An Automated Diagnostic Analytics Workflow for the Detection of Production Events - Application to Mature Gas Fields

Abstract

Detection of production and well events is crucial for planning of production and operational strategies. Event detection is especially challenging in mature fields in which various off-normal events might occur simultaneously. Manual detection of these events by an engineer is a tedious task and prone to errors. On the other hand, abundance of data in mature fields provides an opportunity to employ data-driven methods for an accurate and robust production event detection. In this study a data-driven workflow to automatically detect production events based on signatures of events provided by experts is demonstrated. In the developed workflow, state-of-the-art data-driven methods were integrated with the domain knowledge for an accurate and robust detection. The methodology was applied on several case studies of mature fields suffering from production issues, such as scaling and liquid loading. It was found that the workflow is accurate, robust and computationally efficient which could detect new events (verified by the expert). The demonstrated method could be implemented both in the real-time or offline fashion. Such a workflow is sufficiently generic which can be applied for detection of different events and anomalies than tested and verified in this paper, such as leakage, production losses, …

AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems

“…From logs to stories, Afzaliseresht et al [1] provide human-centered data mining for cyber threat information. Poort et al [70] have described an automated diagnostic analytics workflow for the detection of production events-application to mature gas fields. Srinivas et al [94] provide a prescriptive analytics framework for optimizing outpatient appointment systems using machine learning algorithms and scheduling rules.…”