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Opportunity

Unconventionals

Opportunity
Specific Challenge

Identify bit wear in real time and optimize well delivery

Middle East and North Africa

Middle East

Middle East and North Africa

Challenges

  • Unconventional field with heterogeneous formations characterized by shale, carbonate, and anhydrite
  • Interbedded formations that lead to premature bit damages and multiple trips
  • Manage drilling parameters to maximize ROP in interbedded formations 
  • Improve efficiency and reduce rig time

Solution

  • Use automation solutions in real-time to maximize bit depth-of-cut (DoC), optimize drilling parameters in real-time, and improve ROP with the LOGIX® drilling performance optimizer

Results

  • Maximized drilling efficiency with the calculation of the expected DoC based on the bit-rock signature and delivery of optimum parameter set-points 
  • Early identification of bit-wear/damage while drilling 
  • Alerted operator of bit damage condition, and enabled a data-driven decision to trip out of the hole saving the customer rig time

Overview

Drilling unconventional development wells demands efficient solutions to meet timelines across multiple well pads. Achieving consistency and efficiency in well delivery requires adopting automated, data-driven strategies. With the integration of physics-based algorithms with real-time data, drilling operations can be optimized to improve efficiency and maintain consistency. These advanced algorithms enhance downhole insights and enable precise predictions, supporting informed, data-driven decisions. The LOGIX® drilling performance optimizer manages optimal drilling parameters to navigate difficult formations and maintain optimal bit-rock engagement to achieve consistent penetration rates toward the final well depth.

Challenges

Deep gas unconventional reservoirs require the operator to drill a monobore vertical-curve-lateral (VCL) profile. The vertical interval is known for crossing multiple layers of heterogeneous formations characterized by shale, carbonate, and anhydrite. The aggressive transitions in formation hardness can cause unexpected downhole vibration, reduced bit life, and drill string damage. This can lead to unplanned bit trips to the surface. 

The combination of parameters, such as weight on bit (WOB), surface RPM, and flow rate are crucial to prevent trips for failure and maintain reliable and consistent performance. These parameters often rely on human-generated generic roadmaps or the expertise of drillers. This makes it unfeasible to consistently sustain large-scale drilling operations. Relying solely on such traditional methods makes it difficult to identify the cause of reduced performance. This often results in incorrect decisions that can cause non-productive time—either continuing to drill when it is crucial to trip out or tripping unnecessarily when drilling could continue.

Solution

The operator employed the LOGIX drilling performance optimizer that uses advanced machine learning algorithms to auto-calibrate bit-rock interaction signature and calculate optimal drilling efficiency zones while drilling. This provides automated drilling parameters to increase drilling efficiency, maximize ROP, and automatically provide drilling dysfunction alerts to remote operations engineers.

Results

The LOGIX drilling performance optimizer calculated the bit depth-of-cut (DoC) and operational efficiency in real time and indicated a degradation in drilling efficiency. To restore the ROP performance, the driller increased the WOB by more than twice in magnitude without success. The algorithm determined the drastic reduction in drilling performance indicators was due to bit wear/damage. The bit-rock interaction signature uncovered a sudden loss in DoC from an average of 6 mm to 1 mm over 40 m drilled. 

The real-time data alerts notified the operator of abrupt bit integrity damage. A data-driven decision was made to trip out of the hole and reduce the impact on the well delivery time. Additional time spent on manual data analysis to diagnose and identify the drill-bit dysfunction was avoided. 

Once the BHA was on surface, the operator confirmed the bit to be damaged, as predicted by the algorithm. This solution provided the operator with a confident and reliable data-driven decision and, as a result, reduced rig time. 

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