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.

Gravimetric Monitoring of Water Influx Into a Gas Reservoir: A Numerical Study Based on the Ensemble Kalman Filter

SPE Journal ◽  
2011 ◽  
Vol 17 (01) ◽  
pp. 163-176 ◽  
Author(s):  
M.. Glegola ◽  
P.. Ditmar ◽  
R.G.. G. Hanea ◽  
F.C.. C. Vossepoel ◽  
R.. Arts ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Gravity Data ◽  
Added Value ◽  
Reservoir Model ◽  
Gas Reservoir ◽  
Gas Field ◽  
Water Influx ◽  
Reservoir Permeability

Summary Water influx into gas fields can reduce recovery factors by 10–40%. Therefore, information about the magnitude and spatial distribution of water influx is essential for efficient management of waterdrive gas reservoirs. Modern geophysical techniques such as gravimetry may provide a direct measure of mass redistribution below the surface, yielding additional and valuable information for reservoir monitoring. In this paper, we investigate the added value of gravimetric observations for water-influx monitoring into a gas field. For this purpose, we use data assimilation with the ensemble Kalman filter (EnKF) method. To understand better the limitations of the gravimetric technique, a sensitivity study is performed. For a simplified gas-reservoir model, we assimilate the synthetic gravity measurements and estimate reservoir permeability. The updated reservoir model is used to predict the water-front position. We consider a number of possible scenarios, making various assumptions on the level of gravity measurement noise and on the distance from the gravity observation network to the reservoir formation. The results show that with increasing gravimetric noise and/or distance, the updated model permeability becomes smoother and its variance higher. Finally, we investigate the effect of a combined assimilation of gravity and production data. In the case when only production observations are used, the permeability estimates far from the wells can be erroneous, despite a very accurate history match of the data. In the case when both production and gravity data are combined within a single data assimilation framework, we obtain a considerably improved estimation of the reservoir permeability and an improved understanding of the subsurface mass flow. These results illustrate the complementarity of both types of measurements, and more generally, the experiments show clearly the added value of gravity data for monitoring water influx into a gas field.

Data Assimilation of Coupled Fluid Flow and Geomechanics Using the Ensemble Kalman Filter

SPE Journal ◽  
2010 ◽  
Vol 15 (02) ◽  
pp. 382-394 ◽  
Author(s):  
Haibin Chang ◽  
Yan Chen ◽  
Dongxiao Zhang
Keyword(s):  
Fluid Flow ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Material Properties ◽  
Ensemble Kalman Filter ◽  
Oil Field ◽  
Reservoir Rock ◽  
Model Parameters ◽  
Stress Strain ◽  
Reservoir Flow

Summary In reservoir history matching or data assimilation, dynamic data, such as production rates and pressures, are used to constrain reservoir models and to update model parameters. As such, even if under certain conceptualization the model parameters do not vary with time, the estimate of such parameters may change with the available observations and, thus, with time. In reality, the production process may lead to changes in both the flow and geomechanics fields, which are dynamically coupled. For example, the variations in the stress/strain field lead to changes in porosity and permeability of the reservoir and, hence, in the flow field. In weak formations, such as the Lost Hills oil field, fluid extraction may cause a large compaction to the reservoir rock and a significant subsidence at the land surface, resulting in huge economic losses and detrimental environmental consequences. The strong nonlinear coupling between reservoir flow and geomechanics poses a challenge to constructing a reliable model for predicting oil recovery in such reservoirs. On the other hand, the subsidence and other geomechanics observations can provide additional insight into the nature of the reservoir rock and help constrain the reservoir model if used wisely. In this study, the ensemble-Kalman-filter (EnKF) approach is used to estimate reservoir flow and material properties by jointly assimilating dynamic flow and geomechanics observations. The resulting model can be used for managing and optimizing production operations and for mitigating the land subsidence. The use of surface displacement observations improves the match to both production and displacement data. Localization is used to facilitate the assimilation of a large amount of data and to mitigate the effect of spurious correlations resulting from small ensembles. Because the stress, strain, and displacement fields are updated together with the material properties in the EnKF, the issue of consistency at the analysis step of the EnKF is investigated. A 3D problem with reservoir fluid-flow and mechanical parameters close to those of the Lost Hills oil field is used to test the applicability.

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