Data Aggregation and Drilling Automation: Connecting the Interoperability Bridge between Acquisition, Monitoring, Evaluation, and Control

Abstract

Although the oil and gas industry continues to progress with drilling automation, technical challenges remain and must be addressed before this vision can become a reality. One of these challenges is the data aggregation between operators, service companies, drilling contractors and equipment manufacturers. Automation requires that the system not only control sub processes, it must also enable more complex intelligent systems to plan and react to real-time evaluation criteria and respond to predictive intelligence in real time. Data from control systems such as top drives, and evaluation systems such as downhole logging while drilling (LWD) sensors, must be aggregated in a meaningful way so it is portable, contextual, and actionable. Consider controlling the rate of penetration (ROP) based on predictive analytics assessed from formation evaluation (FE) data provided by an LWD sensor. To achieve this, intelligent control and evaluation systems must monitor and exchange information and events within a single, shared perspective.In this paper, we review several technologies currently in use throughout the drilling industry-WITS, WITSML, OPC and PROFIBUS-and present case studies describing the current roles these technologies play and the problems they solve. The authors then analyze the limitations between these interoperable systems and associated barriers in achieving the drilling automation vision. This paper recommends defined goals for the data aggregator and identifies existing challenges surrounding its deployment in current drilling automation environments. The authors also provide recommendations for how the industry should proceed with the implementation of data aggregation for automation. These recommendations ensure drilling automation becomes more than simple process mechanization, but advances towards intelligent systems that transform plans into reality and optimize performance and cost.

Modeling of dynamic stability of the well deepening process based on the catastrophe theory

“…The main arguments for applying the catastrophe theory to modeling dynamics of the well drilling process are as the follows [9,[16][17][18]:…”

“…It should be noted that scientists of the Society of Petroleum Engineers (SPE) and the International Association of Drilling Contractors (IADC) [16] pay due attention to the issues of preventing drilling emergencies, namely in the subsystem of well irrigation. The IADC/SPE international conferences are deeply concerned with the issues of automation of drilling control and management [17] as well as the issue of emergency-free drilling based on innovative technologies [18]. The problems of all these studies consist in that the drilling process is non-reproducible and the models formed for a particular well cannot be used for the other well.…”

Drilling Systems Automation: Current State, Initiatives and Potential Impact

“…(2013), point out many of the shortcomings of applying the measurements to models. In addition, there is a need for metadata (basic information describing the data) to understand the accuracy and resolution of the sensors, as explained in Sadlier et al (2012). Drilling rigs suffer from two critical sensor drawbacks: the sensors are inadequate for real-time control of the process they attempt to measure (and even ineffective to provide the human operator with sufficient accuracy to control the process) and there are insufficient sensors to provide a complete picture of the rig operation.…”

Drilling-Systems Automation: Current State, Initiatives, and Potential Impact

“…Cayeux et al (2013) point out many of the shortcomings of applying the measurements to models. In addition, there is a need for metadata (basic information describing the data) to understand the accuracy and resolution of the sensors, as explained in Sadlier et al (2012). Drilling rigs suffer from two critical sensor drawbacks: The sensors are inadequate for real-time control of the process they attempt to measure (and even ineffective to provide the human operator with sufficient accuracy to control the process), and there are insufficient sensors to provide a complete picture of the rig operation.…”