In 1987, the IADC adopted an improved Rock Bit Dull Grading System. Although it was modeled after the previously used T-B-G System, it was expanded to provide a much clearer "mental picture" of a dull bit. This paper documents the changes from the 1987 version to the 1992 IADC Dull Grading System. Since the changes are minor, they are listed at the beginning of the paper. Additionally, for those needing more detail, the entire 1992 IADC Dull Grading System is presented. CHANGES FROM 1987 * Outer Cutting Structure: Definition will change to "All cutting elements that touch the side of the hole".*Reasons Pulled Add: "LIH" - for "Left in Hole"*Bearing Grade Add: "N" - for "Not Able to Grade"*Location: "G" represents gage area, replacing "H".*IADC bit topics also updated in 1992 include IADC Classification and Fixed Cutter Dull Grading. Introduction The use of dull bit evaluation methods represents a key step in the advance of rock bit technology. Historically, a driller would learn through experience how to examine a "dull" to determine what type of bit to run next, and how it should be run (WOB, RPM, etc.). This was part of the art that separated the best drillers from the rest. An industry wide effort began years ago to teach the art of bit wear analysis to a broader range of personnel so that dull bit evaluation would become an integral part of daily drilling practice. General guidelines were established in the mid-1950's for relating typical bit wear patterns to the possible causes and remedies. This approach was helpful but limited by the lack of a common vocabulary for describing bit wear and documenting the dull condition in drilling reports. Meanwhile, basic bit performance studies produced a greater appreciation for the economic impact of bit wear and its close relationship to bit selection and operating practices. An industry standard for reporting bit wear was clearly needed. The Weight/Speed/Penetration Sub-Committee of the American Association of Drilling Contractors (AAODC) established the first dull grading standard in 1961. P. 819^

The purpose of this paper is to demonstrate the power and business benefits of leveraging online analytical processing (OLAP) cubes in the utilization of high-level data analytics and data dashboards from an established drilling record system (DRS). The DRS contains over 1.4 million wells, including 75,000 offshore wells drilled worldwide since 1980 with nearly 5 million total bottomhole assembly (BHA) runs from over 100 countries. Since 2009, over 1.5 million BHA runs drilling 2.6 billion feet of formation have been captured. Being able to visualize and understand the drilling data allows for increased efficiencies, reducing the days on wells for operators from deepwater to inland barge and land drilling worldwide. The development of the OLAP cubes required a multidisciplinary team consisting of software developers, business managers, domain champions, field-based engineers, and data scientists. The OLAP cubes consist of multidimensional databases built from relational and algorithmic interpretations of DRS transaction data. These algorithms are generated and developed by an iterative cycle of continuous improvement, development, and utilization of the OLAP cubes in parallel to improve the functionality and business impact for performance analysis, sales, product development, product reliability, and marketing. The data can be analyzed and visualized in the Microsoft Office suite by directly querying the DRS OLAP cubes. This also allows for dashboards to be updated in real time as data are added to DRS. OLAP cubes have been developed to analyze the performance of drill bits, motors, reamers, rotary steerable tools, and many more downhole tools. The DRS cubes assist in identifying failure causes on bits to identify high-risk intervals to better target products and parameters to reduce costly nonproductive time. Fit-for-purpose OLAP cubes have been developed to understand drilling efficiencies and strategies in multibit versus single-bit sections using variable trip speeds and field performance. Traditional business reports were made more efficient and auto-updated and dashboards were built to identify major business trends to equip business managers. This OLAP cube development has allowed for increased usage of the world's largest drilling record database and has made it easier to access and analyze the data. Ultimately, the techniques and development described in this paper help answer business questions to make better business decisions through data-driven analytics.

Schlumberger, one of the world’s leading suppliers of oilfield technology, is a measurement and data-driven company that collects massive amounts of data in the course of its daily operations. These data, diverse in nature, are collected for use in various business and technical workflows. The data can be downhole, surface, post-analysis, and support functions from manufacturing, maintenance, asset management, and finance. Analysis of this Big Data has the potential to drive a step change in operational performance across multiple dimensions. However, accomplishing this step change is not easy to accomplish because often, the data are not well structured and are scattered across individual business systems that do not communicate well with each other. Most of the analysis of these scattered data occurs on a point basis, requiring the significant involvement of various experts and complex time-consuming manipulations. The results are short lived in that they cannot be tracked in real time and the effort expended is not applicable to other data sets or problems. Increasing data volumes, data diversity, and demand from engineers to record multiple new data attributes during the product or technology life cycle further limits the benefits of such a spot analytics process, with potentially severe impacts on the business due to inadequate decision support or missed opportunities. This paper presents a developmental model and change processes, challenges faced and resolution approaches leading to digital transformation, and finally, the resulting value creation through building data visualizations and comprehensible decision-making tools. Once the initial high-value data sets and visualizations are identified, automation opportunities can be exploited. These data sets become the foundation for predictive analysis and machine learning through artificial intelligence (AI) and Internet of things (IoT) to further influence product performance and development in support of customer needs.

The IADC (International Association of Drilling Contractors) roller bit classification system provides a convenient method for categorizing rock bitsaccording to their design features and intended applications. The system has been used worldwide since 1972. The 1992 version includes improvements which reflect the latest design trends in roller cone bits. This paper explains the IADC roller bit classification code. Current classification charts are shown for major bit manufacturers. The relationship between the IADC code, bit design factors, and operating guidelines is discussed. Examples depict the major design features included in the code. Since this version contains only minor changes from the 1987 Roller Bit Classification System, these changes will be listed at the beginning of the paper. Those experienced with the code need only read the changes. Subsequent paragraphs present a complete discussion of the classification system for the benefit of the readers who may not be familiar with the system or who wish more detail. IADC bit topics also updated in 1992 include Fixed Cutter Classification and Dull Bit Grading. CLASSIFICATION CHANGES In 1987, the Classification System was changed to include a fourth character to denote features available. The 1992 revision made the following changes to list of "Features Available". B - Special Bearing Seal - Added H - Horizontal/Steering Application - Added L - Lug Pads - Added M - Motor Application - Added T - Two Cone Bits - Added W - Enhanced Cutting Structure - Added R - Reinforced Welds - Deleted Introduction Hundreds of roller cone bit designs are commercially available for oilfield use. Four major manufacturers market competitive product lines of 1000 or more distinctly different bits. Five or more smaller manufacturers produce at least 100 additional bits. Why are there so many roller bit designs? P. 801

The Bit Record Database and analysis program discussed in this paper were developed to provide rapid access to and analysis of bit run information. The system was developed in three phases: