Drilling costs occupy a significant portion of oil and gas project’s budget. Optimization of drilling - increasing speed, reducing vibrations, and minimizing borehole instability - can lead to significant savings and hence have been extensively studied. Currently, most drilling optimization tools (or models) only tackle a single drilling metric: they seek to optimize either the rate of penetration (ROP), torque on bit (TOB), mechanical specific energy (MSE) or drilling vibrations. Models are often built independent of other metrics (without coupling) and do not accurately represent downhole conditions since drilling metrics are interrelated. This may lead to over or underestimation of the metric optimized which can severely reduce the effect of optimization. The objective of this dissertation is to introduce techniques, strategies, and algorithms that can be used to build a fully coupled drilling optimization model. Drilling optimization is studied by first optimizing ROP– where models for ROP prediction and inference are constructed using machine learning. Strategies and algorithms for determining optimal drilling parameters using ROP models are discussed. The unique problem posed by data-driven models are solved using meta-heuristic algorithms. A coupled model is constructed by building ROP, TOB, and MSE models conjointly using the random forests algorithm. Drilling vibrations – axial, lateral, and torsional – are modeled using a machine learning classification algorithm. This classification algorithm used to restrict the optimization space, ensuring that optimal parameters do not induce vibrations ahead of the bit. This model is used to investigate the effect of optimizing ROP and MSE on field data. A workflow is introduced linking all the aforementioned models into an end-to-end drilling optimization tool. The tool can be used as a recommendation system where field-measured data are used to determine and implement optimal drilling parameters ahead of the bit. The dissertation illustrates the use of statistical (or machine) learning techniques to address the problems encountered in drilling optimizationPetroleum and Geosystems Engineerin
