Automated Reporting Using Rig Sensor Data Enables Superior Drilling-Project Management

Abstract

An integrated process management system is the key to success of an enterprise working in the exploration business. Classic project management consists of the creation of an expectation, the well plan, appropriate process monitoring and the post analysis of the project through the comparison of the expectation and the actual process. As a result of the post analysis lessons learned are compiled. The quality of this traditional project management is strongly dependent on information flow. State-of-the-art reporting on a drilling rig is recurrent human reporting such as the classic morning report, although there are a lot more sources of information available. The presented concept of automated reporting, away from human observations, is ensured by utilizing rig sensor data. The automated reporting provides management, engineering and operations with the level of detail and granularity of objective, high quality information they need. It also offers the possibility of knowledge management and exchange at the same organizational level providing different "views" of the same project. This means that operations receive information on detailed process parameters, engineering on essential design variables for future wells and management on cost. Along with traditional project management this new automated reporting enables continuous, real-time project tracking and analysis even with a declining workforce because all information is bundled and under "central control". Automated reporting is able to cover the life-cycle of every well construction and allows the establishment of high quality benchmarks for future planning and project evaluation in real-time. Moreover, it enhances the learning and experience level of every single person involved in the process. Finally, it offers the possibility to manage multiple concurrent projects such as a company's fleet of drilling rigs to be able to optimize resource allocation and project economics. Introduction The successful construction and operation of oil and gas wells depends to a high degree on the experience of the people involved and their evaluation and management of more or less unknown factors (e.g. geology). The aim of this paper is to outline a concept which provides a platform that allows the optimal use of the knowledge of an organization and to support all decisions and actions during the life-cycle of a well construction project as much as possible. Providing the possibility of a continuous and real-time comparison of plan and actual at the proper technical and commercial level of detail is the key for a successful project execution. All data collected for the purpose of analysis is completely integrated into a domain model. The result is the description of the "Drilling Process" over time. The key element is a high level of automation to increase efficiency and improve the quality of conventional morning reporting, e.g. using rig sensor data. The derivation of a highly accurate drilling operations plan forms the basis for further performance improvement. Existing plans are often a "self-fulfilling prophecy" in terms of performance. The current level of reporting fulfills its purpose as an administrative tool, but is not the ideal basis for the planning of future wells. Another point is the distribution of the right data at the proper level of detail to different organizational levels. The requirements vary from the driller, who essentially needs information about the use of the brake-handle of the drawworks, to upper management who want overall cost and time performance. In order to fulfill these requirements a new way of reporting, processing of data and analysis, together with visualization has to be developed. The challenge is the integration of subjective experience, the measured state of the system and the financial aspects of a project. A key element is to increase the usability of information with less data entry effort. Data quality management is also of central importance.

Positive Displacement Motor Condition and Performance Prediction Using Surface and Downhole Sensor Data

“…To evaluate the mud motor's condition and performance, obtaining the differential pressure for on and off-bottom operations is crucial. The recognition of operations encountered while drilling has been a subject of interest for many years [29][30][31]. In addition, drilling data quality and reliability are important factors in assessing the operations successfully.…”