A guide for geoscientists to maximize the tools they have

This post is based on a virtual talk given by Ed Chow for the Calgary Geoscience Data Managers Society (CGDMS). CGDMS members can access a talk recording through cgdms.ca

Data analytics – challenges getting started

“Data Analytics” can sound intimidating. It sounds complex. Intensive. Specialized. There is no shortage of courses you can take or credentials you can earn online on the topic. But…data analytics doesn’t have to be an exclusive club. As it turns out, geoscientists are already experts at data analytics. All we need are data and somewhere to bring everything together.

It sounds easy. And as it turns out, geoscientists can do it with the tools they already have. So, what is holding us back?

For one, geoscientists are swamped! Producers continue to run lean and are striving to maximize returns for their investors. That leaves geoscientists with a lot of responsibilities and little time to explore new workflows. At the same time, geology and geophysics (G&G) staff continually need to prove their value. In engineering-driven unconventional plays, the value of the G&G team may not always be evident to management beyond locating and geosteering a well.

Adding value

That leaves geoscientists with two options to bring more value to their companies.

  1. Put your head down and grind out more hours like Arnold Schwarzenegger pushing that wheel for years on end in “Conan the Barbarian” or
  2. look for solutions beyond your traditional tool kit.

Option 1 sounds exhausting. Let’s just skip to option 2. For most, there’s no time to invent something new. The solution lies in taking small, achievable steps to view the data that we already have through a new lens.

What value looks like

Using existing information to reduce risk is something that everybody (managers, engineers, etc.) can get on board with. This is one area where geoscientists can add tremendous value for little cost. For example, we could predict which reservoir characteristics contribute to a slower rate of penetration (ROP) while drilling. Arming the drillers and engineers with this kind of information will help them anticipate potential areas of slowdown and not pull the bit in panic, saving >$75k per instance. That is a concrete demonstration of value.

Challenges with 3D data

The challenge in getting to these conclusions is data management. Many companies can’t get the data in place to start. Databases can be out of date, data may reside in different software packages, and things change on a regular basis. Furthermore, proposed wells are often hand-drawn. 2D well sticks don’t integrate with 3D subsurface data and hand-drawn 3D wells are not realistic or accurate enough to integrate plans with 3D subsurface data. The subsurface can change rapidly along a well path so accuracy is required to extract valuable insights from the data.

Bringing it all together

The first step is to get all data in the same place. It’s important to make it as straightforward as possible by leveraging databases that geoscientists already use. In our case, we’re using the database in Geophysics by SeisWare and using SeisWare’s Field Development Tool to generate and easily update our proposed wells with realistic builds and turns. This way we have accurate XYZ information to compare to geoscience attributes.

Additional spatial data can be easily gridded (read about quick grid and contour) and incorporated into the analysis. This allows geoscientists to extract attributes along the wells and start digging into the data in a meaningful way.

Circular process - gather data, combine data, analyze data, repeat

Put simply,

  1. Gather data
  2. Bring data into one environment
  3. Analyze
  4. Repeat

A more approachable way to do data analytics

One straightforward tool to look for potential relationships between geoscience data and engineering data is the crossplot. This isn’t new, and it isn’t necessarily what you may be thinking of when you think of “data analytics” – but it is. For example, here are two crossplots. one identifies a potential relationship between a specific seismic attribute and ROP. This could be useful for forecasting slowdowns at specific stages or flagging potential completions difficulties. And nobody is more qualified to validate these relationships than you, the geoscientist.

Looking for relationships between geoscience data and well data
(Above) Looking for relationships between geoscience data and drilling data using built-in tools.
ROP curves shown with a seismic parameter that predicts slow ROP
A seismic horizon underlays drilled and planned wells. Warm colours may predict changes in ROP, as indicated by the logs along drilled wells.

Data Analytics – not as complicated as it sounds

Data analytics doesn’t have to be complicated but improved well plan accuracy is a key part of value creation. Data management can certainly be a challenge, but one platform to bring it all together enables geoscientists to do more with the data they already have (with little added effort). Combining geoscience and engineering data creates tremendous value, and value for managers means more value for geoscientists.

Want to learn more about combined geoscience workflows or data analytics? Check out these resources:

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