Konsas: An Exhaustive Reservoir Model to Evaluate the Contribution of Seismic to Reservoir Characterization

2002 ◽  
Author(s):  
Benoit Andrieux ◽  
Frederique Fournier ◽  
Frederic Cailly
Keyword(s):  
Reservoir Characterization ◽  
Reservoir Model

Enhancing Reservoir Characterization by Calibrating 3D Reservoir Model with Inter-Well Data in 2D Space

2019 ◽  
Author(s):  
Shubham Mishra ◽  
Chandramani Shrivastava ◽  
Aditya Ojha ◽  
Fabio Miotti
Keyword(s):  
Reservoir Characterization ◽  
Reservoir Model ◽  
Well Data ◽  
2D Space

Combining Saturation Changes and 4D Seismic for Updating Reservoir Characterizations

2006 ◽  
Vol 9 (05) ◽  
pp. 502-512 ◽  
Author(s):  
Arne Skorstad ◽  
Odd Kolbjornsen ◽  
Asmund Drottning ◽  
Havar Gjoystdal ◽  
Olaf K. Huseby
Keyword(s):  
Wave Velocity ◽  
Seismic Data ◽  
History Matching ◽  
Reservoir Characterization ◽  
Seismic Survey ◽  
Production Data ◽  
Reservoir Model ◽  
Simple Method ◽  
4D Seismic ◽  
Elastic Inversion

Summary Elastic seismic inversion is a tool frequently used in analysis of seismic data. Elastic inversion relies on a simplified seismic model and generally produces 3D cubes for compressional-wave velocity, shear-wave velocity, and density. By applying rock-physics theory, such volumes may be interpreted in terms of lithology and fluid properties. Understanding the robustness of forward and inverse techniques is important when deciding the amount of information carried by seismic data. This paper suggests a simple method to update a reservoir characterization by comparing 4D-seismic data with flow simulations on an existing characterization conditioned on the base-survey data. The ability to use results from a 4D-seismic survey in reservoir characterization depends on several aspects. To investigate this, a loop that performs independent forward seismic modeling and elastic inversion at two time stages has been established. In the workflow, a synthetic reservoir is generated from which data are extracted. The task is to reconstruct the reservoir on the basis of these data. By working on a realistic synthetic reservoir, full knowledge of the reservoir characteristics is achieved. This makes the evaluation of the questions regarding the fundamental dependency between the seismic and petrophysical domains stronger. The synthetic reservoir is an ideal case, where properties are known to an accuracy never achieved in an applied situation. It can therefore be used to investigate the theoretical limitations of the information content in the seismic data. The deviations in water and oil production between the reference and predicted reservoir were significantly decreased by use of 4D-seismic data in addition to the 3D inverted elastic parameters. Introduction It is well known that the information in seismic data is limited by the bandwidth of the seismic signal. 4D seismics give information on the changes between base and monitor surveys and are consequently an important source of information regarding the principal flow in a reservoir. Because of its limited resolution, the presence of a thin thief zone can be observed only as a consequence of flow, and the exact location will not be found directly. This paper addresses the question of how much information there is in the seismic data, and how this information can be used to update the model for petrophysical reservoir parameters. Several methods for incorporating 4D-seismic data in the reservoir-characterization workflow for improving history matching have been proposed earlier. The 4D-seismic data and the corresponding production data are not on the same scale, but they need to be combined. Huang et al. (1997) proposed a simulated annealing method for conditioning these data, while Lumley and Behrens (1997) describe a workflow loop in which the 4D-seismic data are compared with those computed from the reservoir model. Gosselin et al. (2003) give a short overview of the use of 4D-seismic data in reservoir characterization and propose using gradient-based methods for history matching the reservoir model on seismic and production data. Vasco et al. (2004) show that 4D data contain information of large-scale reservoir-permeability variations, and they illustrate this in a Gulf of Mexico example.

Reservoir Characterization During Underbalanced Drilling (UBD): Methodology and Active Tests

SPE Journal ◽  
2006 ◽  
Vol 11 (02) ◽  
pp. 181-192 ◽  
Author(s):  
Erlend H. Vefring ◽  
Gerhard H. Nygaard ◽  
Rolf J. Lorentzen ◽  
Geir Naevdal ◽  
Kjell K. Fjelde
Keyword(s):  
Fluid Flow ◽  
Kalman Filter ◽  
Pore Pressure ◽  
Flow Rate ◽  
Ensemble Kalman Filter ◽  
Reservoir Characterization ◽  
Estimation Methods ◽  
Drilling Process ◽  
Reservoir Model ◽  
Bottomhole Pressure

Summary Two methods for characterizing reservoir pore pressure and reservoir permeability during UBD while applying active tests are presented and evaluated. Both methods utilize a fast, dynamic well fluid-flow model that is extended with a transient reservoir model. Active testing of the well is applied by varying the bottomhole pressure in the well during the drilling operations. The first method uses the Levenberg-Marquardt optimization algorithm to estimate the reservoir parameters by minimizing the difference between measurements from the drilling process and the corresponding model states. The method is applied after the drilling process is finished, using all the recorded measurements. The second method is the ensemble Kalman filter, which simulates the drilling process using the dynamic model while drilling is performed, and updates the model states and parameters each time new measurements are available. Measurements are used that usually are available while drilling are used, such as pump rates, pump pressure, bottomhole pressure, and outlet rates. The methods are applied to different cases, and the results indicate that active tests might improve the estimation results. The results also show that both estimation methods give useful results, and that the ensemble Kalman filter calculates these results during the UB operation. Introduction During UBD, the well pressure is kept below the reservoir pore pressure, and reservoir fluids flow into the well. The flow rate from the reservoir depends on the pressure difference between the reservoir pore pressure and the well pressure, in addition to other reservoir parameters, such as permeability and porosity. The viscosity and compressibility of the reservoir fluids also influence the influx rate. The influx of reservoir fluids causes variations in the annulus section of the well, because of changes in well fluid composition and well fluid-flow rate. By measuring some of the fluid-flow parameters of the well, such as pressures changes and rate changes, the reservoir parameters causing the influx might be identified. This is the principal idea that also is the basis for well testing and transient reservoir analysis. Identification of the reservoir properties close to the well gives important information for planning the well-completion design. If highly productive zones can be located, then the use of smart completion can be better utilized. Reservoir characterization during UBD has received attention from several research groups in recent years. Kardolus and van Kruijsdijk (1997) developed a transient reservoir model based on the boundary-element method. This model was compared with a transient analytical reservoir model. One of their findings was that the transient analytical reservoir model could be used for evaluation of the parameters in the reservoir. In a following study, van Kruijsdijk and Cox (1999) presented a method for identifying the permeability in a horizontal reservoir based on measurements of the reservoir inflow. The flow effects caused by the reservoir boundaries were included in the flow calculations.

2D lateral imaging inversion for directional electromagnetic logging-while-drilling measurements

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. D217-D230 ◽  
Author(s):  
Michael Thiel ◽  
Dzevat Omeragic
Keyword(s):  
Real Time ◽  
Reservoir Characterization ◽  
Reservoir Model ◽  
2D Inversion ◽  
Drilling Technology ◽  
2D Imaging ◽  
Logging While Drilling ◽  
1D And 2D Inversion ◽  
Imaging Plane

Deep-directional electromagnetic (EM) logging-while-drilling technology can map reservoir boundaries and fluid contacts for strategic geosteering, reservoir navigation, and more recently, for reservoir characterization. The inversion-based resistivity mapping is used to make real-time geosteering decisions and to refine and update the reservoir model during and after drilling. Traditional 1D and 2D inversion approaches ignore the lateral changes of the reservoir, which are contained in the azimuthally sensitive measurements and only provide a longitudinal 2D representation of the 3D reservoir structure around the well. A new 2D lateral imaging inversion uses the full azimuthal sensitivity of the measurements to map the vertical and lateral resistivity heterogeneities around the wellbore. A 2.5D EM solver is run in a Gauss-Newton optimization to reconstruct the measurements in complex scenarios and determine the 2D anisotropic resistivity distribution in an imaging plane along with the orientation of the formation invariant direction with respect to the wellbore. Continuous 2D imaging along the well path generates a 3D reservoir resistivity map in the proximity of the wellbore.

Sign in / Sign up

Export Citation Format

Share Document