Exxon Mobil Corporation has numerous subsidiaries, many with names that include ExxonMobil, Exxon, Esso and Mobil. For convenience and simplicity in this paper, the parent company and its subsidiaries may be referenced separately or collectively as "ExxonMobil." Abbreviated references describing global or regional operational organizations and global or regional business lines are also sometimes used for convenience and simplicity. Nothing in this paper is intended to override the corporate separateness of these separate legal entities. Working relationships discussed in this paper do not necessarily represent a reporting connection, but may reflect a functional guidance, stewardship, or service relationship.
AbstractAn important challenge for asset management is to analyze large amounts of data in a short period of time to provide insightful information for decision making in a timely fashion. Analyzing all available data manually is impractical and inefficient. It is advantageous to develop pattern recognition algorithms to recognize events-of-interest to achieve effective asset management.
SPE 167839building a statistical model to recognize the validity of rate measurement tests in a test separator. In this case, through their daily activities, the operators have labeled most of these tests as valid or invalid. The extensive amount of well test validation data provides sufficient information to assess the newer approaches under review. The plan then is to apply a similar approach to tasks such as equipment health monitoring to identify pump failures with limited expert input.Conceptually, reduction in labeled input can be achieved by combining the information from the labels and the statistical distribution of the data (e.g., clusters). As an extreme example, consider that the pump measurement data may show two distinct clusters and the operators have labeled a few data points in one cluster as pump failures when reports had to be made due to wells being shut in. This information is sufficient to label one of the clusters as healthy and the other one as faulty. For a new measurement, a prediction may be made by first determining the cluster to which the measurement belongs and then assigning it the corresponding label. While most real world problems are much more challenging than this example due to the number of data points, dimensionality of the data, lack of clear cluster structure and potential ambiguity of data structures, similar ideas can be used to develop highly accurate statistical models with a limited number of labels.