Summary
State-of-the-art drilling-operations analysis is mostly dependent on
existing daily-activity reporting. However, these activity reports are based on
human observations and judgment. This fact implies a number of limitations such
as the coarse level of detail and subjective coding systems. To overcome these
problems, a rule-based system has been applied to analyze real-time
surface-sensor data autonomously. The system evaluates the sensor data stream
and acquires crucial process information as a basis for further analysis.
The scope of the system is the recognition of drilling operations, such as
tripping, making connections, reaming, and washing, to extend and enhance
standard reporting. In this way, a standardized and objective categorization of
the drilling process can be achieved at a level of accuracy and detail yet to
be reached.
Another benefit is the automated reporting feature. Through the recognition
of the rigs, current state, the system is able to propose an impartial process
description. This leads to a reduction of the time spent on reporting and
leaves more time to focus on unexpected events and lessons learned.
Analysis of field data allowed the introduction of new key performance
indicators (e.g., wellbore treatment time per depth interval) for benchmarking,
which are determined automatically during the evaluation process. This type of
benchmarking does not rely on company-specific activity coding systems. In this
way, costly and time-consuming data management effort (e.g., to compare
operated and nonoperated wells) is eliminated.
The new system was applied to wells drilled in the Vienna basin during the
past year. The Vienna basin covers large parts of eastern Austria (lower
Austria, Vienna, and Burgenland) and reaches into the territories of the Czech
Republic in the north and the Slovak Republic in the east. It is approximately
200 km long and 55 km wide, striking roughly southwest to northeast from
Gloggnitz (lower Austria) in the south to southwest to Napajedla (Czech
Republic) in the north to northeast.
As a conclusion, it can be stated that the application of this system
significantly improves the accuracy and resolution of the drilling-process
description, reducing data-management efforts. The objective categorization of
process information is a key enabler for benchmarking, specifically when
identifying hidden lost time.
Introduction
Most papers discussing drilling-activity performance analysis start with
three simple questions (Bond et al. 1998; Iyoho et al. 2004; Adeleye et al.
2004): What is the current level of performance, what is the benchmark, and how
can this gap be closed?
To improve performance, all of these questions have to be answered properly.
As already discussed by Thonhauser (2004), the basis for most
drilling-performance analysis work performed is the daily activity breakdown
with all its drawbacks:
-
Analysis based on subjective human
observations
-
Coarse level of detail
-
Time-consuming data-entry and
quality-control processes
In addition to these drawbacks, the current available personnel numbers and
demographics in the petroleum industry leave very little room for extended
analysis because of very tight time schedules. Experienced drilling engineers
often do not have the required time to do proper analyses, while young,
recently graduated engineers do not have the experience and knowledge to do so.
Bond et al. (1998) stated: “The exercise of extracting removable time analysis
was very time consuming (up to 2 man months for the eight wells reviewed) and
required a high level of drilling/completion knowledge.”
Operating companies often try to reduce the cost of analysis by negotiating
performance-driven contracts with drilling contractors. However, they often
lack the means to subsequently evaluate and benchmark the service they get. If
an ananysis is performed, the money spent on QC, performance analysis, and
benchmarking is substantial and is often not worth the investment effort.
Well Time
The time spent to drill a well is typically longer than what is technically
possible. This is a result of down times. In Fig. 1, a refined graphical
representation of Bond et al. (1998) is presented. The total lost time consists
of two parts, the identified and the invisible lost time. Identified lost time
consists of incidents that are reported in the drilling reports. An example
would be “waiting for equipment.” The invisible lost time is defined as time
when the operations are not running at the technical limit. An example is the
driller performing excessive reaming after each stand because of lack of
experience.
In Bond et al. (1996), they estimate that approximately 40 to 60% of the
total time must be accounted to the removable time. Comparing these numbers
with other performance-improvement attempts, focusing on the identified lost
time, where time reductions of 10 to 30 % are reported (Adeleye et al. 2004;
Kadaster et al. 1992; Millheim et al. 1998), the potential invisible lost time
accounts for approximately half of the removable time. This results in 20 to
30% of potential invisible lost time to be identified by the presented
approach.
As mentioned, the identified lost time has already been addressed in several
publications. State-of-the-art drilling-performance optimization uses the
reported operations to work with, and is successful in doing so. However, it is
only possible to identify only approximately half of the lost time, because the
analysis is based on reported data with their coarse level of detail.
To go beyond these limitations, it is necessary to gather more details about
the process being optimized.
© 2007. Society of Petroleum Engineers
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History
- Original manuscript received:
19 January 2006
- Revised manuscript received:
2 April 2007
- Manuscript approved:
25 April 2007
- Version of record:
20 September 2007
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