TL Methodology Contributes to the Success of Oil/Gas Projects

To maximize the use of engineering tools in addition to expert judgment when setting the Technical Limit (TL) for a given operation, a methodology has been introduced to enable the success of oil and gas projects.

The paper “Integrated Operation Performance and Optimization Analysis Based on Technical Limit and Invisible Lost Time,” by Freddy José Márquez, EPRA Consultores, and Ranses Guillermo Sandrea, Opex, describes a methodology aimed to define a TL based on specific conditions of the well, to identify not only the invisible lost time (ILT) from operational performance, but also identify imprecisions on the daily operation reports (DOR), the program and well design.

Background

Technical Limit (TL) and ILT are concepts that have contributed to the success of oil and gas projects around the world beginning in the late 1990s, many of which have been documented and presented at international conferences. The authors agree that TL represents the optimum time for a given operation, based on statistical analysis or operation team commitment. There is no doubt that defining TL could be challenging when unique activities are programmed or there is a lack of offset data — in these situations the estimated TL may be biased.

To present a better perspective, the paper defines that ILT is the difference between the productive time and TL. This value allows the estimation of efficiency. Identified invisible lost time (IILT) is the ILT portion that can be measured with real time data and/or DOR. The other portion is defined as unidentified ILT (UILT). In this paper, IILT and UILT were estimated to productive and non- productive time (NPT). In addition, geological correlation was used to estimate ILT derived from drilling performance.

Context

Over 20 years ago, Bond et al. (1996) presented a well construction process idea that disrupted the way oil companies planned and executed drilling and completion projects. According to the paper, Bond proposed and defined TL as the "best possible" time for a given set of design parameters. He also introduced the concept of ILT, defined as the time taken to perform those activities included in a "normal" well but excluded on a theoretical well at which each operation is considered to achieve its best performance. The advantage is that TL allows one to estimate the operational efficiency and identify opportunities for improvement even when a well is constructed problem-free. TL is considered the foundation for different technical analyses presented in posts and papers from different companies that document importance of time and cost-saving achievements in international projects. Most of the literature consulted for the elaboration of this paper considers the use of statistics and drilling teams’ commitments to set the TL, which could lead to bias.

Data Analytics and Visualization

TL philosophy has demonstrated that processes can be improved by adopting systematic procedures, and when combined with technology, the results can be much more forceful. For instance, Maidla et al. (2010) use surface sensors data to improve drilling connection performance based on ILT, while Spoerker et al. (2011) presented a similar analysis but considering pipe tripping connections and casing connections. Lakhanpal et al. (2017) applied advanced signal reconstruction concepts to surface sensor data to calculate non-productive time (NPT) and ILT. In addition, Shamsi et al. (2018) and Torres et al. (2019) implemented the TL philosophy and used dashboards as a data visualization tool to communicate the results and explore the data to maximize its value. Ouahrani et al. (2018) presented an outstanding paper that demonstrates the value of combining TL philosophy with advanced data analysis and data visualization in real time.

Machine Learning

According to the report, Coley et al. (2019) presented a paper that focuses on the development of a generalizable rig state engine based on the application of supervised ML classifiers to identify rig state. The model allowed the prediction of the current operation or rig state to derive metrics, Key Performance Indicators (KPI) and ILT, which were stored alongside the original real time and contextual data. Mora et al. (2021) compared different approaches, based on TL defined by experience, statistics, and ML, to measure ILT in drilling connections and point out the advantages and disadvantages of each method.

The Challenge of Defining the Technical Limit

There are challenges and risks. For instance, there is the risk that completion and drilling teams may perceive that TL is neither realistic nor achievable, which has been documented in different papers. For example, Gallagher et al. (2005) noted that it was difficult to convince crews that TL does not mean "rush-rush," so careful attention must be paid to implementation. Also, there is a fine line between loading someone up until they are stretched and setting unrealistic goals through overloading. Therefore, it is critically important to set goals with the right amount of stretch to challenge and to motivate employees without over-stressing. Shamsi et al. and Torres et al. identified it as challenging to obtain alignment across the different teams and overcome the resistance to change. Shamsi et al. also considered this condition a risk to shortcuts and potential trigger for HSE (Health, Safety and Environment) incidents or well integrity issues; hence, the ILT target was defined in agreement with all the crews and service companies based on the ideas proposed in ILT workshops.

The paper goes on to explain that to identify ILT related to performance the TL should be defined considering the current conditions as much as possible. Otherwise, it may be a situation where it is difficult to determine if inefficiency is due to performance or technology.

The engineering team should set up a TL that allows to identify, representative ILT to make improvement plans for future wells. During this time, strategic and stretch goals can be communicated to the team to ensure that they are perceived as realistic. In other words, set an engineering target and a performance target. Management must provide the resources to achieve the goals and promote a "no blame culture" as recommended by Bond et al. (1996) at the beginning of the TL implementation.

Methodology

Define the Technical Limit

The methodology suggests that the first step is to define the operation sequence, followed by defining a TL for each activity on that sequence based on statistics, engineering, or expert judgment. The report recommends that whenever possible, engineering should be used to estimate the TL under current conditions. For instance, hydraulic surge analysis will provide an optimum block velocity while tripping, and lag time will be a reference to define the time needed to clean the hole based on depth. In addition, hydraulics can be used to estimate the optimum pumping time to keep the pressure under rig specifications, as well as quantitatively estimating the value of having an accurate pore pressure prediction, accurate hydraulics models, cement tests, and others.

Estimating the Invisible Lost Time

It is important to mention that the estimation of Invisible Lost Time in this paper, ILT, is referred to the difference between the TL defined on the planning phase and the actual productive time. Moreover, ILT is calculated for routine operations during NPT, such as bit trips, making and breaking bottom hole assemblies (BHA), among others. The paper reports that if real-time data is available, it can be used to identify sources of inefficiency by measuring ILT on drilling connections, time to pull a stand, circulating time, among others. These ILTs are defined as Identified Invisible Lost Time (IILT). The difference between ILT and IILT is defined as Unidentified Invisible Lost Time (UILT).

Data Visualization

Once the ILT is estimated, the paper suggests it should be visualized among the data collected from reports and sensors to complement the improvements in process. High-tech dashboards allow the exploration of the data in real time and the visualization of hidden data patterns that can enhance its value. Current technologies allow sharing those sophisticated dashboards via the internet or intranet.

Evaluation

The paper reports that these results and data collected are analyzed to estimate the operation efficiency, quality of TL defined in the program, DOR precision and other potential inefficiencies that are not identified through the real-time data. Even when a technical limit is achieved, a root cause analysis (RCA) should be performed to evaluate operation performance, DOR accuracy and program quality. Lessons learned and best practices are captured through these RCA to adjust the current operation or be implemented in future projects.

Case Study

The following case study puts the described methodology into context. The data corresponds to the drilling of a 12.25-inch hole, considering the operations from making up directional drilling BHA until the last BHA was laid down to run wireline logs.

The paper reports that after calculating the TL, ILT were estimated. However, some inconsistencies were observed in the results. The DOR was validated with real-time data, finding some activities that were shifted from the actual time that started and/or ended. Therefore, the times each activity on the DOR started and ended were validated or corrected to improve the quality of the analysis.

The case study presented in this paper demonstrated that by combining DOR, real-time data, and visualization tools, it is possible to identify sources of inefficiency. It is important to highlight that applying engineering calculations when defining the TL allows the evaluation of the efficiency of the operation under current conditions and the quality of both the operational program and DOR.

Conclusions

• Setting TL from engineering calculations allows to: 
  • Evaluate operational efficiency.
  • Evaluate the quality of operational program, and operation reports.
  • Reduce the need of historical data.
• ILT during NPT brings an opportunity to reduce the NPT to the minimum possible.
• All tools available must be used to identify ILT.
• UILT quantifies the ILT that cannot be identified with current resources.