Interval Control Valves (ICVs) are important tools for managing production challenges such as localized water or gas breakthrough, particularly in multilateral wells and horizontal wells with open-hole completions. This paper describes an automated workflow designed exclusively for wells with intelligent completions that use ICVs and real-time sensors for both down-hole and surface data. The workflow was designed to provide right-time production monitoring, well diagnostics, and ICV optimization for those intelligent wells equipped with ICV and multiple downhole pressure and temperature sensors. The automated workflow performs several tasks: collect and condition production data, provide local history matching, generate several optimization iterations to estimate the best valve setting, and use numerical simulation to generate a number of scenarios where the ICV positions are changed over simulation time. To perform these tasks, the workflow models fluid mechanics between the ICV and a heterogeneous reservoir using next generation software such as steady-state hydraulic applications and a numerical simulator that accounts for the ICV settings. Tracking and visualization of streamline trajectories was used to add value of optimization process particularly the calculation of well distribution factors. The horizontal well in this study has 5 ICVs positioned at different locations. The reservoir model is dual porosity-dual permeability. Local Grid Refinement (LGR) is used to model the horizontal section. The reservoir is subjected to a waterflooding process with more than 10 years of history. Two injectors are associated with a horizontal producer. The workflow operates in two ways: reactive mode and proactive mode. Reactive mode is activated when water cut increases and oil rate or flowing bottomhole pressure (BHP) decreases, at which point the workflow performs local history matching to update the well performance model and optimize the settings of the 5 ICVs to obtain the minimum water cut and maximum oil rate at given constraints. Proactive mode occurs every month or quarter when the numerical simulation runs hundreds of scenarios to obtain the best combination of ICV settings to maximize oil recovery and minimize water injection and production. The workflows described in this paper demonstrate the potential to manage ICV settings to substantially increase oil production while managing water influx.
Reservoir simulation is becoming increasingly complex because of more advanced wells in the fields, including intelligent and multilateral wells. Advanced completions are also evolving to increase recovery efficiencies. This increasing complexity presents two difficulties, which include the design of advanced completions within reservoir simulators and increased simulation runtime. To describe a well in a reservoir model, a reservoir engineer typically defines a network of hundreds of nodes using keywords and specifies properties for each node. This is a cumbersome and error-prone process. Additionally, detailed well models can slow down reservoir simulation and often cause poor convergence. A new iterative round-trip approach has been implemented, in which an engineer imports an initial reservoir model into a nodal analysis simulator that models flow from the reservoir through complex completions to the wellhead. The simulator accurately models well production in steady state and designs completion strings in detail. After the design is complete, the nodal analysis simulator converts the well model into reservoir simulator keywords that are imported into full-scale simulations for transient analysis. Using this method, reservoir simulators can also model multiple annuli, which was not feasible previously. Highly detailed well models of several thousand nodes that accurately describe completion strings can be generated automatically. Reservoir engineers typically do not possess complex knowledge of well design because this is usually performed by completion/production engineers, who seldom have access to a reservoir simulator. Consequently, they have a limited ability to experiment with different well designs. This paper presents an approach that helps facilitate reservoir and production engineer collaboration, thus helping enable fine-tuning of final completion designs to maximize production, prevent early water/gas breakthrough, and increase overall recovery. This paper describes the application of the new process in an openhole well and presents various completion designs of the same well.
The frac-pack completion has become one of the prominent completion types for deepwater formations throughout the world. The vast majority of frac-pack stimulation treatments are pumped through complex sand control tools in wells with large gravel pack assemblies. The forces created by these stimulation treatments have increased as treatment size, water depth, and total depth have increased offshore. To plan appropriately for these deepwater completions, engineers expend a significant amount of effort to ensure that the tools are robust enough to accommodate the treatment demands. This planning and simulation can be a very time consuming process; moreover, it may evaluate only a limited range of potential frac-pack scenarios. To reduce time and limit uncertainty, a novel tubing movement workflow has been developed. A commercially available workflow software suite was used to connect a commercially available fracture design component and a commercially available casing and tubing simulator. This workflow uses formation parameters and a wellbore input file to process hundreds or thousands of stimulation injection schedules to determine tubing movement, pipe forces, and weight transmitted downhole. The entire process is completed within a significantly shortened time span, depending on the computer speed and the number of iterations desired. The results of the workflow enable the determination of optimal workstring and service tool characteristics. A sample case study is included.
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