Smart wells provide great potential to improve the recovery from hydrocarbon resources. Smart wells provide the ability to control uncertainties associated with reservoir heterogeneity. One example is to mitigate unexpected water production due to fractures and hence increase the ultimate recovery. This is achieved by selectively controlling production from multiple laterals. Due to subsurface communication between laterals that have different productivity indices, it is difficult in practice to optimize production from smart wells. The optimization of smart wells involves adjusting parameters including the settings of the downhole inflow control valves (ICV) that act as subsurface chokes.This paper focused on the reservoir engineering aspects of finding the optimum ICV configuration that optimizes reservoir performance parameters such as recovery factor and net present value. Also, the work studied the effect of heterogeneity, mainly fractures, on the optimization process. This paper also proposes a technique to quantify the effect of fractures on the optimization process and to provide recommendations of further analysis.Genetic algorithm (GA) was used as the main optimization engine to find the optimum ICV configuration. The GA was accompanied by a data library (proxy) to reduce the number of required simulation runs. A commercial reservoir simulator was used as the objective function evaluator that assesses the outcome of candidate ICV configurations.Several examples are presented to show the improvement in reservoir performance made using the optimization process. These examples include a synthetic model, and a real onshore model. Various objective functions were optimized such as water cut, and net present value. IntroductionWell design and planning have advanced tremendously during the last two decades, from the use of conventional vertical wells to nonconventional horizontal wells (NCWs) using directional drilling technology. Nonconventional wells range from simple horizontal wells with single wellbore to complex multilaterals with multiple sublaterals (fishbone wells).Nonconventional wells offer more cost-effective alternatives to conventional wells in terms of drilling, completion, surface equipment, and long-term operation costs. Production targets are achieved with fewer nonconventional wells as they provide better reservoir exposure. From a reservoir management point of view, nonconventional wells improve the productivity index (PI) by maximizing reservoir contact, minimizing water coning by operating at lower drawdown, and increasing sweep efficiency by distributing production along the horizontal section.A 'smart' or 'intelligent' well is considered one of the most advanced types of nonconventional wells. A typical smart well is equipped with a special completion that has packers or sealing elements that allow partitioning of the wellbore, as well as pressure and temperature sensors and downhole inflow control valves (ICV) installed on the production tubing, Figure 1. The sensors allow continuous m...
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. A major challenge for practical field implementation of this technology is dealing with geologic uncertainty. In practice, the reservoir geology is known only in a probabilistic sense; hence, the optimization of smart wells should be carried out in a stochastic framework to account for geologic uncertainty. We propose a practical and efficient approach for computing optimal injection and production rates accounting for geological uncertainty. The approach relies on equalizing arrival time of the waterfront at all producers using multiple geologic realizations. The main objective is to improve sweep efficiency and thereby improve oil production and recovery. 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 function 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 2D synthetic examples for validation and a 3D field-scale application. The role of geologic uncertainty in the outcome of the optimization is demonstrated both during the early stage and also, later stages of waterflooding when substantial production history is available. Introduction The recent increase in oil demand worldwide combined with the decreasing number of new discoveries has underscored the need to efficiently produce existing oil fields. The maturity of most of the existing large fields requires prudent reservoir management and development strategies to maximize recovery. With this goal in mind, the use of smart/complex wells and completions are becoming increasingly common place. Among the various improved recovery schemes, waterflooding is by far the most widely used (Craig 1971; Lake et al., 1992). In spite of its many appealing characteristics, the presence of heterogeneity such as high permeability streaks might yield unfavorable results, causing premature breakthrough, poor sweep and consequently reduce oil production and recovery (Sudaryanto and Yortsos 2001; Brouwer and Jansen 2004; Alhuthali et al., 2007). Various methods have been suggested to mitigate this problem. Among these is smart well completion where the production or the injection section is divided into several intervals (Arenas and Dolle 2003; Glandt 2005). The flow rate at each interval can be independently controlled by inflow control valves (ICVs), making it possible to manage the flood front in highly heterogeneous reservoirs.
Summary Waterflood optimization by means of rate control is receiving considerable attention because of increasing deployments of smart well completions and i-field technology. The use of inflow control valves (ICVs) allows us to optimize the production/injection rates of various segments along the wellbore, thereby maximizing sweep efficiency and delaying water breakthrough. Field-scale rate-optimization problems, however, involve highly complex reservoir models, production and facility constraints, and a large number of unknowns. In this paper, we propose an approach that is computationally efficient and suitable for large field cases. It is based on our previous work (Alhuthali et al. 2007, 2008), which relies on equalizing arrival time of the waterfront at all producers to maximize the sweep efficiency. We use streamlines to efficiently and analytically compute the sensitivity of the arrival times with respect to well rates. We also account for geologic uncertainty by means of a stochastic optimization framework using multiple realizations. Analytical forms for gradients and Hessian of the objective functions are derived, making our optimization computationally efficient for large-scale applications. Finally, optimization is performed under operational and facility constraints using a sequential quadratic programming approach. We demonstrate our approach using two field-scale examples. The first is a synthetic example called "Brugge" field, a benchmark case based on a North Sea Brent-type field. The production optimization of this field is carried out as part of a closed-loop process where the production history is matched prior to the production optimization. The production optimization is performed over multiple realizations for 20 years and involves 30 wells equipped with three ICVs per well. The second example is a super-giant Middle Eastern field that has more than 50 years of historical oil production. The optimization is performed for 20 years on a portion of this field that contains nearly 300 wells consisting of conventional vertical and horizontal wells and smart horizontal wells. In both examples, multiple field-related constraints are imposed, such as the maximum well injection and production rates, the maximum allowable drawdown, restriction on high-water-cut wells, and voidage replacement for pressure maintenance. The results clearly demonstrate the viability of our approach and the benefits of optimal rate control, with a considerable increase in cumulative oil production and a substantial decrease in the associated water production.
Streamline and streamtube methods have been used in fluid flow computations for many years. Early applications for hydrocarbon reservoir simulation were first reported by Fay and Pratts in the 1950s. Streamline-based flow simulation has made significant advances in the last 15 years. Today's simulators are fully three-dimensional and fully compressible and they account for gravity as well as complex well controls. Most recent advances also allow for compositional and thermal displacements. In this paper, we present a comprehensive review of the evolution and advancement of streamline simulation technology. This paper offers a general overview of most of the material available in the literature on the subject. This work includes the review of more than 200 technical papers and gives a chronological advancement of streamline simulation technology from 1996 to 2011. Firstly, three major areas are identified. These are development of streamline simulators, enhancements to current streamline simulators and applications. In view of the fact that this state of-the-art technology has been employed for a wide range of applications, we defined three major application areas that symbolize the relevance and validity of streamline simulation in addressing reservoir engineering concerns. These are history matching, reservoir management and upscaling, ranking and characterization of fine-grid geological models. Streamline simulation has undergone several phases within its short stretch in the petroleum industry. Initially, the main focus was on the speed advantage and less on fluid flow physics. Next, the focus was shifted to extend its applicability to more complex issues such as compositional and thermal simulations, which require the inclusion of more physics, and potentially reducing the advantage of computational time. Recently, the focus has shifted towards the application of streamline technologies to areas where it can complement finite difference simulation such as revealing important information about drainage areas, flood optimization and improvement of sweep efficiency, quantifying uncertainties, etc.
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