Challenging drilling applications and fluctuating oil prices have created a new emphasis on developing innovative technology to enhance safety and reduce cost. During drilling operations, an estimate of the rock strength is usually derived from monitoring downhole drilling forces. Recent advances in downhole measurement technologies allow for accurately estimating these forces at the drill bit, thus reconstructing the rock strength along the well as a 1D-profile. This dissertation presents a novel in-cutter force sensing technology to measure the time evolution of interaction forces at the scale of individual cutters by utilizing a scaled drilling rig. In the first series of drilling tests, rock samples were prepared as homogeneous blocks to assess the average rock strength within the block compared to the rock strength obtained from standard tests. In the second series of drilling tests, layers of gypsum of distinct strengths were prepared with the interface between them consisting of a bedding plane to detect heterogeneities and anisotropies and reconstruct the rock sample in 3D based on the rock strength. The high-frequency force measurements at the cutter are evaluated to assess the wear state and to differentiate the applied forces from the drill bit to the cutter scale.
An artificial neural network (ANN) model utilizes the in-cutter sensing data and the scaled drilling rig sensors to predict the rock strength and rate of penetration. The model employs the acquired data, derived variables, and mechanical properties of the rock samples to conduct the prediction. A scoring mechanism evaluates the drilling efficiency by coupling the vibration modes and the mechanical specific energy.
Finally, a data-driven physics-based drilling monitoring algorithm is developed to utilize actual drilling data and conduct semi-automated data quality control. The system provides feedback regarding operations recognition, drilling mode, and mud motor performance. A dynamic drilling simulator is then implemented to recreate the drilling process by considering appropriate physical models combined with rock properties across the entire well or any given section.
|Date of Award||Feb 2023|
|Original language||English (US)|
- Physical Sciences and Engineering
|Supervisor||Shehab Ahmed (Supervisor)|
- In-cutter sensing
- Rock-cutter interaction
- Rock strength
- Drilling technology
- Artificial neural network