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Summary This paper applies the ensemble Kalman filter (EnKF) to history match a North Sea field model. This is, as far as we know, one of the first published studies in which the EnKF is applied in a realistic setting using real production data. The reservoir-simulation model has approximately 45,000 active grid cells, and 5 years of production data are assimilated. The estimated parameters consist of the permeability and porosity fields, and the results are compared with a model previously established using a manual history-matching procedure. It was found that the EnKF estimate improved the match to the production data. This study, therefore, supported previous findings when using synthetic models that the EnKF may provide a useful tool for history matching reservoir parameters such as the permeability and porosity fields. Introduction The EnKF developed by Evensen (1994, 2003, 2007) is a statistical method suitable for data assimilation in large-scale nonlinear models. It is a Monte Carlo method, where model uncertainty is represented by an ensemble of realizations. The prediction of the estimate and uncertainty is performed by ensemble integration using the reservoir-simulation model. The method provides error estimates at any time based on information from the ensemble. When production data are available, a variance-minimizing scheme is used to update the realizations. The EnKF provides a general and model-independent formulation and can be used to improve the estimates of both the parameters and variables in the model. The method has previously been applied in a number of applications [e.g., in dynamical ocean models (Haugen and Evensen 2002), in model systems describing the ocean ecosystems (Natvik and Evensen 2003a, 2003b), and in applications within meteorology (Houtekamer et al. 2005)]. This shows that the EnKF is capable of handling different types of complex- and nonlinear-model systems. The method was first introduced into the petroleum industry in studies related to well-flow modeling (Lorentzen et al. 2001, 2003). Nævdal et al. (2002) used the EnKF in a reservoir application to estimate model permeability focusing on a near-well reservoir model. They showed that there could be a great benefit from using the EnKF to improve the model through parameter estimation, and that this could lead to improved predictions. Nævdal et al. (2005) showed promising results estimating the permeability as a continuous field variable in a 2D field-like example. Gu and Oliver (2005) examined the EnKF for combined parameter and state estimation in a standardized reservoir test case. Gao et al. (2006) compared the EnKF with the randomized-maximum-likelihood method and pointed out several similarities between the methods. Liu and Oliver (2005a, 2005b) examined the EnKF for facies estimation in a reservoir-simulation model. This is a highly nonlinear problem where the probability-density function for the petrophysical properties becomes multimodal, and it is not clear how the EnKF can best handle this. A method was proposed in which the facies distribution for each ensemble member is represented by two normal distributed Gaussian fields using a method called truncated pluri-Gaussian simulation (Lantuéjoul 2002). Wen and Chen (2006) provided another discussion on the EnKF for estimation of the permeability field in a 2D reservoir-simulation model and examined the effect of the ensemble size. Lorentzen et al. (2005) focused on the sensitivity of the results with respect to the choice of initial ensemble using the PUNQ-S3. Skjervheim et al. (2007) used the EnKF to assimilate seismic 4D data. It was shown that the EnKF can handle these large data sets and that a positive impact could be found despite the high noise level in the data. The EnKF has some important advantages when compared to traditional assisted history-matching methods; the result is an ensemble of history-matched models that are all possible model realizations. The data are processed sequentially in time, meaning that new data are easily accounted for when they arrive. The method allows for simultaneous estimation of a huge number of poorly known parameters such as fields of properties defined in each grid cell. By analyzing the EnKF update equations, it is seen that the actual degrees of freedom in the estimation problem are limited equal to the ensemble size. One is still able to update the most important features of large-scale models. A limitation of the EnKF is the fact that its computations are based on first- and second-order moments, and there are problems that are difficult to handle, particularly when the probability distributions are multimodal (e.g., when representing a bimodal channel facies distribution). This paper considers the use of the EnKF for estimating dynamic and static parameters, focusing on permeability and porosity, in a field model of a StatoilHydro-operated field in the North Sea. The largest uncertainty in the model is expected to be related to the permeability values, especially in the upper part of the reservoir where the uncertainty may be as large as 30%.
- Europe > Norway > North Sea (0.90)
- Europe > United Kingdom > North Sea (0.81)
- Europe > North Sea (0.81)
- (2 more...)
- Europe > Norway > North Sea > Northern North Sea > East Shetland Basin > PL 050 > Block 34/10 > Gullfaks Field > Statfjord Group (0.99)
- Europe > Norway > North Sea > Northern North Sea > East Shetland Basin > PL 050 > Block 34/10 > Gullfaks Field > Lunde Formation (0.99)
- Europe > Norway > North Sea > Northern North Sea > East Shetland Basin > PL 050 > Block 34/10 > Gullfaks Field > Lista Formation (0.99)
- (2 more...)
Bottomhole Pressure Control During Drilling Operations in Gas-Dominant Wells
Nygaard, Gerhard Haukenes (Intl Research Inst of Stavanger) | Vefring, Erlend H. (Intl Research Inst of Stavanger) | Fjelde, Kjell Kåre (Intl Research Inst of Stavanger) | Naevdal, Geir (Intl Research Inst of Stavanger) | Lorentzen, Rolf Johan (Intl Research Inst of Stavanger) | Mylvaganam, Saba (Telemark U. College)
Summary To obtain an underbalanced pressure condition, nitrogen gas can be injected into the drillstring. Simultaneous injection of liquids and gases leads to a highly dynamic flow system. During pipe connections, pressure transients can cause the bottomhole pressure to rise above the pore pressure of the reservoir or fall below the reservoir collapse pressure. Migration of gas during pipe connection and inflow from the reservoir will also cause bottomhole pressure changes. This paper presents a methodology for controlling the bottomhole pressure during drilling operations in gas-dominant wells. The methodology incorporates a dynamic model of the well fluid flow and the well-reservoir interaction. Available control actions during the drilling process are the gas injection rate prior to the pipe connection and choke valve settings during the pipe connection. Measurement of the pump rates, pump pressures, choke pressure and the bottomhole pressure are also available to support the control actions. However, during pipe connections and in the event of transient signal failures, the bottomhole pressure measurements will be suppressed. The control methodology used is based on a nonlinear model predictive control system, which predicts the near-future behavior of the well, and uses these predictions to obtain the optimal choke settings. The model parameters are calibrated using measurements from the well to ensure that the model is suitable for the predictions. A field-based case with gas injection has been examined using this control methodology. The results indicate that model based control can be utilized in developing an automated and integrated pump rate and choke-control system for underbalanced drilling operations. Introduction Injection of nitrogen gas into the drillstring while drilling is often used to obtain underbalanced pressure conditions in the reservoir section of the well. When drilling into a low-pressure reservoir, where the reservoir pore pressure is substantially lower than 1 SG, a large amount of nitrogen gas is needed to achieve underbalanced conditions. This causes the gas properties of the fluid mixture to be dominant in the well. During pipe connection, where the fluid velocity is reduced, gas and liquid segregate because of gravitational forces. In addition, loss of friction pressure causes the pressure difference between the reservoir pressure and the bottomhole well pressure to increase. This results in a larger influx of reservoir fluids into the well. To achieve more stable pressure conditions in the well, the choke setting and the circulation pump rates can be adjusted (Perez-Tellez et al. 2004; Nygaard et al. 2004). This paper evaluates a method for controlling the bottomhole pressure during the whole drilling operation, including operations related to pipe connections.
- Europe > Norway (0.68)
- North America > United States (0.46)
Reservoir Characterization During Underbalanced Drilling (UBD): Methodology and Active Tests
Vefring, Erlend H. (Rogaland Research Centre) | Nygaard, Gerhard H. (RF-Rogaland Research) | Lorentzen, Rolf J. (RF-Rogaland Research) | Naevdal, Geir (International Research Institute of Stavanger AS) | Fjelde, Kjell K. (International Research Institute)
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.
Summary The use of ensemble Kalman filter techniques for continuous updating of reservoir model is demonstrated. The ensemble Kalman filter technique is introduced, and thereafter applied to a simplified 2-D field model, which are generated by using a single horizontal layer from a North Sea field model. By assimilating measured production data, the reservoir model is continuously updated. The updated models give improved forecasts and the forecasts improve as more data is included. Both dynamic variables, such as pressure and saturations, and static variables, such as the permeability, are updated in the reservoir model. Introduction In the management of reservoirs, it is important to utilize all available data in order to make accurate forecasts. For short time forecasts, in particular, it is important that the initial values are consistent with recent measurements. The ensemble Kalman filter1 is a Monte Carlo approach, which is promising with respect to achieving this goal through continuous model updating and reservoir monitoring. In this paper, the ensemble Kalman filter is utilized to update both static parameters, such as the permeability, and dynamic variables, such as the pressure and saturation of the reservoir model. The filter computations are based on an ensemble of realizations of the reservoir model, and when new measurements are available, new updates are obtained by combining the model predictions with the new measurements. Statistics about the model uncertainty is built from the ensemble. When new measurements become available, the filter is used to update all the realizations of the reservoir model. This means that an ensemble of updated realizations of the reservoir model is always available. The ensemble Kalman filter has previously been successfully applied for large-scale nonlinear models in oceanography2 and hydrology3. In those applications, only dynamic variables were tuned. Tuning of model parameters and dynamic variables was done simultaneously in a well flow model used for underbalanced drilling4. In two previous papers5,6, the filter has been used to update static parameters in near-well reservoir models, by tuning the permeability field. In this paper, the filter has been further developed to tune the permeability for simplified real field reservoir simulation models. We present results from a synthetic, simplified real field model. The measurements are well bottom-hole pressures, water cuts and gas/oil ratios. A synthetic model gives the possibility of comparing the solution obtained by the filter to the true solution, and the performance of the filter can be evaluated. It is shown how the reservoir model is updated as new measurements becomes available, and that good forecasts are obtained. The convergence of the reservoir properties to the true solution as more measurements becomes available is investigated. Since the members of the ensemble are updated independently of each other, the method is very suitable for parallel processing. It is also conceptually straightforward to extend the methodology to update other reservoir properties than the permeability. Based on the updated ensemble of models, production forecasts and reservoir management studies may be performed on a single "average" model, which is always consistent with the latest measurements. Alternatively, the entire ensemble may be applied to estimate the uncertainties in the forecasts. Updating reservoir models with ensemble Kalman filter The Kalman filter was originally developed to update the states of linear systems to take into account available measurements7. In our case, the system is a reservoir model, using black oil, and three phases (water, oil and gas).For this model, the solution variables of the system are the pressure and the water saturation, in addition to a third solution variable that depends on the oil and gas saturation. If the gas saturation is zero, the third solution variable becomes the solution gas/oil ratio, if the oil saturation is zero it becomes the vapor oil/gas ratio. Otherwise the third solution variable is the gas saturation. The states of this system are the values of the solution variables for each grid block of the simulation model. This model is non-linear.
- Reservoir Description and Dynamics > Reservoir Simulation > History matching (1.00)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)