Data Science & Analytics / Drilling & Completions

Demystifying Completions Data: Collecting and Organizing Data for Analytics (Part 1)

The oil and gas industry collects a huge amount of data trying to better understand what’s happening in the subsurface. These observations and measurements come in a range of data types that must be pieced together to garner insights. In this blog series we’ll review some of these data types and discuss an approach to integrating data to better inform decision making processes.

Before getting into the data, it’s important to note why every company needs a data strategy. Capital efficiency is now the name of the game in unconventionals. Investors are pushing for free cash flow, not just year over year increases in production. The nearby slide is from one operator but virtually every investor deck has a slide like this one. There are positive trends that operators can show – price concessions from service providers, efficiency gains in drilling, completions, facilities and increases in lateral length. Despite these gains, as an industry, shale is still not profitable. How much further can operators push these trends? How will this chart be created next year? Single-silo efficiencies are gone, and the next step change will only come from an integrated approach where the data acquired across the well lifecycle can be unlocked to fuel cross-silo insights.

Figure 1: Virtually every investor deck has a figure like this one. There are positive trends that operators can show– price concessions from service providers, efficiency gains in drilling, completions, facilities and increases in lateral length. Despite these gains, as an industry, shale is still not profitable. How much further can operators push these trends? How will this chart be created next year?

This is especially true in completions, which represent 60% of the well costs and touches so many domains. What does completions optimization mean? It’s a common phrase that gets thrown around a lot. Let’s unpack this wide-ranging topic into a series of specific questions.

  1. How does frac geometry change with completions design?
  2. How do you select an ideal landing zone?
  3. What operations sequence will lead to the best outcomes?
  4. What effect does well spacing have on production?
  5. Will diverter improve recovery?

This is just a small subset, but we can see these are complex, multidisciplinary questions. As an industry, we’re collecting and streaming massive amounts of data to try and figure this out. Companies are standing up centers of excellence around data science to get to the bottom of it. However, these issues require input from geology, geomechancis, drilling, reservoir engineering, completions, and production – the entire team. It’s very difficult to connect all the dots.

There’s also no one size fits all solution; shales are very heterogenous and your assets are very different from someone else’s, both in the subsurface and surface. Tradeoffs exist and design parameters need to be tied back to ROI. Here again, there are significant differences in strategy depending on your company’s strategy and goals.

Managing a data tsunami

When we don’t know what’s happening, we can observe, and there’s a lot of things we can observe, a lot of data we can collect. Here are some examples that I’ve grouped into two buckets: diagnostic data that you would collect specifically to better understand what’s happening and operational data that is collected as part of the job execution.

The amount of data available is massive – and only increasing as new diagnostics techniques, new acquisition systems and new edge devices come out. What data is important? What data do we really need? Collecting data is expensive so we need to make sure the value is there.

Figure 2: Here are some examples of diagnostic data that you would collect specifically to better understand what’s happening and operational data that is collected as part of the job execution.

The data we collect is of little value in isolation. Someone needs to piece everything together before we can run analytics and before we can start to see trends and insights. However, there is not standards around data formats or delivery mechanisms and so operators have had to bear the burden of stitching everything together. This is a burden not only for the operators, but also creates problems for service providers whose data is delivered as a summary pdf with raw data in Excel and is difficult to use beyond of the original job. The value of their data and their services is diminished when their work product has only limited use.

Thinking through an approach

A common approach to answering questions and collecting data is the science pad, the scope of which can vary significantly. The average unconventional well costs between $6 and 8M but a science pad can easily approach $12M and that doesn’t take into account costs of the time people will spend planning and analyzing the job. This exercise requires collecting and integrating data, applying engineering knowledge, and then building models. Taking science learnings to scale is the only way to justify the high costs associated with these projects.

Whether on a science pad or just as part of a normal completions process, data should be collected and analyzed to improve the development strategy. A scientific approach to completions optimization can help ensure continuous improvement. This starts with a hypothesis – not data collection. Start with a very specific question. This hypothesis informs what data needs to be collected. The analysis should then either validate or invalidate our hypothesis. If we end there, we’ve at least learned something, but if we can go one step further and find common or bulk data that are proxies for these diagnostics, we can scale the learnings with predictive models. Data science can play a major role here to avoid making far reaching decisions based off very few sample points. Just because we observed something in 2 or 3 wells where we collected all this data does not mean we will always see the same response. We can use data science to validate these learnings against historical data and understand the limits where we can apply them versus where we may need to collect more data.

In part 2 of this series, we’ll walk through an example of this approach that addresses vertical frac propagation. Specifically, we’ll dive into collecting, integrating, and interacting with the required data. Stay tuned!

 

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