Data-Driven Detection of Well Events in Mature Gas Fields

Summary The production optimization of mature gas fields is severely complicated by the occurrence of certain undesired well events such as salt precipitation, liquid loading, or gas/water coning. Learning from production data of periods in which such events have taken place could help operators improve the process optimization. However, due to the current manual process of interpreting production data, many well events can go unreported. Reanalyzing historic data could retrieve missed events, but this is a time-consuming and costly process. In this study, the dynamic time warping (DTW) algorithm was used in a developed workflow that automates the process of detecting well events which can be operational both in an offline and real-time manner. Such a workflow supports operators in finding well events within production data based on characteristics of target events provided by operators. Based on a case study using field data for a gas well suffering from salt precipitation, the workflow has been proven to be accurate and significantly computational-efficient in finding 8 new events which were not detected by the operator. Additionally, the algorithm was robust in detecting well events even after introducing up to 10% of added noise.