Semantic Framework Aligns Real-Time Drilling-Management and Control Applications

Abstract

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 194110, “Toward Seamless Interoperability Between Real-Time Drilling Management and Control Applications,” by Eric Cayeux, SPE, Benoît Daireaux, Nejm Saadallah, and Sergey Alyaev, NORCE, prepared for the 2019 SPE/IADC Drilling Conference and Exhibition, The Hague, 5–7 March. The paper has not been peer reviewed. Real-time signals exchanged during drilling operations are in constant evolution, provided by multiple stakeholders that have distinct perspectives on the drilling process. To achieve improved drilling management and control, the seamless exchange of real-time drilling data, without the necessity of any human intervention to configure software applications, is desirable. The complete paper presents a drilling semantic framework that allows software solutions to achieve automatic and versatile self-configuration. However, the data-exchange performances are compatible with the requirements of high-end applications involved in the management and control of drilling operations. Motivation Many data providers are involved in the well-construction process. Their number and role change throughout the drilling process as a function of the different activities that are executed. A typical activity lasts between 1 day and 2 weeks, and, consequently, the constellation of real-time data providers, in the most extreme scenarios, may evolve at the same pace. Furthermore, the drilling system is not static in nature. Typically, new downhole measurement tools are used for every drillstem that is run in hole. Either because of a different setup of data provider companies or as a result of a modification of the drilling system, the number and nature of real-time drilling signals that are active during the well-construction process change almost daily. For any application that needs access to such real-time data feeds, a manual reconfiguration of the system inputs is both cumbersome and a potential source of error. It, therefore, is desirable that applications can automatically and seamlessly expose, discover, and choose the signals that are relevant for the management and control of the drilling process. In the complete paper, to illustrate how automatic discovery and interpretation of the semantics of signals can provide seamless interoperability and improve the quality of service, the authors proceed step by step through an example centered on anomaly detection and characterization. The data consumer application is a data-driven system that detects abnormal hookloads and attempts to characterize the cause of the anomaly. This synopsis will discuss the theoretical underpinnings of the authors’ approach rather than outline the example detailed in the complete paper. Class Hierarchies To Capture Specialization and Generalization In computer science, and more specifically in object-oriented programming, entities that share the same properties and behavior (i.e., functions or procedures, also called methods) are described by a class. A class defines properties and methods that are common to a group of objects. A realization of a class is called an instance of the class and can have its own specific values for each of its properties. It is possible to define subclasses that inherit properties and behavior from the parent classes, but that, in addition, can define new or specialized versions of the properties and methods compared with the parent class. Most object-oriented languages support single inheritance (i.e., a given class can only be a subclass of a single parent class). This defines a directed hierarchical structure of classes, in which classes deeper in the tree structure are more specialized than the shallower ones, or, to put it differently, shallower classes are generalizations of the deeper ones. A class hierarchy is well-suited to describe a decomposition of a domain that needs to be adjusted rarely.