Paper 19541 manuscript 8722 1

LIKELIHOOD EVALUATION OF PIPELINES CORROSION IN THE MEXICAN SOIL ASSISTED BY GEOGRAPHIC INFORMATION SYSTEMS

Arturo Godoy* Universidad Autonoma del Estado de Morelos, CIICAP Ave. Universidad 1001 Col. Chamilpa Cuernavaca, Morelos. 62210 Mexico

Fernando Rubi, Alejandra de León Ibarra, Lorenzo M. Martinez-dela-Escalera and Jorge Canto.

Corrosion y Proteccion Ingeneria, S.C Rio Nazas 6, Cuernavaca, Morelos, 62290.

Mexico

Leonardo de Silva-Munoz Instituto de Investigaciones Eléctricas Reforma 113 Col. Palmira Cuernavaca, Morelos. 62490 Mexico

Jorge A. Ascencio, and Lorenzo Martínez* Instituto de Ciencias Físicas, UNAM Ave. Universidad 1001 Col. Chamilpa Cuernavaca, Morelos. 62210 Mexico

*

Also at Corrosion y Proteccion Ingenieria SC ABSTRACT

The new integrity management standard will be operating for main pipelines in Mexico. In order to achieve that standard, a new methodology was developed where different variables and models of Geographic Information Systems (GIS) were combined in a geodatabase capable of identifying hot spots for corrosion on pipelines. Such geodatabase, integrated into GIS with a graphical user interface, facilitates data review when performing preassessments on an External Corrosion Direct Assesment campaign.

Using multilayer systems from the Mexican Institute of Geography, factors influencing the corrosion rate of buried hydrocarbon pipelines can be visualized. Variables like soil pH, porosity, salinity, temperature, humidity, resistivity, redox potentials, and bacteria concentration can be categorized in order to estimate the likelihood of pipeline corrosion or soil corrosivity at a given area. In order to represent the physical-chemical properties of the soil, these are converted to a raster data format (data model in which geographic entities are represented by pixels arranged forming a grid, with many bands of data).

This conversion discretizes continuous variables in order to render them easier to manage visually and mathematically. Data obtained from Close Inspection Survey (CIS), Direct Current Voltage Gradient (DCVG), Alterate Current Voltage Gradient (ACVG), and In-Line Inspection (ILI) studies can also be integrated to the database allowing more complete corrosion likelihood, and risk assesments.

Key words: Corrosiveness, raster image, soil properties, environmental factor, corrosion map, multilayer, pipeline integrity, Close Inspection Survey, Direct Current Voltage Gradient, Alterate Current Voltage Gradient, In-Line Inspection INTRODUCTION Using data processing technologies and geographical information systems on georeferenced data of Mexican soil properties, a new approach on corrosion risk assessment on buried pipelines has been developed. The data management and analysis method that is presented allows the construction of georeferenced maps of a relative soil corrosiveness index that can deliver valuable information for pipeline integrity managers and pipeline network construction engineers.

Georeferenced data is information of a particular location or region that is associated to a value of latitude and longitude. Georeferenced information such as atmospheric temperature, soil properties or solar irradiance can be displayed as color coded maps where each color represents a property value or class. Vast amounts of information can be quickly analyzed by using such maps. Color coded maps can be constructed form data structures representing a grid of rectangular cells of color or pixels. Each rectangular cell of the grid represents a point or region on the map and stores a value of a local physical property or an area classification. Such data structures are known as raster images.

GEOREFERENCED RASTER IMAGES FOR PRESENTING DATA. Raster images are constructed from georeferenced points, lines or polygons with associated qualitative or quantitative attributes. For example, polygons representing soil types become sets of rectangles of different colors; lines become “pixelated� lines, while points become colored rectangles (figure 1).

Figure 1. Examples of raster image construction from georeferenced features.

Raster images are very useful for terrain information analysis where localization information becomes implicit in the position of particular cells inside the grid. In raster images continuous terrain information becomes discretized, that is, it is sectioned into "discrete" or dicontinous values, which eases data manipulation for certain operations such as surface area estimation. Nevertheless, some raster images can have so much information that displaying on a computer screen can consume too much memory resources. When spatial resolution is unnecessarily high, it is possible to reduce a raster image

resolution by constructing another image with bigger rectangles. But if rectangle dimensions are too big, some polygon and line features of the original map may disappear. In order to determine the optimal size of the raster cells one must consider the available computer power and the polygon or line feature loss (figure 2).

Figure 2. Raster image construction with different cell sizes.

Raster images can also be useful for organizing terrain information. Data can be categorized into groups corresponding to different classes of terrain like forests, water bodies, urban areas, etc. or into groups corresponding to areas with similar properties, like for example dividing a property scale into ranges and create one group per range. It is also possible to calculate the area occupied by each category or group.

Figure 3. Example of categorized geographic information Using multiple layers of raster images mathematical operations can be performed in order to calculate terrain properties that depend on the variables represented on each layer. For example, its possible to estimate soil temperature from different layers of variables like solar radiation, soil humidity, soil texture, soil composition etc.

Figure 4. Stack of raster images RELATIVE CORROSIVITY INDEX ESTIMATION USING RASTER DATA One of the multiple applications for raster images can be the calculation of a relative corrosivity index for buried pipelines. Soil corrosivity is a qualitative concept; there is no standard definition for it. It is clear though that key soil properties can influence significantly the corrosion processes of buried

metallic structures. For example, in the case of buried steel, a high pH in the soil will have a lower corrosivity than an acid soil. High soil temperature, high humidity, low resistivity also mean higher corrosivity.

Group

Mean atmospheric temperature (ยบC)

1

0-10

2

10-20

3

20-30

4

30-40

Color

Figure 5. Categories Example Based on a database of soil properties and environmental variables of Mexico, georeferenced raster images were constructed, organized in layers, and used for the estimation of a soil relative corrosivity index for buried pipelines. The database was constructed using data available from the National Institute of Statistics and Geographical Information (INEGI). By trying different resolutions for the images, going from 0.05 m up to 1 km, it was found that a resolution of 100 m x 100 m was the optimum value between information loss and computing and image refreshing time. The relative corrosivity index was calculated by considering several variables assumed to have an influence on soil corrosivity. These variables are listed below: * Soil texture (STex). Soils are basically classified in three different types of soil texture: sand, silt and clay. The sand types are soils that have a lot of air trapped between its grains. This makes this type of soil a good thermal insulator. For their influence on soil temperature, sandy soils were given the numeric value of 1. Clay soils are more compact and normally contain much more water. Also for their influence on soil temperature, clay soils were given the numeric value of 3. Silt soils, being an intermediate type between sand and clay were given the numeric value of 2. Besides temperature conductivity, soil texture should have an influence on corrosivity according to its oxygen permeability and concentration, which can become a source of electrochemical potential gradients, and to water permeability and residence time, which can modify considerably soil conductivity. Neither oxygen nor water concentration dynamics were considered in the present model. Another variable not considered is the organic matter content of the soil, which increases the probability of microbially influenced corrosion. * Vegetation covering (VegC). The area covered by vegetation, was considered to have an influence on soil temperature due to the limited solar energy that can reach the soil. In the model it was considered to have an inversely proportional influence on soil temperature. * Water precipitation (WPrec). Water precipitation can reduce considerably soil temperature. In the present model it was considered to have an inversely proportional influence on soil temperature.

Its influence on humidity was not considered in this model. Instead, available maps of soil humidity were used. * Solar irradiance (Solar). Solar irradiance is the major contributor to ambient and soil temperature. In the model it was considered to have a directly proportional influence on soil temperature. A yearly average solar irradiance was calculated based on the de average solar irradiance of each one of the four seasons. Figure 6 shows the different solar irradiance in each season.

Figure 6. Solar Irradiance in the different season in Mexico * Ambient temperature (Tamb). Ambient temperature is intimately related to surface soil temperature. Even though soil temperature varies considerably with depth, here ambient temperature was considered to be an important qualitative variable for estimating a soil temperature index. Direct proportionality was considered between ambient and soil temperature. * Soil temperature index (STI). Soil temperature was considered to have a directly proportional influence on corrosivity. As no soil temperature maps were found, a soil temperature index was estimated using the following formula: Soil Temperature Index = STI =[K1 ] (([Solar][T_AMB ])/[VegC]) where K1 is an empirical constant that could be estimated experimentally. Here it was given the value of 1.0. * Soil humidity (SHum). Soil humidity influences corrosion basically through its role on soil conductivity. A more conductive soil will allow more current to flow though it and thus, corrosion rate will be higher. In the model a directly proportional influence on corrosivity was considered. * Soil resistivity (SRes). Soil resistivity was assumed to have a directly proportional influence on corrosivity. A less conductive soil means a lower corrosion rate on metals buried in it. * Soil pH (SpH). Soil pH is a parameter that influences corrosivity significantly. For buried iron or steel structures an acid pH (values lower than 7) increases the corrosivity of the soil while an alkaline pH reduces it.

As a first exploratory effort, a simple formula was used where, depending on each variable, either direct or inverse proportionality was considered. Relative values were used for each of the variables using the following formula: Relative Variable Value = local value/Mexico’s mean value The Soil Relative Corrosivity Index (SRCI) was calculated with the following formula: SRCI=[SHum]+[SRes]+[SpH]+[WPrec]+[STex]+ [STI] The SRCI is a relative value; it only serves as a mean to detect which zones of the country are more corrosive than others.

RESULTS The following figures present the raster images of some of the variables mentioned above. As mentioned above, the raster images were constructed using a 100m x 100m resolution and were calculated using data from INEGI. It can be seen that it would be a difficult task to estimate the corrosivity with just visually analyzing the maps.

Soil Resistivity

Soil Temperature

Soil Texture

Water Precipitation (Average/year)

Soil Humidity

Soil pH

Figure 7. Georeferenced raster images representing values of soil texture, humidity, pH, resistivity and temperature with a color scale.

Figure 8. Raster image of the estimated soil corrosivity index of Mexico’s territory.

Figure 8 presents a raster image of MÊxico’s territory with colors corresponding to the estimated soil corrosivity index. Red zones have a higher corrosivity index than green zones. The map clearly shows zones with different corrosivities.

Even though experimental validation would certainly give necessary elements for refining the formulas used, it is encouraging to see how powerful the technique can be. By taking into account the rest of the variables involved in corrosion of buried structures, along with the development of more rigorous models, the technique can be also integrated into software packages that could suggest the best routes for new pipelines considering installation and operation costs. It can also be integrated into pipeline integrity management systems with geographical information software that could help pipeline managers to make decisions.

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