Title - Prediction of corrosion rate and wear rate constant in gas pipelines using neural network

Master thesis

Trend of precision instruments and automation in the oil industry

Introduction of the whole research

One of the important scientific, technical and economic topics is the issue of corrosion of metals and the protection of metal facilities. Investigating the topic of corrosion is not so simple, and with all the research done, its factors are still not known correctly. Apart from the use of chemical sciences to deal with corrosion, the use of other sciences in controlling and predicting corrosion and using its results in the repair of metal equipment is of particular importance. The problem of corrosion in the oil and gas industry has become more serious than other industries due to the presence of corrosive compounds. The inability to predict the corrosion rate makes it impossible to predict the resulting failure times, which causes problems for the maintenance and repair teams. So far, various methods have been used to deal with this phenomenon. Corrosion modeling can be effective in knowing more and predicting the issues arising from it. In these modelings, more than mechanistic methods have relied on the theoretical background of corrosion and mathematical formulas, but due to the inherent complexity of this phenomenon, these methods have not been very successful.

Due to the mentioned complexity and the multitude of known and unknown factors affecting this phenomenon, it seems that data-driven methods such as neural networks can be used to predict the corrosion rate, provided that sufficient data is available in this field.

This research is based on the use of The capabilities of neural networks have been established to predict the corrosion rate, and for this purpose, the information collected from the gas fields covered by the South Zagros Exploitation Company has been used. Another important issue that is of particular importance in the gas industry is the phenomenon of wear/corrosion. The wear phenomenon is very likely in wells with high flow rate or with solid particles suspended in the production fluid. Even in sand-free conditions or clean services where the intensity of sand production is about several pounds per day, the damage caused by wear is very high at high production speeds. In industry, modified shear stress relationships such as the one provided by the standard are used to predict the wear rate.

where

Ve: fluid wear rate (feet per second)

: volume mass of gas-liquid mixture at operating pressure and temperature (pounds per cubic foot)

: empirical coefficient (unitless)

The limitations and problems of using equation (1-1) for wells are mostly related to the constant value "". The API RP 14E standard suggests that for wells that do not produce sand and also for wells that use CRA (corrosion resistant alloy) tubing, values ??of 150 to 200 should be considered for the constant. If sand or sand is produced along with the fluid, they consider the number 100. Today, after many years have passed since the API RP 14E standard was created, its ineffectiveness has become clear to everyone. The "C" constant in the API RP 14E standard is considered very conservative. In this research, we will predict the wear rate constant (experimental coefficient C). For this purpose, the information collected from the gas fields covered by South Zagros Exploitation Company has been used.

1-2 previous activities and research history

In recent years, the use of artificial intelligence in the field of corrosion process modeling has received attention. Artificial neural network has become one of the most widely used methods in the field of corrosion process modeling. In the following, some researches about corrosion in which neural network is used for modeling are introduced.

One of the most serious works that have been done for the use of neural network in corrosion problems and one of the first in this field is the prediction of corrosion rate using neural network by Smets and Bogaerts. In their work, they developed a neural network and used it to predict [1] SCC on type 304 stainless steel in the presence of chloride and oxygen compounds and a specified temperature. They found that the neural network method is superior to the traditional fitting method in this regard.[1]

In another research, a neural network model was produced to predict the number and depth of pits caused by pitting corrosion. Of course, information about the topology and size of the network and how to train it is not given. The progress in the depth of holes and their number was effectively modeled and showed good results compared to the experimental data. [2]

Neural network has been used to predict the type of corrosion from the polarization curve. The inputs of the network are the density and pitting corrosion potential, and the outputs are the risk of each type of general, pitting, and brittle corrosion. [3]

Nesik et al. in an article have mentioned two important problems that cause neural networks to be used less in corrosion topics. The first reason is the lack of familiarity of corrosion engineers with the category of artificial intelligence and neural network and its application in corrosion prediction, and the second reason is the lack of sufficient data in this matter. Of course, in this article, in the first part, there are explanations about the neural network for corrosion engineers, and in the second part, the Monte Carlo method is introduced, and a practical work has been done during it. [4] [

Trasati and Gabetta produced a neural network in their research that used crude oil, acid number and sulfur percentage, chemical composition of materials, chromium percentage and molybdenum percentage, operating conditions of the process (temperature, pressure and flow rate) as input and corrosion rate in millimeter per year (mpy) as output and successfully predicted the corrosion rate through the generated neural network. [5]

Many researches have not been done in the field of predicting the constant rate of wear in gas pipelines, as well as checking the existing standards in this field.

API RP14E standard suggests a fixed value of "C" for corrosive services, 100 and for services that are under corrosion protection, 150 to 200. For services that do not have corrosion problems, higher values ??for the constant "C" have been suggested, although these values ??have not been specifically stated.

In addition to the inherent limitations in the reservoir that affect the production capacity of the gas producing wells of each reservoir, the limitation of the fluid speed in the production line of the well in order to prevent the phenomenon of wear/corrosion is another factor that determines the production capacity of a gas well.

     In the available references, there are different definitions of this limit speed (which is known as the rate of wear) and various values ??have been mentioned for it. Among other things, in the case of using corrosion-resistant alloys (CRA) in the gas production brain tube, increasing this speed up to several times the usual speeds is allowed.   

     It is necessary to explain that the value of the "C" factor mentioned in the standard and based on industrial experiences in the pipelines for transporting fluids with solid particles, for permanent[2] and intermittent[3] services, is suggested as 100 and 125 respectively. The use of C values ??of 150-200 for permanent service and 250 for shift services is allowed. In cases where the production of solid particles with the fluid is likely, C values ??will be greatly reduced. The importance of the API RP 14E model is due to its connection with the two issues of the initiation of the Annular Mist regime and the speed required to destroy the carbonate and iron oxide films on the brain tube wall.