Design of an Automated Drilling-Prediction System
- Chris Carpenter (JPT Technology Editor)
- Document ID
- Society of Petroleum Engineers
- Journal of Petroleum Technology
- Publication Date
- September 2013
- Document Type
- Journal Paper
- 147 - 148
- 2013. Society of Petroleum Engineers
- 2 in the last 30 days
- 151 since 2007
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This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 163709, "Design of an Automated Drilling-Prediction System - Strengthening-While-Drilling Decision Making," by Samuel R. Perez Bardasz, SPE, Edwin David Hernandez Alejadre, and Armando Almeida Leon, Petrolink, prepared for the 2013 SPE Digital Energy Conference and Exhibition, The Woodlands, Texas, USA, 5-7 March. The paper has not been peer reviewed.
Data-mining processes are fundamental in obtaining the predictive benefits of real-time systems and have been progressing from descriptive to predictive optimization methods. These methods are enhanced by real-time and historic data. Advanced sensor technologies, improved data-quality control, wellsite information-transfer standard-markup-language (WITSML) data advantages, and virtual real-time drilling-optimization concepts have been assimilated into the design and implementation of prediction systems.
As technologies evolve and the WITSML standard allows data exploitation by many specialized applications, more-accurate and reliable drilling data are available at real-time operation centers (RTOCs) to analyze and mitigate drilling issues. This enhances and speeds up the drilling-optimization process, and allows a small group of highly skilled drilling engineers to support several wellbore constructions simultaneously.
However, the traditional tasks of monitoring drilling parameters are still constrained by the constant need for human intervention. First, the particular field-operations knowledge gained by RTOC monitoring engineers is very valuable but fragile, because it requires the continued participation of team members. To ensure that nothing is overlooked, that knowledge should be gathered and used by an intelligent system. Second, the status of a particular event or well is constantly changing as key drilling factors change, and monitoring engineers must review all data in detail before manually defining the new status of a system. Third, a complete update of a general well-operations status report is time consuming. The operations status for a set of wells being drilled and monitored can change dramatically from one minute to the next and therefore requires the constant participation of an engineer. Such a report should be automated to de-rive maximum benefit from the best real-time and historic data.
The drilling industry is aware of the importance of pattern analysis and past performance of correlation wells. It has looked to similar drilling-well experiences to predict the probability of a particular event or drilling outcome. This has been achieved effectively with human intervention, despite the fact that multiple data families that needed to be taken into account were difficult to access for different reasons.
|File Size||83 KB||Number of Pages||2|