Proactive Optimization of Oil Recovery in Multilateral Wells Using Real Time Production Data

Abstract

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...

Assisted optimization method for comparison between conventional and intelligent producers considering uncertainties

“…An important advance was achieved by employing evolutionary computation methods such as genetic algorithms (Alghareeb et al, 2009;Almeida et al, 2010), an efficient method of global optimization in sweeping for the optimal solution, or very close to it, in a feasible computational time but still dependent on the complexity of the reservoir model, the number of variables used (number of valves), and the computational power available. Despite being an efficient global optimization method in scanning for solutions, genetic algorithms are not efficient in finding the local maximum.…”

Proactive Optimization of Intelligent-Well Production Using Stochastic Gradient-Based Algorithms

“…These approaches can be classified as chasing either short-term objectives, known as reactive optimiza-tion (Grebenkin and Davies 2012), or long-term objectives, known as proactive optimization (Alghareeb et al 2009;Almeida et al 2010;Haghighat Sefat et al 2013). These scenarios must also respect the constraints defined by the equipment installed in the field or imposed by the wells, reservoir, or field-management policies.…”

“…However, the number of available techniques for optimal control of I-wells is limited (Sarma et al 2006;Alghareeb et al 2009;Almeida et al 2010;Grebenkin and Davies 2012). However, the number of available techniques for optimal control of I-wells is limited (Sarma et al 2006;Alghareeb et al 2009;Almeida et al 2010;Grebenkin and Davies 2012).…”

Using Distributed Acoustic Sensors to Optimize Production in Intelligent Wells

“…He concluded that, for problems considered, General Pattern Search (GPS) with penalty functions perform the best followed by the combined GA and GPS algorithm. Alghareeb et al (2009) applied a binary Genetic Algorithm (bGA) to optimize ICV settings in a trilateral well in a synthetic model representing a fluvial channel reservoir as well as a real field in the Middle East. His real field optimization results showed that water-free production from the well was extended by two years when compared to the current setting.…”