Smart Waterflooding Tight Fractured Reservoirs Using Inflow Control Valves

Arenas, Eliana; Dolle, Norbert

doi:10.2118/84193-ms

Cited by 16 publications

(1 citation statement)

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“…Various methods have been suggested to mitigate this problem. Among these is smart well completions where the production or the injection section is divided into several intervals (Arenas and Dolle 2003;Glandt 2003;Hussain et al 2005). The flow rate at each interval can be independently controlled by inflow control valves (ICVs); hence, making it possible to control flow rates across the high permeability streaks.…”

Section: Introductionmentioning

confidence: 99%

Optimal Waterflood Management Under Geologic Uncertainty Using Rate Control: Theory and Field Applications

Alhuthali

2009

SPE Annual Technical Conference and Exhibition

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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. It is well recognized that field scale rate optimization problems are difficult because they often involve highly complex reservoir models, production and facilities related constraints and a large number of unknowns. Some aspects of the optimization problem have been studied before using mainly optimal control theory.However, the applications to-date have been limited to rather small problems because of the computation time and the complexities associated with the formulation and solution of adjoint equations. Field-scale rate optimization for maximizing waterflood sweep efficiency under realistic field conditions has still remained largely unexplored.We propose a practical and efficient approach for computing optimal injection and production rates and thereby manage the waterflood front to maximize sweep efficiency and delay the arrival time to minimize water cycling. Our work relies on equalizing the arrival times of the waterfront at all producers within selected sub-regions of a water flood project. The arrival time optimization has favorable quasi-linear properties and the optimization proceeds smoothly even if our initial conditions are far from the solution. 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 iv 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 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 several 2D synthetic examples for validation purposes and 3D field applications.

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Section: Introductionmentioning

confidence: 99%

Optimal Waterflood Management Under Geologic Uncertainty Using Rate Control: Theory and Field Applications

Alhuthali

2009

SPE Annual Technical Conference and Exhibition

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A stochastic approach for evaluating where On/Off zonal production control is efficient

Grebenkin

Muradov

Davies

2015

Journal of Petroleum Science and Engineering

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Prediction of Injection-Fluid Distributions for Multiple Zones—Intelligent Injection System

Sun

Konopczynski²

2006

SPE Annual Technical Conference and Exhibition

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Injection fluid distributions present a challenge for the applications of multi-zones intelligent well injection systems. This paper presents two approaches to fulfill this need. The first approach is to apply the available downhole real-time data to calculate fluid distribution based on the existing single phase flow modeling and flow coefficient (Cv) test data of down-hole Interval Control Valve (ICV). For this method, a fluid distribution model for a multi-zone intelligent injection system is derived, and an example of the application of the first method is presented. For the second approach method, zonal reservoir pressure/temperature and injectivity data are considered in the fluid distribution calculation. This method is based on a balanced pressure-system approach. In this paper, each of these fluid distribution prediction methods considers the choke setting of the multi-position ICV, the completion string's geometric sizes, injection fluid characteristics, available wellhead pressure/temperature data, and the reservoir pressure/injectivity data (or measured down-hole real-time data) to do the fluid distribution prediction. To demonstrate these methods, a hypothetical example of a two-zone intelligent water injection case is illustrated in this paper. The example shows how to use the wellhead pressure/temperature data, zonal reservoir pressure/injectivity data (or measured down-hole real-time pressure/temperature data), with completion string's geometry sizes and ICV positions to predict injection fluid distributions through each zonal ICV. An intelligent injection system operation analysis has been presented based the theoretical models and illustrated example. The technical contributions of this paper include:Present mathematical models which apply the available downhole real-time data to calculate fluid distribution.Propose a balanced pressure-system search method which applies the zonal reservoir pressure and injectivity to calculate fluid distribution.Present an intelligent injection system operation analysis, which reveals the key roles of changing ICV position and wellhead pressure in the controlling of injection fluid distributions.Present an effective way to handle down-hole real-time data for intelligent injection system. Introduction The use of intelligent completions to control injected fluid distribution in multi-zone wellbores has increased significantly as operators to optimize reservoir management and maximize hydrocarbon recovery1–3. Predicting and controlling injection fluid distribution through zonal ICVs into each reservoir unit presents a challenge for operators of a multi-zone intelligent injection well. However, comparably, little research has been published in this area. A simple and accurate mathematical tool/method has been developed to estimate the amount of injection fluids to each reservoir unit based on available well-bore, fluid and well strings data, including the setting of the zonal ICVs, production strings geometry size, injection fluid properties, wellhead P/T data and zonal reservoir pressure/injectivity data (or measured down-hole real-time data). Typical Two-Zone Intelligent Injection System and Available Real-Time Data A Typical Two-Zone Intelligent Injection System. Fig.1 is an illustration of a typical two-zone intelligent injection system. Two ICVs are installed to control the injection fluid distribution. In Fig.1, the upper ICV is used to control the injection fluid rate to the upper zone; the lower ICV is used to control the injection fluid rate to the lower zone. The lower zone ICV is shrouded to separate the injection fluid of both zones.

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Smart Waterflooding Tight Fractured Reservoirs Using Inflow Control Valves

Abstract: TX 75083-3836, U.S.A., fax 01-972-952-9435.

Cited by 16 publications

References 1 publication

Optimal Waterflood Management Under Geologic Uncertainty Using Rate Control: Theory and Field Applications

Optimal Waterflood Management Under Geologic Uncertainty Using Rate Control: Theory and Field Applications

A stochastic approach for evaluating where On/Off zonal production control is efficient

Prediction of Injection-Fluid Distributions for Multiple Zones—Intelligent Injection System

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