Collaborative Approach Optimizes Automated-Drilling Performance Measurement
Authors: Carpenter Chris 
Published: November 2022

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This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 31579, “A Successful Case Study of a Collaborative Approach in Operational Optimization Through Adoption of Automated-Drilling-Performance Measurement,” by Muhammad Afiq Arif Normin, TDE Group, and Azlesham Rosli and Meor M.H.M. Hashim, SPE, Petronas, et al. The paper has not been peer reviewed. Copyright 2022 Offshore Technology Conference. Reproduced by permission.

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This paper discusses the strategy devised, the approach implemented, and the challenges experienced in the adoption and implementation of an automated drilling-performance-measurement (ADPM) system to be used onsite and remotely. The stages of the optimization of the ADPM system are broken down into prespud operations, operations, and post-well analysis. The successful result of this deployment has seen total actual savings of 2.94 days throughout the campaign. From 2016 to 2021, the operator gained 39.03 days of actual savings for their rig fleet.

The ADPM Tool

The ADPM tool was introduced in 2016 as part of the operator’s digitalization effort. The tool was integrated into operations at the Wells Real-Time Center (WRTC) and was managed by a wells technical application champion (WTAC). With this integration in place, the operator aimed to deliver all wells, whether vertical, horizontal, or extended-reach, in the most consistent and efficient way without jeopardizing the operation’s safety. To ensure the investment bore dividends, this initiative required a major collaborative effort.

The tool’s main input is the rig’s real-time surface data obtained by mud-logging sensors. The real-time data are transmitted to the WRTC server in Kuala Lumpur and stored. Access to the data is provided to the ADPM tool to extract the necessary data required for rig-state detection.

The secondary input for the ADPM tool is the daily drilling report (DDR) prepared by drilling supervisors at the rig site. The extraction of information from the DDRs serves two purposes: to provide information on the activity performed on the surface not detected by rig-state detection and to provide a reference for ADPM personnel during data processing and quality control. Each activity code and subcode associated with the DDR also is integrated into the ADPM database. Merging both data has provided more insight to the project team because a comparison between the activity reported in the DDRs and the activity detected by the ADPM can be highlighted.

During the initial phase of ADPM deployment, a list of the key performance indicators (KPIs) was defined and established by the WTAC. This set of KPIs covered the tripping, drilling, and casing and liner operations necessary to determine invisible lost time (ILT). For tripping operations, the critical KPIs are slip time, tripping-in speed, and tripping-out speed. All KPIs are differentiated as having been performed in cased hole or open hole.

Next is on-bottom drilling, with five focused KPIs. Finally, three KPIs were dedicated to the casing and liner operations: slips time, average pipe moving speed, and gross running rate. As with tripping operations, all KPIs were differentiated as having been performed in cased hole or open hole except slips time. All KPIs were computed from a single data source (the real-time data).