Synthetic model for evaluating CO₂ flooding in tight oil reservoir

ABSTRACT

With the increasing energy consumption and the exhaustion of conventional oil reservoir, CO₂ flooding is becoming one of the key technologies to improve the oil production of tight reservoirs. Not only oil production of CO₂ flooding but also its main influencing parameters and quantitative evaluation have attracted people’s attention in recent years. The experimental and numerical techniques are the main approach to evaluate the production and influencing parameters. However, it is expensive and costs a lot of time to use those approaches. To solve the above problems, in this paper, a comprehensive integrated hierarchy and correlation model is initially established to evaluate the production of CO₂ flooding and confirm the main influencing parameters. Specifically speaking, the Analytic Hierarchy Process (AHP) and Grey Relationship Analysis (GRA) are used to evaluate the main influencing parameters of oil production for the CO₂ flooding process in tight oil reservoirs, respectively. Subsequently, the Rank-Sum Ratio (RSR) is adopted to combine the Analytic Hierarchy Process with Grey Relationship Analysis to acquire the comprehensive weight and ensure the main influencing parameters. The results show that the comprehensive weight of permeability, variation coefficient, sand injection, and reservoir thickness is 0.1157, 0.0937, 0.0914, and 0.0895 and those parameters play a extremely important role in the production of CO₂ flooding. The model is applied to eight production wells and the rank–sum ratio is 2.3 to 6.6. The arrange regular of comprehensive results evaluated by the rank–sum ratio has a very consistent relationship with that of production. It is convenient and accurate to evaluate the production-related parameters by using the comprehensive integrated-hierarchy model, and this study provides a functional method to analyze the production performance.

KEYWORDS:

• CO₂ flooding
• tight oil reservoirs
• quantitative evaluation
• integrated-hierarchy and correlation model
• major parameters analysis

Acknowledgments

The authors would like to acknowledge the funding by the project (51974329) sponsored by the National Natural Science Foundation of China. In addition, Reallythank Yanan Xue for understanding, support and kindness, Xiaolong Chai can accomplish this manuscript. Her supports provide me with great motivation.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Nomenclature

$X_i^{\prime }$ - i parameter value of each sequence after normalization;

$X_i$ - i parameter value of each sequence;

$X_{max}$ - the maximum value of parameters in each sequence;

$X_{min}$ - the minimum value of parameters in each sequence;

$\rho$- the resolution, normally is 0.5;

$\xi$ - the correlation coefficient;

$Y$ - the reference sequence after normalization;

$r_i$ - the correlation degree of i parameter;

$N$ - the total number of each parameter value;

${\omega }_{Gi}$ - the weight of GRA for each parameter;

CI - the indicator of the degree of inconsistency;

CR - the random consistency ratio;

RI - the average random consistency ratio;

${\omega }_j$ - the comprehensive weight of each parameter;

${\omega }_{Ai}$ - the weight of AHP for each parameter;

a - distributive coefficient of weight for GRA;

b - distributive coefficient of weight for AHP;

R - the rank matrix;

RSR_i - rank sum ratio of i parameter.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [51974329].