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<p>The oil and gas industry’s profitability and performance are largely dependent on the stability of wellbores and the efficacy of drilling operations. The key hydraulic and geomechanical factors affecting wellbore integrity and operating effectiveness are examined in this systematic review. The research follows PRISMA guidelines and uses the PICOS framework. Out of 373 publications found by a thorough search, 44 eligible studies were chosen for in-depth examination based on methodological quality, journal impact factors (median: 4.2), and citation metrics (average: 7.3 citations/year). The results show that wellbore stability is significantly impacted by hydraulic aspects including fluid dynamics, pressure management, and flow behavior as well as geomechanical characteristics such as in-situ stress, rock strength, and fracture networks. Key findings reveal that coupled hydraulic-geomechanical models improve prediction accuracy by 25–40% compared to single-parameter approaches, while field-validated numerical simulations demonstrate wellbore collapse prediction with error margins below 15%. Laboratory studies confirm that cement sheath integrity depends critically on temperature cycling and natural fracture interactions. Advanced numerical models, fluid behavior and cement integrity laboratory studies, and validation using field data are all useful approaches for researching these issues. By lowering hazards like wellbore collapse and damage from fracturing, coupled models that incorporate hydraulic and geomechanical data have the potential to improve drilling performance. Notable research gaps still exist despite tremendous advances. Complex field conditions like cyclic loading and transient thermal loads are not well replicated by many experimental configurations. Additionally, automated systems and machine learning have potential, but their integration with real-time data for predictive modeling is still underused. This study emphasizes the pressing need for creative experimental designs and multidisciplinary techniques that connect lab results with field applications. The creation of reliable models and technology to fill in these gaps should be the top priority of future research.</p>

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Coupled hydraulic-geomechanical analysis in well drilling operations: a systematic review of experimental and numerical methodologies

  • 期刊论文

  • 发表日期：

    2026年1月5日

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  • Farid Ghodusi Borujeni,
  • Masoud Sheikha Hamad,
  • Amin Sadeqian Shahi

摘要

The oil and gas industry’s profitability and performance are largely dependent on the stability of wellbores and the efficacy of drilling operations. The key hydraulic and geomechanical factors affecting wellbore integrity and operating effectiveness are examined in this systematic review. The research follows PRISMA guidelines and uses the PICOS framework. Out of 373 publications found by a thorough search, 44 eligible studies were chosen for in-depth examination based on methodological quality, journal impact factors (median: 4.2), and citation metrics (average: 7.3 citations/year). The results show that wellbore stability is significantly impacted by hydraulic aspects including fluid dynamics, pressure management, and flow behavior as well as geomechanical characteristics such as in-situ stress, rock strength, and fracture networks. Key findings reveal that coupled hydraulic-geomechanical models improve prediction accuracy by 25–40% compared to single-parameter approaches, while field-validated numerical simulations demonstrate wellbore collapse prediction with error margins below 15%. Laboratory studies confirm that cement sheath integrity depends critically on temperature cycling and natural fracture interactions. Advanced numerical models, fluid behavior and cement integrity laboratory studies, and validation using field data are all useful approaches for researching these issues. By lowering hazards like wellbore collapse and damage from fracturing, coupled models that incorporate hydraulic and geomechanical data have the potential to improve drilling performance. Notable research gaps still exist despite tremendous advances. Complex field conditions like cyclic loading and transient thermal loads are not well replicated by many experimental configurations. Additionally, automated systems and machine learning have potential, but their integration with real-time data for predictive modeling is still underused. This study emphasizes the pressing need for creative experimental designs and multidisciplinary techniques that connect lab results with field applications. The creation of reliable models and technology to fill in these gaps should be the top priority of future research.