| Popis: |
This paper presents a new generalized global model approach for failure prediction for rod pumps. By embedding domain knowledge into an Expectation Maximization clustering algorithm, the proposed global model is able to statistically recognize pre-failure and failure patterns from normal patterns during the training stage. Compared with previous field-specific models, the enriched training set for the global model learns from much larger scale of normal and failure examples from all fields with which a generalized Support Vector Machine (SVM) is trained and is able to predict failures for all fields. The data set for this paper is taken from five real-world assets using rod pump artificial lift systems which contain nearly 2,000 rod pumps. The results show that the global model of failure prediction is capable of capturing future rod pump and tubing failures that produce acceptable precision and recall. The resulting global model is scalable and can be used for predicting failures for proactive maintenance to reduce lost oil production. Results from our case studies with multiple fields data show that precision and recall are better than 65% with this global model. Our prior work [1] used machine learning techniques to generate high quality failure prediction models with good accuracy. However, these efforts suffer from two major drawbacks. First, this used machine learning techniques that require labeled datasets for training the model. Generating these labeled datasets is human-intensive and time-consuming. Second, this model is field-specific which is only applicable to the specific field from which the labeled dataset is derived. These field-specific models generally perform poorly on other fields because of the differences in the data characteristic caused by field geology, operational procedure, etc. Moreover, these models have to be maintained independently, which accordingly raises nontrivial maintenance costs. |
