Mathematical Problems in Engineering

Volume 2015 (2015), Article ID 964204, 5 pages

http://dx.doi.org/10.1155/2015/964204

## Study and Application of Safety Risk Evaluation Model for CO_{2} Geological Storage Based on Uncertainty Measure Theory

^{1}School of Earth Science and Resources, Chang’an University, Xi’an 710054, China^{2}Key Laboratory of Western Mineral Resources and Geological Engineering, Ministry of Education, Chang’an University, Xi’an 710054, China^{3}The 213th Research Institute of China Ordnance Industry, Xi’an 710061, China

Received 23 September 2015; Accepted 29 November 2015

Academic Editor: Hiroyuki Mino

Copyright © 2015 Hujun He et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Analyzing showed that the safety risk evaluation for CO_{2} geological storage had important significance. Aimed at the characteristics of CO_{2} geological storage safety risk evaluation, drawing on previous research results, rank and order models for safety risk evaluation of CO_{2} geological storage were put forward based on information entropy and uncertainty measure theory. In this model, the uncertainty problems in safety risk evaluation of CO_{2} geological storage were solved by qualitative analysis and quantitative analysis, respectively; uncertainty measurement functions for the relevant factors were established based on experimental data; information entropy theory was applied to calculate the index weight of factors; safety risk level was judged based on credible degree recognition criterion and ordered. This model was applied in three typical zones of Erdos and Hetao basins. The results show that uncertainty measure method is objective and reasonable and can be used as a new way to evaluate the safety of CO_{2} geological storage sites in the future.

#### 1. Introduction

The extensive use of coal, oil, natural gas, and other fossil fuels significantly increases the CO_{2} content of the atmosphere. This leads to global warming and poses a serious threat to humans and the sustainable development of our society and economy. CO_{2} emissions have become a major problem. At present, scientists agree that CO_{2} capture and storage is an important and effective strategy for easing climate change. It can also be effectively, comprehensively, and sustainably applied to resource development, energy utilization, economic growth, and ecological environment protection, among others [1, 2].

A geological implementation of CO_{2} storage could safely trap CO_{2} in rock cracks in the deep crust of the earth, but this takes a long time. Furthermore, temperatures and pressure changes inside the earth cause sudden geological events such as volcanoes and earthquakes, which trigger tectonic movements. Additionally, human engineering activities such as abandoned wells could lead to CO_{2} leaks [3]. Thus, CO_{2} geological sequestration technology is a “double-edged sword”; it could effectively alleviate the environmental disaster of climate change, but, at the same time, it could induce geological environmental disasters. It is therefore imperative that the people research methods for geologically storing CO_{2} and evaluate the security risks.

Recently, there have been several investigations into evaluating the risks of CO_{2} geological storage, and researchers have proposed many risk evaluation methods. However, the proposed standards and methods have mainly been aimed at the storage medium and lack of systemic evaluation weight methods [4–6]. CO_{2} geological storage security risk is a comprehensive combination of many internal and external factors, which are random, fuzzy, grey, and uncertain. It is important that the people analyse the uncertainties of this information. In this regard, uncertainty mathematical theory provides a better way. Based on previous CO_{2} geological storage security risk evaluation research and referring to the mathematical theory of uncertainty [7, 8], a new method is proposed for evaluating the risks in this paper.

#### 2. Uncertainty Measure

Let be objects in the index space . Each object has single evaluation index spaces; that is, . can be expressed as a -dimensional vector . has evaluation levels, and its evaluation level space is , where indicates the th level. The th level is stronger than the ()th level; that is, . Then, is an ordered partition class in evaluation space .

##### 2.1. Uncertainty Measure for a Single Index

Let indicate that measured value belongs to (the th evaluation level). must satisfy

Equation (2) means meets the “normalization” for evaluation space ; equation (3) means meets the “additivity” for evaluation space . Because it satisfies (1), (2), and (3), is called the uncertainty measure or measure [9–11].

The matrix is the single index evaluation matrix. When constructing it, the single index measure function must be first established. Existing methods for constructing this function include linear, exponential, parabola, and sinusoidal functions. Regardless of its type, the simulation function must be nonnegative, unitary, and additive. Suitable uncertainty measures can be selected according to the characteristics of specific indexes. A linear uncertainty measure is currently the most popular and simple, so it was used in this paper.

##### 2.2. Determining the Index Weight

Let represent the importance of relative to the other indexes, and let be the weight of . The weight of each index is determined using information entropy theory [12–15]. That is,

##### 2.3. Comprehensive Measure for Multiple Indexes

Using the index weights, the multi-index comprehensive measure can be calculated to evaluate . Here, if , , then is known as the uncertainty measure, and is the multi-index comprehensive evaluation measure vector of .

##### 2.4. Recognition Criterion of the Credibility

If , a credibility criterion can be introduced. Let be the degree of credibility, where ; typically use or 0.7. If , then it is considered to belong to .

##### 2.5. Ordering

As well as discriminating ’s evaluation level, sometimes it must be ordered according to its importance. If , the command value of is . Then, , and , where is the uncertainty importance degree of evaluation factor . is called the uncertainty importance vector. is ordered according to the value of .

#### 3. Example Application

The data of 3 typical zones in the Erdos and Hetao basins provided by Wang [16] were taken as the research object. Drawing from the results in [17–20], we set the cover layer lithology, total thickness of the cover layer, seismic safety, presence of drilling and abandoned wells within 25 km^{2} of the site, topography, main wind directions between the CO_{2} perfusion site and residents, distance from permanent residents, and distance from surface drinking water sources such as rivers and reservoirs, as evaluation factors. The qualitative parameters such as the cover layer lithology, seismic safety, presence of drilling and abandoned wells within 25 km^{2} of the site, topography, and main wind directions between the CO_{2} perfusion site and residents are valued by a semiquantitative method. The grading standards and values are shown in Table 1. The values of the quantitative parameters such as total thickness of the cover layer, distance from permanent residents, and distance from surface drinking water sources such as rivers and reservoirs were valuable measures, as shown in Table 2. Each evaluation index was classified and valued, and the evaluation set is (or high, medium, and low). The basic situations of number 1, number 2, and number 3 target zones are shown in Table 3.