​Abstract: Drilling is crucial to many industries, including hydrocarbon extraction, CO₂ sequestration, geothermal energy, and others. During penetrating the subsurface rocks, drilling fluid (mud) is used for drilling bit cooling, lubrication, removing rock cuttings, and providing wellbore mechanical stability. Significant mud loss from the wellbore into the surrounding formation causes fluid lost-circulation incidents. This phenomenon leads to cost overrun, environmental pollution, delays project time, and causes safety issues. Although lost-circulation exacerbates wellbore conditions, prediction of the characteristics of subsurface formations can be obtained. Generally, four formation types cause lost-circulation: natural fractures, and induced fractures, vugs and caves, and porous/permeable medium. The focus of this work is on naturally fractured formations, which is the most common cause of lost circulation. In this work, a novel prediction tool is developed based on analytical solutions and type-curves (TC). Type-curves are derived from the Cauchy equation of motion and mass conservation for non-Newtonian fluid model, corresponding to Herschel-Bulkley model (HB). Experimental setup from literature mimicking a deformed fracture supports the establishment of the tool. Upscaling the model of a natural fracture at subsurface conditions is implemented into the equations to achieve a group of modified type-curves (MTC) alongside another set of derivative-based type-curves (DMTC). The developed approach is verified with numerical simulations. Further, verification is performed with other analytical solutions. This proposed tool serves various functionalities; It predicts the volume loss as a function of time, based on wellbore operating conditions. The time-dependent fluid loss penetration from the wellbore into the surrounding formation can be computed. Additionally, the hydraulic aperture of the fracture in the surrounding formation can be estimated. Due to the non-Newtonian behavior of the drilling mud, the tool can be used to assess the fluid loss stopping time. Validation of the tool is performed by using actual field datasets and published experimental measurements. Machine-Learning is finally investigated as a complementary approach to determine the flow behavior of mud loss and the corresponding fracture properties.

Zoom Meeting https://kaust.zoom.us/j/99757086778

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• CONTACT Rami Albattat
• EMAIL Rami.Albattat@kaust.edu.sa