A Pore-Network-Modeling Approach To Predict Petrophysical Properties of Diatomaceous Reservoir Rock
- Liping Jia (Stanford University) | Cynthia Marie Ross (Stanford University) | Anthony Robert Kovscek (Stanford University)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- December 2007
- Document Type
- Journal Paper
- 597 - 608
- 2007. Society of Petroleum Engineers
- 3 Production and Well Operations, 5.6.1 Open hole/cased hole log analysis, 5.3.4 Reduction of Residual Oil Saturation, 3.2.3 Hydraulic Fracturing Design, Implementation and Optimisation, 2.4.3 Sand/Solids Control, 5.5 Reservoir Simulation, 5.3.1 Flow in Porous Media, 1.6.9 Coring, Fishing, 5.3.2 Multiphase Flow, 4.3.4 Scale, 5.1.5 Geologic Modeling, 5.1 Reservoir Characterisation, 5.2.1 Phase Behavior and PVT Measurements, 1.2.3 Rock properties, 5.4.6 Thermal Methods
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A 3D pore-network model of two-phase flow was developed to compute permeability, relative permeability, and capillary pressure curves from pore-type, -size, and -shape information measured by means of high-resolution image analysis of diatomaceous-reservoir-rock samples. The diatomite model is constructed using pore-type proportions obtained from image analysis of epoxy-impregnated polished samples and mercury-injection capillary pressure curves for diatomite cores. Multiple pore types are measured, and each pore type has a unique pore-size and throat-size distribution that is incorporated in the model. Network results present acceptable agreement when compared to experimental measurements of relative permeability. The pore-network model is applicable to both drainage and imbibition within diatomaceous reservoir rock. Correlation of network-model results to well log data is discussed, thereby interpolating limited experimental results across the entire reservoir column. Importantly, our method has potential to predict the petrophysical properties for reservoir rocks with either limited core material or those for which conventional experimental measurements are difficult, unsuitable, or expensive.
Model generation for reservoir simulation requires accurate entering of physical properties such as porosity, permeability, initial water saturation, residual-oil saturation, capillary pressure functions, and relative permeability curves. These functions and parameters are necessary to estimate production rate and ultimate oil recovery, and thereby optimize reservoir development. Accurate measurement and representation of such information is, therefore, essential for reservoir modeling.
Relative permeability and capillary pressure curves are the most important constitutive relations to represent multiphase flow. Often, it is difficult to sample experimentally the range of relevant multiphase-flow behavior of a reservoir. In addition to the availability of rock samples, measurements are frequently time consuming to conduct, and conventional techniques are not suitable for all rock types (Schembre and Kovscek 2003). It is impossible, therefore, to measure all the unique relative permeability functions of different reservoir-rock types and variations within a rock type. This lack of constitutive information limits the accuracy of reservoir simulators to predict oil recovery. Simply put, other available data must be queried for their relevance to multiphase flow and must be used to interpret the available relative permeability and capillary pressure information.
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