The Good, the Bad or the Ugly – How to tell which well you have in the Permian basin from cuttings analysis
Milly Wright, Ken Ratcliffe and Eliza Mathia – Chemostrat Inc

Direct measure analysis of cutting samples in the Permian Basin to gather elemental, mineralogical and organic contents has become almost routine. Typically these data are used to characterize petro-facies and tie back lateral well to target zones, here we explore how those data can be used to figure out whether you have wells that can be described as the Good, the Bad or the Ugly and how this can be used to forward model.

The approach of using elemental data to predict zone performance was useful in the Eagle Ford, where elements such as Ni and Mo showed elevated values in zones that were more prolific. However, in the Permian Basin, several factors negate the Eagle Ford approach. Firstly facies change rapidly both spatially and temporally in the formations of the Permian Basin, therefore even if the “best” facies is identified in a pilot, it will change laterally. Secondly, the petroleum system of the Wolfcamp Formation is different to that of the self-sourcing Eagle Ford, meaning that high TOC (and therefore Ni and Mo) will not predict the best producing zones in the Permian Basin. Examples from the Wolfcamp B and lower Spraberry targets in the Midland Basin will demonstrate that by taking a more holistic approach, production zone performance can be predicted from direct cuttings analysis.

Data gathered from lateral well cuttings are organic geochemistry, inorganic elemental data, mineralogical data, RHOB, porosity (total and pore size distribution), which are complimented by any downhole logs available. This paper will demonstrate, with examples, how integration of these data streams with well production data enables understanding of what “ingredients” are needed to create the conditions most likely to result in a good producing from the Wolfcamp and Spraberry targets. With this knowledge it becomes possible to begin to predict where in each stratigraphic target zone the probability of best performing zones will be found.

Key to achieving this goal is being able to gather enough data in a timeframe and budget that allows the work to be done all wells being drilled, not just the occasional science well. Therefore, in this paper we will not only look at the science that allows predictions to be made, but also the workflow that allows these large datasets to be gathered in a way that makes them operationally effective.   

Bio:

Milly has spent the last 15+ years working the field of chemostratigraphy and bulk elemental analyses, and as Director of Operations for Chemostrat Inc, in Houston has studied elemental datasets and their interpretations in wide variety of depositional environments and plays all over the world. Milly is originally from the U.K. and graduated with her undergraduate degree in geology from the University of Leicester in 2000, she then completed her master’s degree in geology from University of Houston in 2010. Her graduate research largely focused on identifying facies and provenance changes using an integrated chemostratigraphic and sequence stratigraphic approach in the Ferron Sandstone, Utah. Over the last few years Milly’s research has predominantly focused on resource plays, regularly presenting at local and national conferences. Outside of work Milly spends her time on the dance floor and is an experienced ballroom and latin dancer.