Technology Update: Big Data for Advanced Well Engineering Holds Strong Potential To Optimize Drilling Costs
- Nicola Rossi (kwantis) | Jean Michelez (kwantis) | Fabio Concina (kwantis)
- Document ID
- Society of Petroleum Engineers
- Journal of Petroleum Technology
- Publication Date
- February 2018
- Document Type
- Journal Paper
- 18 - 21
- 2018. Copyright is retained by the author. This document is distributed by SPE with the permission of the author. Contact the author for permission to use material from this document.
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Considering the significant weight of drilling costs in upstream ventures, saving even a few hours of drilling could lead to substantial cost savings on the overall capital expenditures (Capex). Thanks to the big data revolution, cost optimization still has strong potential in drilling operations.
Traditionally, drilling performance has been addressed through the analysis of drilling reports written once a day by the operator’s staff. However, high-frequency data are already collected continuously by surface and downhole sensors to address operational safety and continuity. These data contain a huge amount of valuable information that can be used for drilling operations performance analysis (Fig. 1).
High-frequency data analysis allows a new level of performance monitoring through the identification of potential invisible lost time and the anticipation of well problems that could be minimized to reduce drilling costs.
Data Valorization, Combination
More than 60 sensors record high-frequency data during drilling, creating sets of millions of data for a single well. These data can be reprocessed through a set of algorithms to identify each single activity with the highest possible level of accuracy and granularity. As an example, reaming is based on the following seven surface logging parameters: bit depth, well depth, rate of penetration (ROP), weight on bit (WOB), torque, mud flow-in, and standpipe pressure.
However not all activities can be automatically detected through surface log-data interpretation. It they cannot be, daily reporting becomes a relevant source of information. Thus, a set of preeminence rules is applied so that the most relevant data can be used and combined as needed at any time to build a complete and precise time breakdown for the analysis of drilling and flat time.
This innovative approach is the basis of the Integrated Drilling Data Discovery (ID3) system, developed by kwantis. ID3 manages one single big-data platform to integrate surface logging data with reporting information (e.g., daily drilling reports and bit reports) and measures lithology, trajectories, and other key data while drilling to create a complete set of drilling performance analyses.
The system is able to break down the drilling sequence to the most detailed activities (e.g., bit on bottom, reaming, circulating, or on slip) with a 5-seconds accuracy. The information regarding phases, troubles, and equipment are provided by the daily reporting and plotted on the common time or depth bases to create meaningful analytics on a single well or group of offset wells.
The applications of these analyses are multiple and provide new capabilities at any stage of the well life cycle: planning, operating, or post-well analysis.
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