Optimal Reservoir Management And Well Placement Under Geologic Uncertainty
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| Author | : Satyajit Vijay Taware |
| Publisher | : |
| Total Pages | : |
| Release | : 2012 |
| Genre | : |
| ISBN | : |
Reservoir management, sometimes referred to as asset management in the context of petroleum reservoirs, has become recognized as an important facet of petroleum reservoir development and production operations. In the first stage of planning field development, the simulation model is calibrated to dynamic data (history matching). One of the aims of the research is to extend the streamline based generalized travel time inversion method for full field models with multimillion cells through the use of grid coarsening. This makes the streamline based inversion suitable for high resolution simulation models with decades long production history and numerous wells by significantly reducing the computational effort. In addition, a novel workflow is proposed to integrate well bottom-hole pressure data during model calibration and the approach is illustrated via application to the CO2 sequestration. In the second stage, field development strategies are optimized. The strategies are primarily focused on rate optimization followed by infill well drilling. A method is proposed to modify the streamline-based rate optimization approach which previously focused on maximizing sweep efficiency by equalizing arrival time of the waterfront to producers, to account for accelerated production for improving the net present value (NPV). Optimum compromise between maximizing sweep efficiency and maximizing NPV can be selected based on a 'trade-off curve'. The proposed method is demonstrated on field scale application considering geological uncertainty. Finally, a novel method for well placement optimization is proposed that relies on streamlines and time of flight to first locate the potential regions of poorly swept and drained oil. Specifically, the proposed approach utilizes a dynamic measure based on the total streamline time of flight combined with static and dynamic parameters to identify "Sweet-Spots" for infill drilling. The "Sweet-Spots" can be either used directly as potential well-placement locations or as starting points during application of a formal optimization technique. The main advantage of the proposed method is its computational efficiency in calculating dynamic measure map. The complete workflow was also demonstrated on a multimillion cell reservoir model of a mature carbonate field with notable success. The infill locations based on dynamic measure map have been verified by subsequent drilling.
| Author | : Reza Yousefzadeh |
| Publisher | : Springer Nature |
| Total Pages | : 142 |
| Release | : 2023-04-08 |
| Genre | : Technology & Engineering |
| ISBN | : 3031280792 |
This book explores methods for managing uncertainty in reservoir characterization and optimization. It covers the fundamentals, challenges, and solutions to tackle the challenges made by geological uncertainty. The first chapter discusses types and sources of uncertainty and the challenges in different phases of reservoir management, along with general methods to manage it. The second chapter focuses on geological uncertainty, explaining its impact on field development and methods to handle it using prior information, seismic and petrophysical data, and geological parametrization. The third chapter deals with reducing geological uncertainty through history matching and the various methods used, including closed-loop management, ensemble assimilation, and stochastic optimization. The fourth chapter presents dimensionality reduction methods to tackle high-dimensional geological realizations. The fifth chapter covers field development optimization using robust optimization, including solutions for its challenges such as high computational cost and risk attitudes. The final chapter introduces different types of proxy models in history matching and robust optimization, discussing their pros and cons, and applications. The book will be of interest to researchers and professors, geologists and professionals in oil and gas production and exploration.
| Author | : Y. Zee Ma |
| Publisher | : AAPG |
| Total Pages | : 329 |
| Release | : 2011-12-20 |
| Genre | : Science |
| ISBN | : 0891813780 |
| Author | : Shahed Rahim |
| Publisher | : |
| Total Pages | : 125 |
| Release | : 2015 |
| Genre | : Reservoirs |
| ISBN | : |
Three different studies related to geological uncertainty reduction in reservoir applications are performed in this thesis. The first study proposes an optimal realization reduction framework for quantifying geological uncertainty. The second study applies the optimal realization reduction framework to incorporate geological uncertainty into an application of vertical well placement optimization. The third study proposes a two stage Steam Assisted Gravity Drainage (SAGD) well drainage area (DA) arrangement optimization method and incorporates geological uncertainty to the SAGD arrangement optimization by using the optimal realization reduction framework. Geological uncertainty is reduced by generating and incorporating multiple realizations in the application of reservoir development or controls optimization. However, only few realizations are selected from a large superset for the reservoir application due to intensive computational efforts. The proposed optimal realization reduction method is a mixed integer linear optimization model which minimizes the probability distance between the discrete distribution represented by the superset of realizations and the reduced discrete distribution represented by the selected realizations. The results of applying the realization reduction method to various case studies show that the proposed method can effectively select realizations and assign probabilities such that the extreme and expected reservoir performances are recovered better than any of the other realization reduction methods. The optimal realization reduction method is then used to select a subset of realizations and incorporate them into a framework of robust vertical well placement optimization under geological uncertainty. Applying the well placement optimization framework to reservoirs demonstrate the similarity between the expected reservoir performance results from well placement optimization using the realization reduction method and well placement optimization using all the realizations in the superset. SAGD is an increasingly popular in-situ method for extraction of bitumen from Alberta's oil sands. The first stage of the model determines the optimal arrangement of the compact set of all the DAs that maximize the available bitumen. The second stage of the model selects a smaller set of DA and surface pad (SP) from the compact arrangement that maximize the available bitumen and minimizes the distance between the selected SPs. Results of applying the SAGD well arrangement optimization method to a reservoir lease area showed a compact DA arrangement with DAs containing higher bitumen content and SPs in close proximity to each other being selected. Geological uncertainty is incorporated to the optimization method by using selected realizations obtained from the optimal realization reduction framework. Results showed DA arrangement plan with higher expected bitumen and greater number of DAs within the compact arrangement.
| Author | : Dehua Liu |
| Publisher | : Springer |
| Total Pages | : 394 |
| Release | : 2017-03-15 |
| Genre | : Science |
| ISBN | : 3662532875 |
The book is focused primarily on characteristics and determinative methods of reservoir orientation, the concept of vector well pattern and corresponding realistic techniques of well pattern deployment, well pattern control principles, Optimum design of well pattern based on the reservoir direction characteristics, and the schemes of well spacing density regulation at different stages of development. The procedures for improving water flooding efficiency have been provided. This book is suitable for reservoir engineering managers, reservoir engineers, and students of petroleum engineering.
| Author | : Zeid M. Alghareeb |
| Publisher | : |
| Total Pages | : 237 |
| Release | : 2015 |
| Genre | : |
| ISBN | : |
Reservoir management and decision-making is often cast as an optimization problem where we seek to maximize the field's potential recovery while minimizing associated operational costs. Two reservoir management aspects are considered here, new well placement and production controls. Reservoir simulators are at the heart of this process as they aid in identifying best field development plans. The computational cost associated with managing realistic reservoirs is however substantial due to the significant number of unknowns evaluated by the simulator as well as the number of simulations required to achieve an optimal plan-it involves hundreds to thousands of reservoir simulation runs. Reduced-order models (ROM) are considered powerful techniques to address computational challenges associated with reservoir management decision-making. In this sense, they represent perfect alternatives that trade off accuracy for speed in a controllable manner. In this work, we focus on developing model-order reduction techniques that entail the use of proper orthogonal decomposition (POD), truncated balanced realization (TBR) and discrete empirical interpolation (DEIM) to accurately reproduce the full-order model (FOM) input/output behavior. POD allows for a concise representation of the FOM in terms of relatively few variables while TBR improves the overall stability and accuracy. DEIM improves the shortcomings of POD and TBR in the case of nonlinear PDEs, i.e., saturation equation, by retaining nonlinearities in lower dimensional space. Example cases demonstrate ROMs ability to reduce the computational costs by 0(100) while providing close overall agreement to FOM for instances with significant difference in boundary conditions (well placements and controls). ROMs are potentially perfect alternatives to FOMs in reservoir management intensive studies such as field development and optimization. However, ROMs presented in this thesis and the overall physics-based ROMs have the tendency to perform well within a restricted zone. This zone is generally dictated by the training simulations (with a specific set of boundary conditions) used to build the ROM. Therefore, special care is considered when implementing these training runs. To mitigate the heuristic process of implementing training runs (multiple boundary conditions training runs), we apply a trust-region approach that provides an adaptive framework to systemically retrain and update ROMs utilizing new solutions (flow) characteristics revealed during the course of the optimization run. The adaptive framework for determining the optimal well placements entails the development of a hybrid optimization algorithm, MCSMADS, that combines positive features of both local and global optimization methods. Typical FOM is used in conjunction with MCS to globally search the optimization surface while ROMs are used in conjunction with MADS to further improve the solution quality with minimum increase in computational costs. Well production controls are optimized sequentially via gradient-based trust-region approach. ROMs in this approach replace the FOM to find optimal solutions within a trust-region (subset of the optimization space). At the end of each trust-region optimization, the accuracy of the obtained solution is assessed and the ROM is updated. Both approaches are capable of handling nonlinear constraints. They are treated using a filter-based technique. The developed framework for adaptive ROMs is applied to two realistic field examples. The first example considers maximizing net present value (NPV) through sequentially optimizing well placements and controls while the second example considers maximizing recovery through minimizing Lorenz coefficient. Nonlinear constraints including well-to-well distance and field production limits are imposed in both examples. For all cases considered, the hybrid approach for well placement based on MCS-MADS was able to constantly provide better solution quality (up to 22% increase in NPV) when compared to standalone MCS with only 3% increase in computational costs. The incorporation of ROMs for well controls was shown to reduce computational cost by 96% with only 1% difference in solution quality when compared to FOM.
| Author | : Yijie Zhou |
| Publisher | : |
| Total Pages | : |
| Release | : 2013 |
| Genre | : |
| ISBN | : |
Tight gas reservoirs provide almost one quarter of the current U.S. domestic gas production, with significant projected increases in the next several decades in both the U.S. and abroad. These reservoirs constitute an important play type, with opportunities for improved reservoir simulation & management, such as simulation model design, well placement. Our work develops robust and efficient strategies for improved tight gas reservoir simulation and management. Reservoir simulation models are usually acquired by upscaling the detailed 3D geologic models. Earlier studies of flow simulation have developed layer-based coarse reservoir simulation models, from the more detailed 3D geologic models. However, the layer-based approach cannot capture the essential sand and flow. We introduce and utilize the diffusive time of flight to understand the pressure continuity within the fluvial sands, and develop novel adaptive reservoir simulation grids to preserve the continuity of the reservoir sands. Combined with the high resolution transmissibility based upscaling of flow properties, and well index based upscaling of the well connections, we can build accurate simulation models with at least one order magnitude simulation speed up, but the predicted recoveries are almost indistinguishable from those of the geologic models. General practice of well placement usually requires reservoir simulation to predict the dynamic reservoir response. Numerous well placement scenarios require many reservoir simulation runs, which may have significant CPU demands. We propose a novel simulation-free screening approach to generate a quality map, based on a combination of static and dynamic reservoir properties. The geologic uncertainty is taken into consideration through an uncertainty map form the spatial connectivity analysis and variograms. Combining the quality map and uncertainty map, good infill well locations and drilling sequence can be determined for improved reservoir management. We apply this workflow to design the infill well drilling sequence and explore the impact of subsurface also, for a large-scale tight gas reservoir. Also, we evaluated an improved pressure approximation method, through the comparison with the leading order high frequency term of the asymptotic solution. The proposed pressure solution can better predict the heterogeneous reservoir depletion behavior, thus provide good opportunities for tight gas reservoir management. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/151291
| Author | : Elnur Aliyev |
| Publisher | : |
| Total Pages | : |
| Release | : 2015 |
| Genre | : |
| ISBN | : |
Determining the well locations and settings that maximize reservoir performance is a key issue in reservoir management. Computational optimization procedures are commonly applied for this purpose. In conventional optimization methods, flow simulation is performed over the basic reservoir model. If optimization under uncertainty is considered, multiple geological realizations, which quantify reservoir uncertainty, are typically used. This can result in great computational expense, as commonly used optimization methods may require thousands of function evaluations (each of which entails multiple flow simulations). Efficient surrogate models, which can be used to reduce the computational requirements, would thus be highly beneficial. In this thesis, a multilevel optimization procedure, in which optimization is performed over a sequence of upscaled models, is developed for use in combined well placement and control problems. The multilevel framework is applicable for use with any type of optimization algorithm. In this work it is implemented within the context of a particle swarm optimization -- mesh adaptive direct search (PSO-MADS) hybrid technique. An accurate global transmissibility upscaling procedure is applied to generate the coarse-model parameters required at each grid level. Distinct upscaled models are constructed using this approach for each candidate solution proposed by the optimizer at each grid level. We demonstrate that the coarse models are able to capture the basic ranking of the candidate well location and control scenarios, in terms of objective function value, relative to the ranking that would be computed using fine-scale simulations. This enables the optimization algorithm to appropriately select and discard candidate solutions. The multilevel optimization framework is further extended for use in optimization under geological uncertainty. Toward this goal, we introduce an accelerated multilevel optimization procedure, in which the full PSO-MADS algorithm is used only at the coarsest grid level. At subsequent grid levels, standalone MADS (a local optimization algorithm) is applied. An optimization with sample validation (OSV) procedure is also incorporated into the multilevel method. This approach enables us to optimize over a limited set of geological realizations. The combined use of accelerated multilevel optimization and OSV leads to substantial speedup compared to direct application of the standard multilevel optimization procedure. Optimization results for two- and three-dimensional example cases, which involve both single and multiple geological realizations, are presented. The multilevel procedure for single realizations is shown to provide optimal solutions that are comparable, and in some cases better, than those from the conventional (single-level) approach. Computational speedups of about a factor of five to ten are achieved. For optimization under uncertainty, we use the accelerated multilevel procedure with sample validation to further reduce the computational cost of the optimization. Speedups of a factor of 10--20 are achieved by the accelerated multilevel approach relative to the conventional procedure for examples with ten realizations. An additional speedup factor of about two is observed through incorporation of OSV for cases involving 100 realizations. Our overall findings thus suggest that this framework may be quite useful for practical field development. We also investigate the application of the multilevel Monte Carlo approach for field development optimization under geological uncertainty as an alternative to the multilevel optimization technique. Although this approach provides speedup relative to the conventional (single-level) treatment, it is not as efficient as the multilevel procedure developed in this work.
| Author | : Adrian Morales |
| Publisher | : |
| Total Pages | : |
| Release | : 2011 |
| Genre | : |
| ISBN | : |
Hydrocarbon use has been increasing and will continue to increase for the foreseeable future in even the most pessimistic energy scenarios. Over the past few decades, natural gas has become the major player and revenue source for many countries and multinationals. Its presence and power share will continue to grow in the world energy mix. Much of the current gas reserves are found in gas condensate reservoirs. When these reservoirs are allowed to deplete, the pressure drops below the dew point pressure and a liquid condensate will begin to form in the wellbore or near wellbore formation, possibly affecting production. A field optimization includes determining the number of wells, type (vertical, horizontal, multilateral, etc.), trajectory and location of wells. Optimum well placement has been studied extensively for oil reservoirs. However, well placement in gas condensate reservoirs has received little attention when compared to oil. In most cases involving a homogeneous gas reservoir, the optimum well location could be determined as the center of the reservoir, but when considering the complexity of a heterogeneous reservoir with initial compositional variation, the well placement dilemma does not produce such a simple result. In this research, a horizontal well placement problem is optimized by using a modified Genetic Algorithm. The algorithm presented has been modified specifically for gas condensate reservoirs. Unlike oil reservoirs, the cumulative production in gas reservoirs does not vary significantly (although the variation is not economically negligible) and there are possibly more local optimums. Therefore the possibility of finding better production scenarios in subsequent optimization steps is not much higher than the worse case scenarios, which delays finding the best production plan. The second modification is developed in order to find optimum well location in a reservoir with geological uncertainties. In this modification, for the first time, the probability of success of optimum production is defined by the user. These modifications magnify the small variations and produce a faster convergence while also giving the user the option to input the probability of success when compared to a Standard Genetic Algorithm.
| Author | : Paulo Sergio Da Cruz |
| Publisher | : |
| Total Pages | : 434 |
| Release | : 2000 |
| Genre | : Decision making |
| ISBN | : |
