Closed Loop Feedback Control of Injection and Production Wells in the SPE Brugge Model

Imperial Users onl

Strategies for real time reservoir management

Real–time reservoir management is developed to manage a shrinking labor force and rising demand on energy supply. This dissertation seeks good strategies for real–time reservoir management. First, two simulator–independent optimization algorithms are investigated: ensemble–based optimization (EnOpt) and bound optimization by quadratic approximation (BOBYQA). Multiscale regularization is applied to both to find appropriate frequencies for well control adjustment. Second, two gathered EnKF methods are proposed to save computational cost and reduce sampling error: gathered EnKF with a fixed gather size and adaptively gathered EnKF. Finally, oil price uncertainty is forecasted and quantified with three price forecasting models: conventional forecasting, bootstrap forecasting and sequential Gaussian simulation forecasting. The relative effect of oil price and its volatility on the optimization strategies are investigated. A number of key findings of this dissertation are: (a) if multiscale regularization is not used, EnOpt converges to a higher net present value (NPV) than BOBYQA—even though BOBYQA uses second order Hessian information whereas EnOpt uses first order gradients. BOBYQA performs comparably only if multiscale regularization is used. Multiscale regularization results in a higher optimized NPV with simpler well control strategies and converges in fewer iterations; (b) gathering observations not only reduces the sampling errors but also saves significant amount of computational cost. In addition, adaptively gathered EnKF is superior to gathered EnKF with a fixed gather size when the prior ensemble mean is not near the truth; (c) it is shown that a good oil price forecasting model can improve NPV by more than four percent, and (d) instability in oil prices also causes fluctuation in optimized well controls

Joint History Matching of Multiple Types of Field Data in a 3D Field-Scale Case Study

This work presents an ensemble-based workflow to simultaneously assimilate multiple types of field data in a proper and consistent manner. The aim of using multiple field datasets is to improve the reliability of estimated reservoir models and avoid the underestimation of uncertainties. The proposed framework is based on an integrated history matching workflow, in which reservoir models are conditioned simultaneously on production, tracer and 4D seismic data with the help of three advanced techniques: adaptive localization (for better uncertainty quantification), weight adjustment (for balancing the influence of different types of field data), and sparse data representation (for handling big datasets). The integrated workflow is successfully implemented and tested in a 3D benchmark case with a set of comparison studies (with and without tracer data). The findings of this study indicate that joint history matching using production, tracer and 4D seismic data results in better estimated reservoir models and improved forecast performance. Moreover, the integrated workflow is flexible, and can be extended to incorporate more types of field data for further performance improvement. As such, the findings of this study can help to achieve a better understanding of the impacts of multiple datasets on history matching performance, and the proposed integrated workflow could serve as a useful tool for real field case studies in general.publishedVersio

Effective Reservoir Management Using Streamline-Based Reservoir Simulation, History Matching and Rate Allocation Optimization

The use of the streamline-based method for reservoir management is receiving increased interest in recent years because of its computational advantages and intuitive appeal for reservoir simulation, history matching and rate allocation optimization. Streamline-based method uses snapshots of flow path of convective flow. Previous studies proved its applicability for convection dominated process such as waterflooding and tracer transport. However, for a case with gas injection with strong capillarity and gravity effects, the streamline-based method tends to lose its advantages for reservoir simulation and may result in loss of accuracy and applicability for history-matching and optimization problems. In this study, we first present the development of a 3D 3-phase black oil and compositional streamline simulator. Then, we introduce a novel approach to incorporate capillary and gravity effects via orthogonal projection method. The novel aspect of our approach is the ability to incorporate transverse effects into streamline simulation without adversely affecting its computational efficiency. We demonstrate our proposed method for various cases, including CO2 injection scenario. The streamline model is shown to be particularly effective to examine and visualize the interactions between heterogeneity which resulting impact on the vertical and areal sweep efficiencies. Next, we apply the streamline simulator to history matching and rate optimization problems. In the conventional approach of streamline-based history matching, the objective is to match flow rate history, assuming that reservoir energy was matched already, such as pressure distribution. The proposed approach incorporates pressure information as well as production flow rates, aiming that reservoir energy are also reproduced during production rate matching. Finally, we develop an NPV-based optimization method using streamline-based rate reallocation algorithm. The NPV is calculated along streamline and used to generate diagnostic plots of the effectiveness of wells. The rate is updated to maximize the field NPV. The proposed approach avoids the use of complex optimization tools. Instead, we emphasize the visual and the intuitive appeal of streamline methods and utilize flow diagnostic plots for optimal rate allocation. We concluded that our proposed approach of streamline-based simulation, inversion and optimization algorithm improves computational efficiency and accuracy of the solution, which leads to a highly effective reservoir management tool that satisfies industry demands

Integration of Time-lapse Seismic Data Using the Onset Time Approach: the Impact of Seismic Survey Frequency

The seismic inversion method using the seismic onset times has shown great promise for integrating frequent seismic surveys for updating geologic models. However, due to the high cost of seismic surveys, frequent seismic surveys are not commonly available. In this study, we focus on analyzing the impact of seismic survey frequency on the onset time approach, aiming to extend the advantages of onset time approach when infrequent seismic surveys are available. To analyze the impact of seismic survey frequency on the onset time approach, first, we conduct a sensitivity analysis based on the frequent seismic survey data (over 175 surveys) of steam injection in a heavy oil reservoir (Peace River Unit) in Canada. The calculated onset time maps based on seismic survey data sampled at various intervals from the frequent data sets are compared to examine the need and effectiveness of interpolation between surveys. Additionally, we compared the onset time inversion with traditional seismic amplitude inversion and quantitatively investigate the nonlinearity and robustness for these two inversion methods. The sensitivity analysis shows that using interpolation between seismic surveys to calculate the onset time, an adequate onset time map can be extracted from the infrequent seismic surveys. This holds good as long as there are no changes in the underlying physical mechanisms during the interpolation period. It is concluded that the linear interpolation is more efficient and robust than the Lagrange interpolation. A 2D waterflooding case demonstrates the necessity of interpolation for resolving into the large time span between the seismic surveys and obtaining more accurate model update and more efficient misfit reduction. The Brugge benchmark case shows that the onset time inversion method obtains comparable permeability update as the traditional seismic amplitude inversion method while being much more efficient. This results from the significant data reduction achieved by integrating a single onset time map rather than multiple sets of amplitude maps. The onset time approach also achieves superior convergence performance, resulting from its quasi-linear properties. It is found that the nonlinearity of the onset time method is smaller than that of the amplitude inversion method by several orders of magnitude

Multiobjective and Level Set Methods for Reservoir Characterization and Optimization

Proper management of oil and gas reservoirs as dynamic systems reduces operational expenditures, alleviates uncertainty, and increases hydrocarbon recovery. In this dissertation, we focus on two issues in reservoir management: multiobjective integration and channelized reservoir calibration. Multiple objectives, including bottom-hole pressure (BHP), water cut, and 4-D seismic data, are utilized in model ranking, history matching, and production optimization. These objectives may conflict, as they represent characteristics coming from different measurements and sources, and, significantly, of varying scales. A traditional weighted-sum method may reduce the solution space, often leading to loss of key information for each objective. Thus, how to integrate multiple objectives effectively becomes critical in reservoir management. This dissertation presents a Pareto-based approach to characterize multiobjective and potentially conflicting features and to capture geologic uncertainty, preserving the original objective space and avoiding weights determination as in the weight-sum method. For channelized reservoirs, identification of the channel geometry and facies boundaries, as well as characterization of channel petrophysical properties are critical for performance predictions. Traditional history matching methods, however, are unable to preserve the channel geometry. We propose a level set based method, integrated with seismic constraint and coupled with the Grid Connectivity Transform (GCT) for channelized reservoirs calibration. We first develop the Pareto-based model ranking (PBMR) to rank multiple realizations, taking into consideration seismic and production data. We demonstrate that this approach can be applied to select multiple competitive realizations compared with the weighted-sum method, and uncertainty range of each objective can be effectively addressed. Next, we extend the Pareto-based framework to full-field history matching and production optimization of the Norne Field in the North Sea. A hierarchical history matching workflow including global and local updates helps to capture the large- and fine-scale heterogeneity. A two-step polymer flood optimization consisting of the streamline-based rate optimization and the Pareto-based polymer optimization is shown to be beneficial for reducing the impact of heterogeneity and increasing production improvement as well as NPV. Finally, we propose a two-step history matching workflow for facies and property calibration of the channelized reservoirs, where the channel geometry is modeled using the level set method, and smaller scale heterogeneity is modeled using the GCT. Moreover, the seismic constraints incorporated into the level set improves facies model calibration

Optimal Waterflood Management under Geologic Uncertainty Using Rate Control: Theory and Field Applications

Waterflood optimization via rate control is receiving increased interest because of rapid developments in the smart well completions and I-field technology. The use of inflow control valves (ICV) allows us to optimize the production/injection rates of various segments along the wellbore, thereby maximizing sweep efficiency and delaying water breakthrough. It is well recognized that field scale rate optimization problems are difficult because they often involve highly complex reservoir models, production and facilities related constraints and a large number of unknowns. Some aspects of the optimization problem have been studied before using mainly optimal control theory. However, the applications to-date have been limited to rather small problems because of the computation time and the complexities associated with the formulation and solution of adjoint equations. Field-scale rate optimization for maximizing waterflood sweep efficiency under realistic field conditions has still remained largely unexplored. We propose a practical and efficient approach for computing optimal injection and production rates and thereby manage the waterflood front to maximize sweep efficiency and delay the arrival time to minimize water cycling. Our work relies on equalizing the arrival times of the waterfront at all producers within selected sub-regions of a water flood project. The arrival time optimization has favorable quasi-linear properties and the optimization proceeds smoothly even if our initial conditions are far from the solution. We account for geologic uncertainty using two optimization schemes. The first one is to formulate the objective function in a stochastic form which relies on a combination of expected value and standard deviation combined with a risk attitude coefficient. The second one is to minimize the worst case scenario using a min-max problem formulation. The optimization is performed under operational and facility constraints using a sequential quadratic programming approach. A major advantage of our approach is the analytical computation of the gradient and Hessian of the objective which makes it computationally efficient and suitable for large field cases. Multiple examples are presented to support the robustness and efficiency of the proposed optimization scheme. These include several 2D synthetic examples for validation purposes and 3D field applications