%0 Journal Article
%T 杭锦旗地区X区块盒一段孔隙度在严重扩径和非严重扩径条件下的校正方法研究<br>Paper Study on the Correction Method of Porosity of the First Member of the First Block of the X Block in Hangjinqi Area under Severe and Non-Serious Diameter Expansion
%A 刘锋
%A 杨斌
%A 张鹏
%A 张智南
%J Advances in Geosciences
%P 33-43
%@ 2163-3975
%D 2021
%I Hans Publishing
%R 10.12677/AG.2021.111004
%X 研究区目前主要存在的问题是已有的储层参数解释精度不高，传统的使用单孔隙度曲线或者多孔隙曲线多元回归法计算的储层孔隙度无法满足需求，导致气水识别困难。针对研究区孔隙度方面，首先研究了本研究区大量存在的扩径现象对孔隙度测井曲线的影响。在此基础上将研究区储层扩径现象分为了非严重扩径和严重扩径两个部分。发现非严重扩径条件下储层声波几乎不受扩径影响，密度测井曲线受影响极其微弱；严重扩径下声波同样受影响微弱，密度则受影响严重。对此，在非严重扩径条件下使用神经网络法计算了孔隙度；在严重扩径条件下使用声波和电阻率建立多元回归计算孔隙度。并使用曲线重叠法的交会图法等分析了误差，发现效果远好于先前计算的孔隙度，能为后续的流体识别提供更可靠的孔隙度参数。<br />
The main problem in the study area is that the interpretation accuracy of existing reservoir parameters is not high. The traditional reservoir porosity calculated by the single-porosity curve or the multi-porosity curve multiple regression method cannot meet the demand, which makes it difficult to identify gas and water. Regarding the porosity of the study area, firstly, the influence of the large diameter expansion phenomenon in the study area on the porosity logging curve is studied. On this basis, the reservoir diameter expansion in the study area is divided into two parts: non-serious diameter expansion and severe diameter expansion. It is found that under the condition of non-severe diameter expansion, the reservoir acoustic wave is almost unaffected by diameter expansion, and the density logging curve is extremely weakly affected; under severe diameter expansion, the acoustic wave is also slightly affected, and the density is seriously affected. In this regard, the neural network method is used to calculate the porosity under the condition of non-severe diameter expansion; the multiple regression calculation of the porosity is established using acoustic waves and resistivity under the condition of severe diameter expansion. The error was analyzed using the intersection graph method of the curve overlap method, etc., and found that the effect was much better than the previously calculated porosity, which can provide more reliable porosity parameters for subsequent fluid identification.
%K 扩径，神经网络，孔隙度，测井解释，非线性<br>Diameter Expansion
%K Neural Network
%K Porosity
%K Log Interpretation
%K Nonlinear
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=40084