Key Papers To Unlock Drilling Performance, ep.6

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SPE-150208-MS Critical Role of Digital Data in Physics-based Performance Workflow

This paper talks about the role of digital data in the workflow to maximize physics-based drilling performance.

And it talks about the use of digital data in a way that’s not confusing or vague, such as do so many articles and posts on the hot topic of “digital transformation (DT)”. Maybe it makes your head dizzy as well. Despite the buzz around that term, I have not yet seen anything concrete and actionable that lays out how DT can specifically help unlock drilling performance.

Until I found this paper.

This paper provides some clarity and solid foundation upon which you can think about the use of digital data. You will literally see the drilling data in a different way and see better ways to use them to unlock your drilling performance.

Intro: Two Distinct Strategies

First, we all understand that there’s tons of data that’s being generated every day when you’re drilling a well and it comes in different forms and sizes: Daily Drilling Report, Mudlogs, Downhole data collected through MWD/LWD, Bit Records, just to name a few.

The question is: what can we do with these data regarding drilling performance?

More specifically, are you seeking to use digital data to maintain and optimize current practices for a uniform performance? Or are you seeking to use digital data to change current practices and create new performance?

Two fundamentally different philosophies to use digital data (Fig 1 in SPE 150208)

Depending on how you or your company look at drilling performance, you will have two fundamentally different approach to use your digital data.

Which one are you?

An example for maintaining current practices would be adjusting controls to maintain specified set points. Think auto-driller to keep a constant WOB (digital data and controls) instead of the driller constantly watching Martin Decker while adjusting the brake handle.

An example for changing current practices would be identification of unknown root performance limiters. Think regular WOB/RPM step tests with real-time baseline MSE surveillance to understand what’s really limiting you from raising WOB, is it the onset of bit dysfunctions or is it your augers that have trouble keeping up with the rate of cutting generation?

Rooted in the Limiter Redesign way of drilling, this paper is on the side of using digital data to change current practices to get new (much higher) performance, as is summarized below (emphasis mine):

The volume and types of data that are being collected during drilling operations continue to increase. But new data does not result in new performance unless its existence changes the way the work is done. Consequently, a strategy to use the data must begin with an understanding of the work, the people conducting the work, and the specific characteristics of the organization it is being implemented through.

…The “limiter redesign” workflow… does not seek to perfect old practices but to fundamentally change the way the work is done so that the current limiter no longer constrains performance.

This current downturn in a way has witnessed the limitation of the current practices. Maybe it’s time for us to rethink what we really want to achieve in our drilling performance.

Effecting Change with Digital Data

In this section you will see two excellent examples of using digital data to change current practices.

  • Shaker surveillance
  • Bit and BHA forensics

What strikes me most when I read through these examples is that there is not necessarily much new data to be collected, but rather it’s understanding why you’re collecting those data and what you want to do with it.

For example, I have done quite a few bit records when I was working on the rig. But most of the time it was for the sake of getting the bit record done and filed away. Unless abnormally lower ROP had been experienced or some unexpected damage suffered by the bit, I haven’t had to do much further investigation about the bit and the BHA.

Because the objective was more to avoid trouble (NPT) than to create differentiating performance, data was also collected and used accordingly.

Now looking back through the lens of limiter redesign, I realize that I have probably missed a bunch of non-bit limiters.

This paper offers a new paradigm of using digital data:

The data must be packaged and displayed in a manner that allows drillers and operations personnel to identify dysfunctions or limiters and make real-time decisions.

It must also allow offsite engineers to understand the performance limiter to enable them to design field trials and redesign the system when needed.

Display of Data

Another two excellent examples about “displaying data on routine operations in a manner with which new conclusions could be drawn”:

  • Real-time MSE showing why WOB needs to be applied rapidly rather than slowly.
  • 1-sec data to reliably detect stick-slip (3-10 sec periods phenomenon that’s hard to detect with 20-sec data that used to be displayed in the driller’s cabin)

In both examples, data has been displayed in a way to help drillers “see” dysfunctions that used to be hidden behind how-it-has-always-been-done “best practices”.

Statistical and Physics-based Approaches

This phrase marks the difference and challenges our thinking about drilling performance:

Where do new practices come from? They rarely arise from statistical studies of “what” happened, but from observation of events and deterministic understandings of “why” they happened.

Without understanding the “why”, it’s hard to change current practices (“If it ain’t broke, don’t fix it”) and create new performance.

Global Data Usage in the Performance Workflow

This section talks about data usage in the context of a globalized operations, such as ExxonMobil.

It’s worth noting that the way ExxonMobil did it, was establishing a global personnel network with participating individuals coming from different drill teams, technical and research organizations.

It’s a community that allows sharing of different new practices, as well as the required data collection and analysis, across the entire global operations of the company.

However, for a much smaller operator, the same performance workflow and digital data strategy can still be applied by each of its drill teams to yield newer and greater performance.

Rig Automation and Complexity

This section will help you understand why drilling automation is not an easy task. It also provides three strategies to approach it if you ARE working on automating specific parts of the drilling process.

The reason drilling automation is difficult is due to the complexity of the operations involved in drilling a well. Complexity here specifically is related to the unpredictable nature of drilling a well.

Whether a well is considered to be difficult or simple, all wells are technically complex in the sense that some interactions cannot be predicted.

It provides an excellent example to illustrate the point:

  1. Inadequate mud weight leads to hole enlargement.
  2. Hole enlargement leads to reduced ROP because hole cleaning becomes problematic.
  3. Reduced ROP, which means reduced WOB then leads to whirl because the bit is less engaged and stabilized.
  4. Whirl may lead to vibrationally induced borehole patterns which in turn causes increased friction at stabilizers and loss of weight transfer, and you get chronic stick-slip.

Now if I tell you “inadequate mud weight will give you chronic stick-slip”, this may not appear obvious to you at first glance, nor can you use this to predict what would happen on the next well.

That is complexity, a chain of events that can be logically understood but difficult to predict.

So how do you approach it if you DO want to automate a specific part of the drilling process. Here’re three ways:

  1. Eliminate the unpredictable elements. Example would be what has been done with differential sticking (see SPE-128129, also a great example of new practices born out of physics-based understanding of the drilling process)
  2. Decouple the predictable element which has unpredictable interactions with other elements. Example would be, in the inadequate-mud-weight-causing-stick-slip example above, to use fluid with the right mud weight that results in a gauge hole in the first place.
  3. Maintain continuous real-time surveillance with trained personnel if not able to predict. Example would be real-time MSE surveillance because the onset of dysfunction cannot be predicted with certainty.

*******

In short, to borrow from Francis Bacon’s quote on money: digital data is a good servant but a terrible master.

Guard your drill team from being ruled by some so-called DT experts and their expensive “solutions”. Start by answering the question at the beginning: what specifically are you seeking to achieve regarding your drilling performance? The tools are only secondary.

You can download the paper here on OnePetro.org. May it provide an anchor point when you’re in the midst of all these buzz surrounding digital transformation.

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