Intelligent Real-time Drilling Operations Classification Using Trend Analysis of Drilling Rig Sensors Data

Detection of oilwell drilling operations is an important step for drilling process optimization. If drilling operations are classified accurately, detailed performance reports not only on drilling crews but also on drilling rigs can be produced. Using such reports, the management can evaluate the drilling work more precisely from performance point of view. Mud-logging systems of modern drilling rigs provide numerous sensors data. Those sensors measurements are considered as indicators to monitor different states of drilling process. Usually real-time measurements of the following sensors data are available as surface measurements: hookload, block position, flow rates, pump pressure, borehole and bit depth, RPM, torque, rate of penetration and weight on bit. In this work, collected sensors measurements from mud-logging systems are used to detect different drilling operations. Detailed data analysis shows that the surface sensors measurements can be considered as a main source of information about drilling operations. For this purpose, a mathematical model based on polynomials approximation is constructed to interpolate sensors data measurements. Discrete polynomial moments are used as a tool to extract specific features (moments) from drilling sensors data. Then we use these moments for each drilling operation as pattern descriptor to classify similar operations in drilling time series. The extracted polynomial moments describe trends of sensors data and behavior of rig’s sub-systems (Rotation System, Circulation System, and Hoisting System). Furthermore, this paper suggests a method on how to build patterns base and how to recognize and classify drilling operations once sensors data received from mud-logging system. Drilling experts compare the results to manually classified operations and the results show high accuracy. Introduction Improving performance of drilling process is a big challenge in nowadays drilling industry. To improve drilling performance, we need first to measure it. Performance measurement means determining quantitative values or weights that describe each drilling operation and complete drilling process as resultant. For example, duration of each drilling operation can be considered as a useful measure. Also number of drilling operations and distributions of those operations over different well drilling phases are important measures of drilling performance. Automatic detection and recognition of drilling operation is the first step towards drilling performance measurement and improvement (G. Thonhauser, Mathis, et al. 2006). Automatic detecting and recognition of drilling operations gives flexibility in monitoring and recognizing drilling process. Monitoring drilling operations helps rig operator in finding limitations and shorts in performance of either drilling crews or rig equipment or even both. Then saving potentials are accurately estimated through training plans for drilling crews and/or replacement parts for rig’s equipment. Furthermore, evaluation of drilling performance supports the drilling crews in their drilling tasks with consideration to safety and technical limits of their drilling equipment (G. Thonhauser, Mathis, et al. 2006). Through drilling process, a huge amount of data in form of sensors measurements is produced over time. This data contains not only readings of sensors but also information about each drilling operation i.e. start, end, and behavior of each equipment. Drilling operations such as formations drilling, making connection for new drillstand, breaking connection, pulling out of hole, running in hole, and cleaning hole are carefully chosen as basic drilling operations performed by drilling crew. Each of those drilling operation has a specific pattern in rig sensors measurements. Detecting drilling operations patterns in sensors data supports rig operators in finding out the state of drilling rig instantly. At the end of the day, it gives detailed information on rig state over any span of time. So rig’s operator can easily observe operating time of drilling rig and how the actual performance matches with the pre-defined well plan (G. Thonhauser, Wallnoefer, et al. 2006). Rig Sensors Systems Drilling rig performs its functionality in drilling boreholes through collaboration of three main sub-systems: Rotary System, Circulation System, and Hoisting System. Rotary System is the system that turns the drillstring. Top drive as type of rotary system which consists of one or more motors (electric or hydraulic) connected with appropriate gearing to a short section of pipe called a quill, that in turn may be screwed into a saver sub or the drillstring itself. Also rotary table another type of rotary system and it consists of revolving or spinning section of the drillfloor that provides power to turn the drillstring in a clockwise direction (as viewed from above). The rotary motion and power are transmitted through the kelly bushing and the kelly to the drillstring. RPM and torque sensors measure the revolution per min and torque of rotation at the surface (Florence et al. 2010). Circulation system is defined as the complete, circuitous path that the drilling fluid travels. Starting at the main rig pumps, major components include surface piping, the standpipe, the kelly hose (rotary), the kelly, the drillpipe, drill collars, bit nozzles, the various annular geometries of the openhole and casing strings, the bell nipple, the flowline, the mud-cleaning