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How to get more from the rock properties and lithologies associated with seismic inversion results

When you have dozens of wells and hundreds of attributes, how do you determine which attributes, or more precisely - which combination of attributes, are associated with marginal vs. excellent well performance? How can you utilize seismic data to more accurately predict production volumes? These were the questions the geophysicist at Painted Pony wanted to understand to help his reservoir engineers and geologists select the most lucrative well locations, and to inform completions strategies based on seismically derived rock properties.

Optimizing the Human/Machine Partnership: Revealing More Value Than Ever from Investments in Data

Geoscientists and their friends fall into two polarized camps these days: the ‘machine learning/AI will replace humans’ camp and the ‘a computer can’t possibly learn the kind of creativity required to do geology’ camp.  Neither is right.  If the two camps all got together in a crowded room, it would be like the chocolate-lover and the peanut butter-lover bumping into each other in the street and creating my favorite sweet treat, the Reese’s peanut butter cup! The whole is definitely tastier than the sum of its parts.

The Evolution of Geoscience

It’s a little cliché to say, but technology is changing so fast that it’s difficult to keep up. Computer storage, processing speeds and visualization capabilities continue to grow overwhelmingly; “integration” (however you define it) is the dream of most geoscience departments and generic “big data” analysis techniques are evolving rapidly – this is indeed an exciting time to be a geophysicist, a scientist, a human being.

A rock-physics based classification of the Doba Basin of Chad

Rock-physics models provide the means to translate the elastic properties obtained from seismic inversion, into geological attributes that carry more intuitive meaning for reservoir development. In this presentation, we create a rock model that initially is based on x-ray diffraction (XRD) analysis, fluid analysis, and engineering parameters. We then extend the model to account for the interbedded nature of the reservoir, using net-to-gross ratio (N:G), along with porosity, to create a geological model of the field.

Incorporating 3C seismic data quantitatively for enhanced geologic detail in an oil sands reservoir

A workflow incorporating converted-wave (PS) data in an integrated quantitative interpretation (QI) was illustrated with a case study in the Canadian oil sands in which multiple data types were available including high-quality 3D multicomponent data, dipole sonic logs, and a multicomponent walk-away vertical seismic profile (VSP). In an area with unconventional rock property behavior and complex fluid distributions, the dipole sonic logs provided the data necessary for robust deterministic rock-physics templates... 

Quantitative Seismic Interpretation in a Canadian Shale Gas Reservoir

A big challenge in unconventional resource development is how to optimize production for attractive economics.  An often overlooked factor in this optimization is the geology. This peer-reviewed paper describes a detailed integrated analysis of seismic data including the rarely-acquired (and even more rarely processed) converted-shear-wave (PS) data. The results predicted variations in reservoir properties and quality that can contribute vital information to the decision-making.

Discover seismic attribute interpretation simplicity.

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Canadian Society of Exploration Geophysicists (CSEG) - DoodleTrain

November 4 - 8, 2019

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Learn how to prepare, combine and classify seismic attributes in a practical QI workflow. Or, contact us for an in-house customized version of this course.