The effectiveness of this methodology was confirmed by
successfully matching primary and secondary production his-
tories using a flow unit-based reservoir model of the Norcan
East field without permeability modifications. The methodol-
ogies discussed should prove useful for robust flow unit char-
acterization of different kinds of reservoirs.
INTRODUCTION
Flow units, which do not always coincide with geologic litho-
facies, subdivide reservoirs into zones (layers) based on hydrau-
lic flow properties and are best suited to determine reservoir
layering for flow-simulation studies. Prevalent techniques for
flow unit zonation include application of Lorenz plots and use
of flow zone indicators (FZI). Both these techniques require
knowledge of porosity and permeability distribution on a foot-
by-foot basis. Wire-line logs can provide good estimates of po-
rosity and help define net pay; however, permeability profiles
ultimately can only be generated from core measurements.
Estimation of permeability from wire-line logs in uncored wells
commonly consists primarily of the use of (1) log porosity and
a single permeability-porosity transform, (2) log porosity and a
series of lithofacies- or log-facies-specific permeability-porosity
transforms possibly incorporating other log-measured prop-
erties (e.g., irreducible water saturation), (3) multivariate pre-
diction of permeability from various log response curves, or
(4) nonlinear permeability prediction methods (e.g., neural
network analysis). The robustness of the permeability estima-
tion is generally determined by blind-test correlation of esti-
mated and measured permeability over specific cored inter-
val(s). Petrophysical properties, like geologic parameters, vary
spatially, and thus, acceptable performance in blind test(s) is
not a guarantee that the estimated permeabilities are accurate
in wells located significant distances from the cored wells in
the field. Thus, flow unit zonation in most of the wells in a field
is commonly based on nonvalidated permeability values.
This case study in the Norcan East field (Clark County,
Kansas), which produces from an upper Atokan fine- to very
fine-grained sandstone, does not investigate the best permeabil-
ity estimation method at uncored wells. Instead, it examines
and illustrates a methodology for validation and refinement of
the estimated permeability once a particular method has been
applied. The proposed validation process is an iterative tech-
nique that minimizes the difference between log-derived water
saturation (Swlog) and saturation determined from capillary
pressure (SwPc) at every 0.5-ft (0.15-m) interval in net pay at
each well. Such validated permeability distribution
served as the basis for flow unit delineation in the
Norcan East reservoir using the stratigraphic modi-
fied Lorenz (SML) method, which plots the ratio of
percent flow capacity and percent storage capacity
in stratigraphic sequence. Upon simulation, primary
and secondary field performances were matched
without any permeability modifications, thus con-
firming the robustness of the flow unit model de-
veloped using validated permeability at uncored
but logged wells.
摘要
在储层模拟中，把储集层层段划分为流动单元是一种细分产油层的有效方法。常用的流动单元识别技术需要以逐步估算可信的渗透率值为基础。大部分井没有取心，因而，大量的文献论述了各种不同的方法包括类比、转换、预测等方法来得出剖面中产油层得渗透率。然而，为了建立可靠的流动单元，未取心井段渗透率预测的有效性是基础，如果需要，还要更精确。
这项研究列出了使用网格化为基础的渗透率验证技术，并描述了堪萨斯州克拉克县Norcan东油田钻井中的流体单元特征，产油层为Atokan时期沉积的细粒到极细粒石英砂岩，为半咸水沉积环境下, 小的深切谷填充系统内潮水控制形成的潮滩、潮汐水道、滩海沙坝和河口沙坝的沉积产物，这个方法体系可以识别油田自由水界面，并可以通过迭代方法验证和精确到0.15米为间隔的预测渗透率值，迭代所用的含水饱和度值通过电测数据和毛细管压力公式计算细粒-极细粒并伴有粘土成岩作用和粉砂岩或页岩纹理的砂岩层得到。
该方法的有效性在Norcan East油田被证实，在没有进行渗透率修正的情况下，利用以流通单元为基础的储层模型，初次和二次生产史与预测向匹配。可方法应该也适用于其它各种不同的储层类型。
引言
流体单元并不总是与地质岩相相一致，而是根据水动力特征划分储集岩为带（层），并且最适合为流体模拟实验确定储集岩分层。流体单元层带化的常规技术包括洛伦兹曲线的应用（Lorenz plots）和使用流体带指数（FZI）。 这两种方法都要求孔隙性和渗透性的知识。电缆测井可以提供准确的孔隙度预测值并且可以帮助确定有效产油层；但是渗透率资料最后只能由岩心测量方法取得。根据未取心井中的电缆测井来预测渗透率，一般有以下几种组成： (1) 孔隙度测井和单井孔隙度—渗透率转换, (2) 孔隙度测井和一系列岩相或者特殊的孔隙度渗透率转换并综合其他方法所得资料(例如：残余水饱和度), (3) 不同测井相应曲线中的渗透率的多相预测(4) 非线性的渗透率预测方法 (例如：神经网络分析法)。渗透率估算的准确度一般用估算盲试相关对比来鉴定，并且在特殊的岩心间隔（S）中测量渗透率。岩石物理性质相其他地层参数一样有空间变化，因此在盲试（S）中其可接受的性能在油田已取心并有意义的井间距的井的渗透率估算且精确的，但并不完全准确。因此，大部分井的流体单元的划分是基于不准确的渗透率有效值的。
本研究在Norcan东部油田 (堪萨斯，克拉克村)Atokan 上部细粒至极细粒砂岩，并不是在未取心井试验的最好的渗透率方法。相反，它曾是用于检测和说明估算渗透率值真实性和精度的一种被应用过的特殊方法。这个方法是用迭代算法将由测井资料导出含水饱和度 (Swlog)和由毛细管压力计算出的每0.15米间隔的生产层中的每口井饱和度(SwPc) 的差值缩减到最小。在Norcan东部油藏使用洛伦兹地层校正（SML）方法时，这些证实了的渗透率的分布是描绘流体单元的基础，表示出层状地层的流动能力百分率和存储能力百分率。模拟实验中，第一次开采和第二次开采产出量不用经过渗透率校正也是相一致的。因此在取心的测井中确定稳定的流体单元模型需要一个准确的渗透率。
