Three-dimensional finite element numerical simulation and physical experiment for magnetism-stress detecting in oil casing

Abstract

The casing damage has been a big problem in oilfield production. The current detection methods mostly are used after casing damage, which is not very effective. With the rapid development of China's offshore oil industry, the number of offshore oil wells is becoming larger and larger. Because the cost of offshore oil well is very high, the casing damage will cause huge economic losses. What's more, it can also bring serious pollution to marine environment. So the effective methods of detecting casing damage are required badly. The accumulation of stress is the main reason for the casing damage. Magnetic anisotropy technique based on counter magnetostriction effect can detect the stress of casing in real time and help us to find out the hidden dangers in time. It is essential for us to prevent the casing damage from occurring. However, such technique is still in the development stage. Previous studies mostly got the relationship between stress and magnetic signals by physical experiment, and the study of physical mechanism in relative magnetic permeability connecting the stress and magnetic signals is rarely reported. The present paper uses the ANSYS to do the three-dimensional finite element numerical simulation to study how the relative magnetic permeability works for the oil casing model. We find that the quantitative relationship between the stress's variation and magnetic induction intensity's variation is: * K B     , K = 8.04×10 9 , which is proved correct by physical experiment.

A Machine Learning Method for the Risk Prediction of Casing Damage and Its Application in Waterflooding

“…The second kind of method is based on finite element theory and uses commercial software to establish a spatial simulation mechanical model with which to simulate the casing deformation under the combined load of compression, torsion, and bending and to analyze the stress and deformation characteristics of the oil and water well casing through numerical calculation. Examples of this type of method are the finite element method, boundary element method presented by Lian [15,16], Lagrange element method presented by Deng [17], and coupled seepage-geomechanics models represented by Han [18]. The third kind of method effectively identifies casing damage and quantitatively evaluates the casing health through measurement of the water injection profile, pulsed neutron analysis, measurement of the multi-arm well diameter, acoustic logging, and electromagnetic flaw detection [19][20][21][22].…”

“…The casing-damage prediction method of the SVM takes the influencing factors of casing damage as the input of the SVM and optimizes the parameters of the SVM using an immune algorithm to make an accurate prediction. The use of SVM algorithms to build single-well casing loss prediction models has been shown to achieve good results [16,[24][25][26][27]. Additionally, the artificial neural network has attracted much attention for its strong autonomous learning, strong memory and fault tolerance, strong nonlinear parallel processing ability, and other characteristics.…”