Research On Methods Of Using Machine Learning To Predict Casing Damage

With the oilfield enters the middle and later stages of development,the geological conditions are becoming more and more complex,and the frequency of casing damage in oil and water wells is also increasing,which seriously restricts the development benefits of the oilfield.Therefore,real-time prediction of the production status of casing is helpful to take timely precautions and has important engineering significance for maintaining the normal production of the oilfield.Casing damage problem has many influencing factors and complicated mechanisms.However,traditional casing damage prediction methods are mostly based on geological,engineering and other static data to establish a mechanical model.It is difficult to reflect changes in the production environment of oil and water wells in a timely manner.Firstly,the performance in casing damage prediction of several common classification models based on production dynamic data are compared.Then aiming at the imbalanced distribution of casing damage sample,two improved methods are proposed from the perspective of improved Adaboost algorithm and ensemble learning.Finally,according to the prediction results of casing undamaged wells,an early warning rule based on Kmeans algorithm is proposed to quantify and visualize casing damage.Taking the A fault block of S Oilfield as the experimental object,the result indicates that improved Adaboost model performs best.The recall are 87.0% and 90.6% for optimized water well model and optimized oil well model.The visualization of casing damage early warning can effectively guide the oilfield to monitor the casing status in real time,which provides a new idea for the research of oilfield casing damage prediction and has a good application prospect.