Deconvoluting Mixed Petroleum and the Effect of Oil and Gas-Condensate Mixes on Identifying Petroleum Systems – AAPG ACE 2020

Virtual talk at AAPG ACE 2020

Watch recent virtual talk by Ken Peters at AAPG ACE on using Pirouette’s unmixing algorithm for evaluating oil production.

 

 

Two points made in the talk are:

  • You cannot use ratios as the input variables and need to use concentrations instead.
  • The alternating least squares algorithm performs well to untangle mixed sources accurately.

Last call. Upgrade Pirouette before July 1, 2020

Hello fellow Pirouette users.

If you are still using a legacy version of Pirouette, on July 1st the price to upgrade to the current version of Pirouette, version 4.5, will no longer be available. The cost will be the full retail price of Pirouette v4.5. Older versions (e.g., Pirouette 4.0) were designed and implemented for older Windows environments and have become less compatible with current Windows operating systems. If you are still using an older system that has not been upgraded to Windows 10, products like Pirouette 4.0 may still work. However, we are no longer fixing bugs or implementing enhancements. If you have an older version, we recommend you take advantage of the current upgrade rate before it goes to full price. If you have any questions, need additional information or a quote, email us at sales@infometrix.com.

Chemometrics-enhanced Classification of Source Rock Samples Using their Bulk Geochemical Data: Southern Persian Gulf Basin

Chemometrics-enhanced Classification of Source Rock Samples Using their Bulk Geochemical Data: Southern Persian Gulf Basin, co-authored by Infometrix’ Scott Ramos has recently been published. See abstract below and contact us if you have any questions.

Abstract

Chemometric methods can enhance geochemical interpretations, especially when working with large datasets. With this aim, exploratory hierarchical cluster analysis (HCA) and principal component analysis (PCA) methods are used herein to study the bulk pyrolysis parameters of 534 samples from the Persian Gulf basin. These methods are powerful techniques for identifying the patterns of variations in multivariate datasets and reducing their dimensionality. By adopting a “divide-and-conquer” approach, the existing dataset could be separated into sample groupings at family and subfamily levels. The geochemical characteristics of each category were defined based on loadings and scores plots. This procedure greatly assisted the identification of key source rock levels in the stratigraphic column of the study area and highlighted the future research needs for source rock analysis in the Persian Gulf basin.

Keywords: Chemometric Classification, Source Rock Geochemistry, Rock-Eval Pyrolysis Data, HCA, PCA.