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Using Analytics to De-risk Drilling Practices: an Offshore Example


The shale revolution taking place in the United States is overwhelming because of the amount of data acquired from unconventional plays. Because of the technology the operators employ during any given onshore drilling program, we now have access to information across the gamut of exploration & production. Information inundation is an understatement. But unconventional reservoir E&P is very much an American product. Internationally, unconventional well drilling/completion account for only a fraction of overall hydrocarbon production. Even explorationists focusing on the Gulf of Mexico see their brethren working onshore in the US as a completely different group.

And rightfully so.

Offshore conventional players are greatly limited in terms of public data they have access to, and the amount of well control to employ in an area. Targets are deeper thus providing seismic interpreters with a number of challenges, and drilling programs cost more. Much more. In some cases, an offshore well can cost 10-20 times more than a typical onshore US well, maybe more. So what are explorationists to do when relying on such little data to make such big decisions?

The key is analytics. Even in areas of limited well control, we are still able to make quantitative interpretations, thus de-risking our drilling practices while saving a lot of time, effort and money

Take the example below. In this area, we have a typical offshore clastic environment where geophysicists are chasing high-amplitude beds while staying sensitive to certain tectonic and structural influences. In the Gulf, these include challenges like salt movement and sealing vs. non-sealing faults. Although we only have access to 1 post-stack seismic volume and a couple of wells with limited production data, we can still use the power of analytics to accurately identify the reservoir while at the same time scouting out our next area of interest. One well proved to be a prolific oil discovery. The other was a dry hole. Which one was the dry hole and which one produced? And why?

RM-Fig-1 Using Analytics to De-risk Drilling Practices: an Offshore Example

The problem with the realm of post-stack seismic attribute analysis is that, oftentimes, the interpreter is left with a plethora of seismic volumes from which to analyze. This is a time-consuming and data-intensive process. And the yield is purely qualitative unless you have rock property information to leverage. And once you have a result, geologists and engineers have a hard time consuming it and relating it back to the wellbore. Even still, there are plenty of examples of these data reflecting real-life geohazards that we need to take into consideration. Below is an example of how Spectral Decomposition and RGB blending can reveal subtle features like the white lineations that have been interpreted as glacial recession scars. Drillers know these features well; hitting one of these can and have resulted in offshore rig blowouts.

RM-Fig-2 Using Analytics to De-risk Drilling Practices: an Offshore Example

Again, this is just an example that we can get from conventional geophysical analysis. How can we utilize it all, and ideally get to a confident interpretation, quickly?

Here’s how analytics can help. Let’s consider the many volumetric ways of analyzing offshore data, with the hopes of de-risking our reservoir and use that same technique to go and find our next area of interest. Once we level the playing field for these attributes, we can see that it’s only after incorporating multiple volumes in analytics that features start to jump out at us. Whether it’s subtle channel events that were previously indiscernible via the seismic (below), or precise identification of known reservoir, the user is confident that the result is that much more accurate because of the amount of data that they are utilizing.

RM-Fig-3 Using Analytics to De-risk Drilling Practices: an Offshore Example

Let’s use this method and apply it to a broad area. In this example, let’s leverage the known producing well (example A above) and apply that same sort of seismic facies classification method to use when looking for a new area of interest in that volume. In the example below, we see accurate delineation of the feature we can confidently say is categorizing known reservoir, and use that same specific analytical model to find the new zone of interest within the same volume. Of course, we need to apply more science and utilize other data, but from a de-risking perspective, the interpreter can say with confidence that, with appropriate utilization of existing well data in an exploratory play, they have quickly and accurately identified the next zone of interest based on the specific multi-variate response of the known reservoir.

RM-Fig-4 Using Analytics to De-risk Drilling Practices: an Offshore Example

Learn more about how DI Analytics is powering oil & gas professionals to make better, faster decisions, or watch this brief demo video of our DI Transform Predictive Decision Platform.

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Ryan Martin

Ryan Martin is a Solutions Architect at DrillingInfo in the Houston office, working closely with clients on various aspects of G&G software and data consultancy, installation, interpretation, well optimization, field development, and training. He came to DrillingInfo via the acquisition of Transform Software & Services in 2013. Prior to that, Ryan worked for a geophysical analysis software company called ffA (Foster Findlay & Associates) from 2009 to 2012, where he managed technical sales & implementation of ffA’s software GeoTeric for the entire Western Hemisphere as a Business Development Geoscientist. Ryan has processed 2D & 3D G&G data from over 30 different countries in all of the major oil & gas basins of the world, both on and offshore, and in both conventional and unconventional reservoirs. Ryan received a Bachelor of Science in Archaeology, Geology, and Business from The University of Texas in 2008.