Measuring Drilling Standardization Using Approximate String Matching

Abstract

Standardization of drilling procedures is a key element for success in the development of unconventional oil and gas fields today. We propose approximate string matching as a data analytic technique that can be used to measure standardization across wells in a field. We describe the implementation of approximate string matching, providing details of how it can be contextualized for oil and gas drilling using operation codes, and how results can be normalized to allow for comparison between disproportionate sequences of operations. We use this technique to develop a measure for operational variability, the first objective metric for evaluating and comparing the rate and consistency of standardization across different wells, sections of wells, and rigs, informing decision-making based on the existing large streams of feedback from field operations. We provide examples of operational variability using analysis of data from about two hundred horizontal wells drilled by a single operator in a major North American unconventional field, and develop the concept of the "standardization curve" from this to demonstrate the importance of this metric in understanding learning and movement along the "learning curve" during a drilling campaign. Additionally, we outline some specific ways that these approaches can be used to automate the data-driven comparison, disaggregation, and assimilation of field learning to better manage improvement within drilling campaigns. This tool provides a foundation upon which future data analytic and machine learning techniques can build, developing learning programs for "smart" oil and gas fields that make better use of available data to enable rapid adaptation and greater overall drilling efficiency.