Abstract

Reservoir evaluation is one of important contents in the reservoir study. This paper has adopted cluster analysis method to optimize evaluation parameters of low permeability reservoir and the analytic hierarchy process（AHP） to determine the weight coefficient. Moreover， this paper has made the reservoir comprehensive quantitative evaluation for low permeability reservoir of Chaoyanggou oil field. According to the cumulative probability curve， the evaluation results can be divided into three categories， which conform to the low permeability reservoir characteristics of Chaoyanggou oilfield. The method of reservoir comprehensive quantitative evaluation has solved the problems of single-factor classification evaluation that the evaluation result is not unique and provided favorable basis for low permeability reservoir evaluation of Chaoyanggou oilfield.

Key words： Reservoir evaluation； Weight coefficient； Low permeability reservoir； Analytic hierarchy； Cluster analysis

INTRODUCTION

With the deepening development of the Chaoyanggou oilfield， most of the blocks have entered a development adjustment stage. Because the influential factors of reservoir geological characteristics are complex and multifaceted[1]， only making the reservoir comprehensive evaluation can improve the success rate of drilling， which has provided reliable geological basis for the formulation of development plan， development dynamic analysis， reservoir engineering study， reservoir numerical simulation and development plan adjustment.

1. THE GEOLOGICAL CHARACTERISTICS OF THE STUDY AREA

Chaoyanggou oilfield is a typical low porosity and low permeability oilfield， in which the seepage ability is poor[2-3] and the heterogeneity is serious. The Fuyang reservoir in Chaoyanggou oilfield mainly develops river facies. The lithology of Fuyang reservoir is mainly mudstone and sandstone. The single layer sandstone thickness is 3.8 m on average. The sand body is mainly given with narrow stripes and intermittent strip channel. The width of the sand body is from 300 to 600 meter. Reservoir porosity is about 16% on average， and the air porosity is generally 10.7×10-3 μm2. The pore types include primary interranular pore， remaining intergranular slot form intergranular pore， dissolution and intergranular hole. The intergranular pore accounting for 64% is the main reservoir space in Fuyang reservoir. The corrosion hole and the intergranular hole are secondary reservoir space in Fuyang reservoir and have few holes. Fuyang reservoir has small pore throat[4-6] and big pore throat ratio. In Fuyu reservoir， the average pore radius is 1.28 mm and the average crude oil viscosity is 9.4 mPa.s. In Yangdachengzi reservoir， the average crude oil viscosity is 18.0 mPa.s and the crude oil density is 0.834 t/m3. Compared with the Fuyu oil layer， the formation oil viscosity of Yangdachengzi reservoir increased obviously.

In Table 1， the parameter 1， 2， 3， 4， 5， 6， 7 and 8 respectively represent permeability， porosity， effective thickness， abundant reserve， starting pressure gradient， movable fluid saturation， the average pore radius and mobility. In order to compare and optimize parameters， we mapped the clustering tree of geologic parameter in Fuyang reservoir.

Seen from Figure 1， the correlation coefficient between starting pressure gradient and the effective thickness is the largest and the correlation coefficient between permeability and movable fluid saturation is the second largest. As well known， the effect of starting pressure gradient is greater than the effect of effective thickness. Thus， the effective thickness parameter is eliminated. Similarly， having less effect than permeability variable， movable fluid saturation parameter is eliminated. Therefore， the remaining six parameters are the permeability， porosity， reserve abundance， starting pressure gradient， the average pore radius and mobility.

These six optimized parameters not only have macro characteristic parameters but also have micro characteristic parameters. In these six optimized parameters， permeability， porosity and reserve abundance can reflect the reservoir physical property， the average pore radius can reflect the characteristics of pore structure， and starting pressure gradient only exists in low permeability reservoir. It can be seen that the selected parameters can reflect all the characteristics of low permeability reservoir.

3. THE SINGLE-FACTOR CLASSIFICATION EVALUATION OF LOW PERMEABILITY RESERVOIR

According to cluster analysis method， six parameters， including the permeability， porosity， reserve abundance， starting pressure gradient， the average pore radius and mobility， were selected to take part in the evaluation. In the process of determining the classification boundary， the classification function of the cumulative probability curve was used.

Seen from the single-factor classification results， the same block had different classification results by using different parameters. Therefore， the single-factor classification can not fully reflect the nature of the block. Meantime， it is easy to appear the classification results are not unique and the evaluation results are often not very accurate. Especially， when too many parameters are involved， using single-factor classification evaluation can lead to contradictory classification results.

Thus， we need to choose some parameters which can represent the reservoir characteristics to make a comprehensive classification evaluation for the reservoir. 4. MULTI-FACTOR COMPREHENSIVE EVALUATION METHOD OF LOW PERMEABILITY RESERVOIR

4.1 Fundamental Principles

Based on the reservoir evaluation parameter selection， the reservoir comprehensive quantitative evaluation makes a comprehensive analysis for the multiple influence factors of the reservoir and gets a comprehensive evaluation index. According to this evaluation index， reservoir was classified.

In combination with the characteristics of low permeability reservoir， the hierarchical analysis method was applied to evaluate Fuyang reservoir of Chaoyanggou oilfield. The evaluation results were listed in the following table.

The evaluation indexes were classified according to cumulative probability curves.

CONCLUSION

（1） Taking into account the correlation between the parameters， the cluster analysis can optimize the parameters which can reflect all the characteristics of the reservoir and use fewer parameters to comprehensively reflect the characteristics of the reservoir.

（2） By using different parameters， the same block had different classification results. Therefore， the single-factor classification can not fully reflect the nature of the block. Meantime， it is easy to appear the classification results are not unique and the evaluation results are often not very accurate. Especially， when too many parameters are involved， using single-factor classification evaluation can lead to contradictory classification results.

（3） The comprehensive quantitative evaluation method is adopted to make a comprehensive classification evaluation for the Fuyang reservoir of Chaoyanggou oil field and the evaluation results can be divided into three categories. In Class Ⅰ， comprehensive evaluation index of the AHP is greater than 0.7； In Class Ⅱ， comprehensive evaluation index of the AHP is between 0.52 and 0.7； In Class Ⅲ， comprehensive evaluation index of the AHP is lower than 0.52. Evaluation results conform to the low permeability reservoir characteristics of Chaoyanggou oil field.

（4） The comprehensive quantitative evaluation method has solved the problems of single-factor classification evaluation that the evaluation result is not unique and provided favorable basis for low permeability reservoir evaluation of Chaoyanggou oilfield.

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