Case Study : Optimizing ERW with StarSteer and DrillSpot
Introduction:
Extended Reach Wells (ERW) presents significant challenges due to complex wellbore trajectories, local dip variations, and the stratigraphic uncertainty of subsurface formations. This study discusses a methodology for optimizing ERW drilling by integrating advanced geosteering techniques using StarSteer and real-time drilling parameter analysis with DrillSpot. These approaches enhance decision-making, reduce geological uncertainty, and improve overall operational performance.
Key Technologies and Methodology:
- Geosteering Techniques: There are various geosteering techniques available in the market, and the application of these methods depends on the resources at hand. For the case study, techniques based on modeling and stratigraphy were applied by integrating StarSteer, utilizing conventional logs such as gamma ray and resistivity. This approach enabled a more accurate interpretation of the geological formations encountered, allowing for real-time adjustments to the wellbore trajectory and ensuring optimal placement within the target zone.
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Drilling Parameters Integration: The case study integrates geological interpretation with the drilling parameters of previously drilled wells, using DrillSpot, through machine learning, rescales the drilling parameter logs of nearby wells to match the interpreted geology (in StarSteer) of the well being drilled. These parameters are compared with those of the current well, and this integration provides the optimal parameters to achieve the highest rate of penetration (ROP).
Workflow:
The implemented workflow reduces geological uncertainty by integrating geosciences, drilling parameters, and analytics. It continuously updates the geological model on the StarSteer, allowing the DrillSpot to auto-correlate parameters based on the drilled stratigraphy. This integration ensures effective coordination between geological data and drilling operations.
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Case Study:
An offshore extended reach well was drilled, reaching a depth of 4388 meters (MD) with a ratio of 2.55 MD/TVD. The advanced geosteering and parameter monitoring resulted in optimized drilling performance, including the identification of the target with a minimal error margin and precise setting of casing depths.
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Results:
The well successfully landed at the top of the target with a margin of error of 2 meters, and geosteering positioned it 29 m below the top and 7 m above the base, achieving 100% effectiveness within the geological target. The well reached a total depth of 4388 m, with an average apparent upward dip of 0.9 degrees over a lateral section of 688 m. During drilling, real-time geological data were integrated with drilling parameters thanks to the collaborative environment between StarSteer and DrillSpot , providing optimal parameters to enhance drilling performance.
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Conclusion:
The integration of geosteering with drilling parameters using StarSteer and DrillSpot has significantly reduced operational uncertainties in highly deviated wells. The real-time adaptability and data-driven approach improved the decision-making process, setting a new benchmark in well drilling optimization.
Acknowledgments:
The authors would like to thank Petróleos Mexicanos (PEMEX) and Rogii Inc for their valuable contribution to the development of this work.
