Downhole shocks and vibrations have been identified by many operators as one of the biggest causes of Non-Productive Time, the most significant factors limiting rate of penetration (ROP) and the leading cause of premature failure of downhole tools. Today most of the existing methods for detection and characterization of downhole dynamics rely on costly downhole sensors integrated in bottom hole assembly (BHA). This paper presents a new technique for detecting and characterizing drillstring shock and vibrations in real-time using solely surface measurements and a machine learning method. Using historical offset well data and simulated well data, this new technique provides a method to build a classifying model that can be used during drilling operations to characterize real-time drilling data. Validation of the new technique on recorded data demonstrates the method’s capability to detect and characterize downhole dynamics such as stick-slip and lateral shocks from surface measurements.

Summary In hole enlargement while drilling (HEWD) operations, underreamers are used extensively to enlarge the pilot hole. Reamer wipeout failure can cause additional bottomhole assembly (BHA) trips, which can cost operators millions of dollars. Excessive reamer shock and vibration are leading causes of reamer wipeout; therefore, careful monitoring of reamer vibration is important in mitigating such a risk. Currently, downhole vibration sensors and drilling dynamics simulations (DDSs) are used to comprehend and reduce downhole vibration, but vibration sensors cannot be placed exactly at the reamer to monitor the vibrations in real time. DDSs are difficult to calibrate and are computationally expensive for use in real time; therefore, the real-time reamer vibration status is typically unknown during drilling operations. A process digital twin using a hybrid modeling approach is proposed and tested to address the vibration issue. Large amounts of field data are used in advanced DDSs to calibrate the HEWD runs. For each HEWD section, calibrated DDSs are performed to comprehend the downhole vibration at the reamer and downhole vibration sensors. A surrogate regression model between reamer vibration and sensor vibration is built using machine learning. This surrogate model is implemented in a drilling monitoring software platform as a process digital twin. During drilling, the surrogate model uses downhole measurement while drilling (MWD) data as inputs to predict reamer vibration. Wipeout risk levels are calculated and sent to the operators for real-time decision-making to reduce the possibility of reamer wipeout. Large volumes of reamer field data, including field recorded vibration and reamer dull conditions were used to validate the digital twin workflow. Then, the process digital twin was implemented and tested in two reamer runs in the Gulf of Mexico. A downhole high-frequency sensor was placed 8 ft above the reamer cutting structure in one field run, and the recorded sensor vibration data and corresponding reamer dull conditions showed a very good match with the real-time digital twin predictions in a low-vibration scenario. Cases in high vibration are needed to fully validate the feasibility and accuracy of the digital twin. State-of-the-art downhole sensors, DDS packages, large amounts of field data, and a hybrid approach are the solutions to building, calibrating, and field testing the reamer digital twin to ensure its effectiveness and accuracy. Such a hybrid modeling approach can not only be applied to reamers but also to other critical BHA components.

The highly productive Permian Basin requires wells with high dogleg severity (DLS) curve and long lateral sections. After many years of development and participation by all major industry players, most 8.5in size wells are still being drilled using two bottomhole assemblies (BHAs); one for the curve and a second for the extended lateral section. This increases cost, time, and risks. With development of our new solution, curve and lateral sections of Permian wells can now be consistently drilled in one run. Using in-house digital modeling and dynamic drilling simulation software of the complete drilling system and complex lithology profiles, design attributes were evaluated for directional performance before initial prototypes were created. These models increased efficiency and cost savings in the design process. Analysis revealed that shortening the distance from the RSS pad actuators to the bit (L1), increases the build-rate capability, increases the DLS output in the curve section, and provides tight trajectory control in long laterals. The system design also has a proprietary bit box connection and polycrystalline diamond compact (PDC) cutters on the bias unit. Prototype testing was done on high-DLS curve and lateral wells with major operators delivering wells per client requirements. The new solution successfully landed high DLS curve sections in 14 wells. The solution achieved a new milestone of delivering the 8.5-in curve on target and much faster than the conventional motor in the same application. After the curve sections the same BHA drilled into the long lateral section without making a trip between the curve and lateral sections. Several records were broken in some of these 14 wells, including 19% more daily footage than the previous record. Almost all these wells were also drilled using a remote operations center utilizing latest digital capabilities, reducing onsite footprint. Based on the most conservative figures from field test results and projected usage, the increased efficiency and faster well delivery time can significantly impact sustainability, reducing CO2 emissions per well drilled by this new solution.

After 10 years of Brazilian presalt exploration and development, the carbonate reservoirs continue to pose drilling challenges, leading to unwanted bottomhole assembly (BHA) trips due to severe shock and vibration, low rates of penetration (ROP), and premature drill-bit cutting structure damage. Today, industry efforts to improve the performance in the Brazilian presalt carbonates are driven by trial and error, which is very costly in the ultradeepwater drilling environment. The adoption of a collaborative mindset since 2012 between a service provider and operator with the desire to bring about a step change in drilling efficiency on the Brazilian presalt cluster enabled a systematic learning framework to capture, evaluate, and reuse knowledge from drilling dynamics, geological, and petrophysical aspects. The innovation of this work is the implementation of an improved, fully digital bit design workflow, integrating calibrated 4D dynamic simulation model and petrotechnical expertise from drilling engineering, geomechanics, geology, and petrophysics groups to continue to push the drilling performance envelope in the challenging Brazilian presalt application.

In hole enlargement while drilling (HEWD) operations, reamers are extensively used to enlarge the pilot hole. Reamer wipeout failure can cause additional bottom hole assembly (BHA) trips, which can cost operators millions of dollars. Excessive reamer shock and vibration is a leading cause of reamer wipeout; therefore, careful monitoring of reamer vibration is important in mitigating such a risk. Currently, downhole vibration sensors and drilling dynamics simulations are used to comprehend and reduce downhole vibration, but vibration sensors cannot be placed exactly at the reamer to monitor the vibrations in real time. Drilling dynamics simulations are difficult to calibrate and are computationally expensive for use in real time; therefore, the real-time reamer vibration status is typically unknown during drilling operations. A process digital twin using a hybrid modeling approach is proposed and tested to address the vibration issue. Large amounts of field data are used in advanced drilling dynamics simulations to calibrate the HEWD runs. For each HEWD section, calibrated drilling dynamics simulations are performed to comprehend the downhole vibration at the reamer and downhole vibration sensors. A surrogate regression model between reamer vibration and sensor vibration is built using machine learning. This surrogate model is implemented in a drilling monitoring software platform as a process digital twin. During drilling, the surrogate model uses downhole measurement while drilling (MWD) data as input to predict reamer vibration. Wipeout risk levels are calculated and sent to the operators for real-time decision making to reduce the possibility of reamer wipeout. Large volumes of reamer field data, including field recorded vibration and reamer dull conditions were used to validate the digital twin workflow. Then, the process digital twin was implemented and tested in two reamer runs in the Gulf of Mexico. A downhole high-frequency sensor was placed at the reamer in one field run and the recorded sensor vibration data and corresponding reamer dull conditions showed a very good match with the real-time digital twin predictions. The field tests demonstrated the feasibility and accuracy of the digital twin and established a method for aiding in future real-time decision making. State-of-the-art downhole sensors, drilling dynamics simulation packages, large amounts of field data, and a hybrid approach are the solutions to building, calibrating, and field testing the reamer digital twin to ensure its effectiveness and accuracy. Such a hybrid modeling approach can not only be applied to reamers, but also to other critical BHA components.