Risk assessment of corroded casing based on analytic hierarchy process and fuzzy comprehensive evaluation

Casing corrosion during CO2 injection or storage results in significant economic loss and increased production risks.Therefore,in this paper,a corroded casing risk assessment model based on analytic hierarchy process and fuzzy comprehensive evaluation is established to identify potential risks in time.First,the corrosion rate and residual strength characteristics are analyzed through corrosion tests and numerical simulations,respectively,to determine the risk factors that may lead to an accident.Then,an index system for corroded casing risk evaluation is established based on six important factors:temperature,CO2 partial pressure,flow velocity,corrosion radius,corrosion depth and wellhead pressure.Subsequently,the index weights are calculated via the analytic hierarchy process.Finally,the risk level of corroded casing is obtained via the fuzzy comprehensive evaluation.The corroded casing risk assessment model has been verified by a case well,which shows that the model is valuable and feasible.It provides an effective decision-making method for the risk evaluation of corroded casing in CO2 injection well,which is conductive to improve the wellbore operation efficiency.

Adaptive fault diagnosis of sucker rod pump systems based on optimal perceptron and simulation data

A highly precise and timely diagnosis technology can help effectively monitor and adjust the sucker rod production system(SRPS)used in oil wells to ensure a safe and efficient production.The current diagnosis method is pattern recognition of a dynamometer card(DC)based on feature extraction and perceptron.The premise of this method is that the training and target data have the same distribution.However,the training data are collected from a field SRPS with different system parameters designed to adapt to production conditions,which may significantly affect the diagnostic accuracy.To address this issue,in this study,an improved model of the sucker rod string(SRS)is derived by adding faultparameter dimensions,with which DCs under 16 working conditions could be generated.Subsequently an adaptive diagnosis method is proposed by taking simulated DCs generated near the working point of the target SRPS as training data.Meanwhile,to further improve the accuracy of the proposed method,the DC features are improved by relative normalization and using additional features of the DC position to increase the distance between different types of samples.The parameters of the perceptron are optimized to promote its discriminability.Finally,the accuracy and real-time performance of the proposed adaptive diagnosis method are validated using field data.