Data-driven drilling optimization and field development in unconventionals




Zhou, Yang, Ph. D.

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Leveraging the better availability of drilling data, this dissertation explores ways and opportunities to optimize the drilling process and field development through data analytics in the Permian and Williston Basin shale fields. Unlike traditional wells, unconventional wells share similar well design, are drilled in close proximity, and require high directional execution precision. A large number of wells need to be drilled, so the relatively thin margins per well demand high efficiency and low variance operation, especially in the current low oil price environment. This study addresses these challenges in three ways, using a more accurate physics-based model, a real-time assessment/advisory system, and a value-oriented (asymmetric) optimization strategy. The first part of this dissertation presents a thorough investigation and refresh of the Mechanical Specific Energy (MSE) and Confined Compressive Strength (CCS) model, because after enjoying past success and popularity, the current MSE/CCS model has become inadequate for modern unconventional drilling. It omits some important, well-known components and corrections, which are essential in today’s operations. Though it has been powerful and popular in the past, its completeness and physical validity have always being questionable. In addition, the popularization of the mud motor (PDM), which is powered by hydraulic energy, now magnifies these problems. The classic MSE equation badly needs an update to suit the needs in current unconventional drilling. This study proposes a new two-tier system of surface and downhole MSE to address these issues; it also examines all components, corrections, and factors known to affect MSE/CCS with data from the Bakken and the Permian fields, providing an updated iteration with more scientific rigor and physical justification. The second part of this dissertation conducts a case study of tracking and removing Non-Productive Time (NPT) and Invisible Lost Time (ILT), leveraging data analytics techniques with a value-oriented asymmetric approach. Process discretization is introduced to transform the continuous drilling process into individual standardized tasks to expose NPT and ILT buried in the process. Best practices are identified through rig-to-rig comparison and then implemented to the entire fleet to scale up the benefit. Furthermore, to overcome the potential limitation/risk posed by relying on an individual flagship rig as a benchmark, a dedicated test rig/digital twining system is recommended to test and push technical limits. After defines technical limit in a systematic manner, the author proposes future plans to develop thoughtful, robust, implementable, and sustainable Standard Operating Procedures (OPS) for each individual task


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