Injection Scheduling Design for Reduced Order Waterflood Modeling

Abstract

In this paper we use the concept of experimental design from the System Identification literature and introduce a novel methodology for more accurate characterization and monitoring of waterfloods using linear models. To improve the predictability of the low order linear models they must be trained with sufficiently informative data. To achieve this goal, the input (here injection rate) must contain sufficient variations (without exceeding the technical and operational limits) so that all the relevant dynamics of the system are excited and can be seen in the output (here production rate). In sum, a successful injection scheduling design boils down to introducing predetermined variations on top of the existing injection rates. We will discuss how to design these variations, specifically their amplitude, sampling time, experiment length, frequency of variations and signal type, using priori knowledge of the reservoir dynamics and the production constraints. Our proposed methodology can be applied to currently available linear modeling techniques such as CRM, FIR, subspace models, etc. This study shows that the prediction error of a model that has been trained with a proper injection scheduling design, in a multi injector multi producer oil field, can be reduced significantly regardless of the modeling technique.

Oil-Rate Forecast by Inferring Fractional-Flow Models From Field Data With Koval Method Combined With the Capacitance/Resistance Model

“…Eqs. B-1 and B-2 are the weak forms of the material balance for two-phase immiscible flow of water and oil (Walsh and Lake 2003). To obtain the total-fluid material balance, we expand Eqs.…”

“…For a mature waterflood, the PI is a function of rock properties, fluid properties. and well properties but not of pressure or time (Walsh and Lake 2003). Therefore, it is reasonable to consider the PI as a constant if there is no dramatic change in reservoir/ well conditions.…”

A State-of-the-Art Literature Review on Capacitance Resistance Models for Reservoir Characterization and Performance Forecasting

“…As studied by Tafti et al [65], the identification of the CRM parameters and their underlying uncertainty are intrinsically related to:…”

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A novel interwell connectivity evaluation method for waterflooding reservoir using oil-water production performance

“…、电容模型 [5~11] 、多井生产指数 [12] 、流动网络 模型 [13] 和井间数值模拟模型 [14,15] 等. 其中, 电容模型是 基于水电相似和物质平衡原理得出的注入速度时滞 模型, 它通过连通系数、时滞常数、产液指数等表征 油藏产液数据变化, 理论基础坚实、易于编程且表征 参数物理意义明确, 可较好适应于油藏管理和实时优 化调整, 已广泛用于弹性驱 [16] 、水驱 [17,18] 、气驱 [16,19] 及CO2地质埋存 [20] 等领域.…”