonstrated on one field data set from a North Sea well contained a complete suite of logs and extensive core measurements.
The relationship between formation permeability and well logs is often overwhelming complex and nonlinear. We use the SVR method to establish the correlation between CWLs and limited core permeability, thereafter building a permeability-prediction model as a function of selected well logs. The basic logging data used here include density, neutron, deep resistivity, compressional and Stoneley wave slowness. The permeability derived from these well logs generally compares well with the measured core permeability. Additional logging data including the clay weight fraction, thorium/potassium content, or nuclear-magnetic-resonance (NMR) bulk volume movable and irreducible are also separately integrated into those basic logs to determine if the prediction accuracy can be improved. There is no obvious difference among the predicted permeability profiles even these additional well logs are added, which could imply that the basic logs are sufficient to generate the permeability with good accuracy.
SVR method could be used to improve the log interpretation accuracy as shown in this study. It can be easily adapted to predict other rock electrical, mechanical and petrophysical properties when only conventional logs and few core measurements are available. It is especially useful for unconventional reservoirs where traditional models may not be applicable and new methods are still evolving. Such new data analysis technologies could optimize our logging service and core analysis planning.
|File Size||2 MB||Number of Pages||10|
Vapnik, V., Golowich, S. E., and Smola, A., 1997, Support vector method for function approximation, regression estimation, and signal processing. In Advances in Neural Information Processing Systems 9, ed. By Mozer, M. C., Jordan, M. I., and Petsche, T., P. 281–287, Cambridge, Massachusetts: MIT Press.