Asphaltene precipitation becomes a serious problem especially when it causes plugging of the formation, wellbore, or production facilities, which will significantly affect the productivity and final recovery of the area. This paper presents an overview of asphaltene properties, structure, and its impact on oil production operations. It also specifically, discusses different available modeling approaches used to analyze and predict asphaltene precipitation, flocculation, and deposition in oil reservoirs. We will review asphaltene in crude oil systems: asphaltene properties and their impact on oil production, including the effects of pressure, temperature, and composition.

To overcome horizontal well reservoir and production challenges, inflow control devices are adopted as the optimal completion method in many parts of the world. The main industry drivers for adopting inflow control device completions include balancing inflow along the well, delaying water and gas breakthrough, controlling water and sand production, and providing a cost-effective reservoir completion solution to meet the majority of the reservoir challenges. In offshore Saudi Arabia, inflow control devices have been used successfully in fulfilling these objectives. The downhole completion efficiency is evaluated using multiphase production logging tools. Many completion accessories can be run with inflow control device completion strings, these include isolation packers, liner hangers, setting tools, end-of-completion string plugs and valves. All of these accessories may affect the well's production performance. New challenges arise when one of these accessories, such as the end-of-completion valve or isolating packers malfunction and develop a leak; thus, affecting the inflow control device completion performance. A major failure can result from such a malfunction, including fluid drainage from the toe section, sand production, early water breakthrough and water coning. This paper presents two field examples in which multiphase production logging profiles of horizontal wells are used to evaluate the inflow control device completion performance and detect any completion-accessory malfunction. Multiple sensitivity simulation runs — supported by multiphase production logging results — facilitate optimizing the inflow control device completed well performance. This integrated approach is used to recommend solutions or remedial actions to overcome sub-optimal well completion performance. Also, the approach provides a comprehensive understanding of the reservoir results that should be considered in planning future completion and workover strategies. Introduction Hydrocarbon demands continue to drive production, particularly among assets in offshore reservoirs. Horizontal wells produce fluids by nonuniform inflow due to reservoir permeability/pressure variations along laterals, frictional effects along the wellbore, and sand production. The nonuniform inflow promotes early water/gas breakthrough. These wells are completed using inflow control devices (ICDs) with screens to optimize the well performance and overcome the main field challenges, namely formation damage during drilling by better cleanup processes, pressure loss due to heel-to-toe friction, and sand control.

To overcome horizontal well reservoir and production challenges, inflow control devices are adopted as the optimal completion method in many parts of the world. The main industry drivers for adopting inflow control device completions include balancing inflow along the well, delaying water and gas breakthrough, controlling water and sand production, and providing a cost-effective reservoir completion solution to meet the majority of the reservoir challenges. In offshore Saudi Arabia, inflow control devices have been used successfully in fulfilling these objectives. The downhole completion efficiency is evaluated using multiphase production logging tools. Many completion accessories can be run with inflow control device completion strings, these include isolation packers, liner hangers, setting tools, end-of-completion string plugs and valves. All of these accessories may affect the well's production performance. New challenges arise when one of these accessories, such as the end-of-completion valve or isolating packers malfunction and develop a leak; thus, affecting the inflow control device completion performance. A major failure can result from such a malfunction, including fluid drainage from the toe section, sand production, early water breakthrough and water coning. This paper presents two field examples in which multiphase production logging profiles of horizontal wells are used to evaluate the inflow control device completion performance and detect any completion-accessory malfunction. Multiple sensitivity simulation runs — supported by multiphase production logging results — facilitate optimizing the inflow control device completed well performance. This integrated approach is used to recommend solutions or remedial actions to overcome suboptimal well completion performance. Also, the approach provides a comprehensive understanding of the reservoir results that should be considered in planning future completion and workover strategies.

To perform an optimization study for a green field (newly discovered field), one must collect the information from different parts of the field and integrate these data as accurately as possible in order to construct the reservoir image. Once the image, or alternate images, are constructed, reservoir simulation allows prediction of dynamic performance of the reservoir. As field development progresses, more information becomes available, enabling us to continually update and, if needed, correct the reservoir description. The simulator can then be used to perform a variety of exercises or scenarios, with the goal of optimizing field development and operation strategies.We are often confronted with important questions related to the most efficient well spacing and location, the optimum number of wells needed, the size of the production facility needed, the optimum production strategies, the location of the external boundaries, the intrinsic reservoir properties, the predominant recovery mechanism, the best time and location to employ infill drilling and the best time and type of the improved recovery technique we should implement. These are some of the critical questions we may need to answer.A reservoir simulation study is the only practical means by which we can design and run tests to address these questions in sufficient detail. From this perspective, reservoir simulation is a powerful screening tool. The magnitude, time and complexity of a reservoir simulation problem depends in part on the available computational environment. For instance, simple material balance calculations are now routinely performed on desktop personal computers, while running a field-scale three-dimensional simulator may call for the use of a supercomputer and may take many days to finish. We must also take into account the storage requirements and limitations, CPU time demand and the general architecture of the machine. The problem arises when there is a large amount of data available with a study objective that requires running several scenarios incorporating millions of grid cells. This will limit the applicability of reservoir simulation as it will be computationally very inefficient. For example, determining the optimum well locations in a field that will result in the most efficient production rate scenario requires a large number of simulation runs which can make it very inefficient. This is because one will have to consider multiple well scenarios in multiple realizations. SPE-185718-MSThe main purpose of this paper is to use a novel methodology known as the Fast Marching Method (FMM) to find the optimum well locations in a green oil field that will result in the most efficient production rate scenario. FMM tracks a pressure front from a well and can approximately determine the drainage radius. For single phase fluid, we can determine the rate profile for a well. By quickly generating rate profiles for multiple scenarios, we can rank multiple realizations with multiple well scenarios in matter of minutes.