Rigorous Drilling-Nonproductive-Time Determination and Eliminating Invisible Lost Time

Abstract

This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 138804, ’Rigorous Drilling- Nonproductive-Time Determination and Elimination of Invisible Lost Time: Theory and Case Histories,’ by Eric Maidla, SPE, and William Maidla, SPE, TDE Petroleum Data Solutions, prepared for the 2010 SPE Latin American & Caribbean Petroleum Engineering Conference, Lima, Peru, 1-3 December. The paper has not been peer reviewed. Measuring techniques that involve data-quality control (QC) and automatic detection of routine drilling operations are available in modern drilling programs. Implementation was carried out in an onshore area in which a series of similar wells was drilled. Measurement accuracy, training, and development of new work processes that were implemented successfully and these led to major key-performance-indicator (KPI) time savings of 31 to 43%. Introduction Past performance of drilling and completing wells was analyzed to capture learnings, which then were applied on the next well or well campaign. The process relied on running rigorous QC on the drilling morning-report data and correlating those events with other well data from electric logs, geology, and recorded drilling data. Leadership is critical, whether through internal management or through specialized consultants who understand how to motivate groups to excel. Even when implemented properly, many approaches tend to be cyclic, and keeping the processes in effect has proved to be difficult with the processes breaking down when certain elements of the group leave. In most cases, improvements of the drilling performance of routine drilling operations were very limited until a change occurred on the rig, regardless of how many meetings and workshops were carried out away from the rig, and even if some rig personnel were involved. Only when the driller was engaged did the final results improve. The process presented by the authors focuses on the driller. The proposal is to provide this individual or group with all the information and support necessary to allow the drilling-rig group to achieve the required safety practices that come from understanding how to perform the job properly and in a consistent manner. Automatic Operations Detection The work used automatic operations detection of the routine drilling operations and was the basis of collecting real-time (or near-real-time) data to determine the rig status. The key was to have an independent data-QC process performed by a company other than the data provider, thus ensuring no conflict of goals or interests. Following the QC process, the data were classified and processed and a rig state was assigned to each time interval (e.g., drilling—rotary, drilling—sliding, and reaming and washing upward). These rig states were the building blocks for all classifications and reports per-formed throughout this study.