Fully Integrated 3D-Reservoir Characterization and Flow Simulation Study: A Field Case Example
- Maged Al-Deeb (ADCO) | Gerard Bloch (ADCO) | Salem El-Abd (ADCO) | Mohsen Charfeddine (ADCO) | Asnul Bahar (Kelkar and Associates, Inc.) | Harun Ates (Kelkar and Associates, Inc.) | Tono Soeriawinata (Kelkar and Associates, Inc.) | Mohan Kelkar (The Univ. of Tulsa)
- Document ID
- Society of Petroleum Engineers
- Abu Dhabi International Petroleum Exhibition and Conference, 13-16 October, Abu Dhabi, United Arab Emirates
- Publication Date
- Document Type
- Conference Paper
- 2002. Society of Petroleum Engineers
- 5.1 Reservoir Characterisation, 5.2 Reservoir Fluid Dynamics, 4.3.4 Scale, 5.1.8 Seismic Modelling, 5.6.9 Production Forecasting, 1.6.9 Coring, Fishing, 5.5.7 Streamline Simulation, 5.6.2 Core Analysis, 4.1.5 Processing Equipment, 3.3.6 Integrated Modeling, 5.7.2 Recovery Factors, 5.5.8 History Matching, 5.1.5 Geologic Modeling, 5.3.2 Multiphase Flow, 5.6.5 Tracers, 5.8.6 Naturally Fractured Reservoir, 3.1.2 Electric Submersible Pumps, 5.5.3 Scaling Methods, 4.1.2 Separation and Treating, 5.6.1 Open hole/cased hole log analysis, 2 Well Completion, 5.5 Reservoir Simulation, 5.4.7 Chemical Flooding Methods (e.g., Polymer, Solvent, Nitrogen, Immiscible CO2, Surfactant, Vapex), 5.6.4 Drillstem/Well Testing
- 0 in the last 30 days
- 424 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 9.50|
|SPE Non-Member Price:||USD 28.00|
This paper presents the result of fully 3D integrated reservoir description and flow simulation study of a giant oil field in Middle East using the state of the art technology. The overall goal is to develop a representative reservoir model to form the basis for reservoir management and longterm development planning. This is done by generating alternate reservoir descriptions, based on stochastic models, to quantify uncertainties in the future performance. The data that were integrated include well cores and logs, geological interpretation (stratigraphy, rock type, depositional model), seismic (structure, curvature analysis and inversion-derived porosity), well test, SCAL, production data and fracture distribution.
The 3D multiple realizations were generated by considering rock type and petrophysical properties at well location, obtained from well logs and cores, and simultaneously constrained by seismic derived porosity. The simulations of properties were generated using simultaneous sequential Gaussian simulation where the seismic constraint was introduced via Bayesian Updating procedure. Special consideration was given to the spatial modeling of data where soft information was derived both from hard data and depositional environment. Fracture distribution, derived from seismic curvature analysis, was used in the integration process to match the core-based derived permeability with well test permeability. This distribution was used to obtain permeability anisotropy distribution using newly developed tensorial approach.
A total of forty-eight realizations were generated considering four major types of uncertainties: structure, spatial model, petrophysical properties and simulation path. The results have been used as the basis for fluid in place (STOIIP) calculation using Monte Carlo simulation technique. These realizations are then ranked based on the sweep efficiency, obtained from multiphase streamline simulations, and the STOIIP. Three realizations, representing medium, low and high realizations, were selected and upscaled. An optimum vertical upscaling level was determined using streamline simulator and developing quantitative criterion. This ensures that the representative heterogeneity of the reservoir was maintained during the upscaling process.
Comprehensive history matching was done for the three selected realizations for the entire nineteen years of production history using objective criterion so that the quality of the three matches is similar. The observed data matched include water cuts and measured pressures. The parameters used to match the history are restricted to the parameters that have not been accounted for in the static model. Using probabilistic concepts, uncertainties in future performance were quantified for various scenarios.
Reservoir model is a tool that can be used to obtain better reservoir management and long term development planning. A representative reservoir model can only be achieved by properly integrating various sources of data in a consistent manner. The state of the art of technology in building representative reservoir model is to use the technique(s) that can quantify all possible uncertainties of the future performance.
This paper shows an application of various techniques in integrating various data sources for an oil field in the Middle East. The overall objective was to obtain a representative reservoir model, i.e., model that can be used to quantify uncertainties in the future performance.
|File Size||630 KB||Number of Pages||10|