Chapter 1 - Introduction

01-01 - Introduction (13 min.) Sample Lesson
01-02 - Instrumentation (14 min.)
01-03 - XRF Theory (16 min.)
01-04 - Data Quality (30 min.)
01-05 - Case Study - Core Analysis (9 min.) Quiz: 01-05 - Case Study - Core Analysis

Chapter 2 - Elements and Minerals

02-01 - Common Mineral and Element Associations (13 min.)
02-02 - Application of Well Data (15 min.)
02-03 - Common Element-Mineral Links (16 min.) Quiz: 02-03 - Common Element-Mineral Links

Chapter 3 - ED XRF - Calibration and QC

03-01 - Instrumentation Setup (15 min.)
03-02 - Effects and Solutions (9 min.) Quiz: 03-02 - Effects and Solutions

Chapter 4 - Interpretation Methods

04-01 - Data Interpretation Methods (23 min.)
04-02 - Examples - Key Elements (13 min.) Quiz: 04-02 - Examples - Key Elements

Chapter 5 - Interpreting XRF Data

05-01 - Benchmarking Data Sources (14 min.)
05-02 - Horizontal Well Example (10 min.)
05-03 - Integration of Elemental Data with TOC (17 min.)
05-04 - Lateral Variations (12 min.)
05-05 - Broader Examples and Datasets (12 min.) Quiz: 05-05 - Broader Examples and Datasets

Acquisition and Use of Elemental Data for Rock Characterization and Correlation / Chapter 1 - Introduction

Lesson 01-01 - Introduction

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Transcript

01. Lesson 1.01: Introduction 02. Why Are We Acquiring Elemental Data on Rocks?03. Applications of Elemental Data 04. What is Chemostratigraphy?05. Use of Chemostratigraphic Data 06. What Do We Need to do to Make it Work?

01. Lesson 1.01: Introduction

Hey guys, thanks for clicking on my course. My name is Milly Wright, and today I'm going to be talking to you about elemental datasets, how to acquire those datasets, how to build up meaningful projects, and QC and interpret that data.
I've been doing this kind of work now for, goodness, like 25 years and I've a real evolution in these types of studies. It's gone from the start of my career, when nobody did this work, to now this kind of analysis being pretty commonplace within our industry. There's a lot of different things that you can do with this data, and I'm going to try and take you through some of those examples through the duration of these courses. But to begin with, we're just going to cover some of the basics.
As I said before, I've had a lot of experience dealing with these datasets. Currently, I am a co-founder of RohmTek and we supplyXRF elemental data in mineralogy to the oil and gas and materials analysis industries. And as I said, before then I've been doing similar work. I did my master's thesis acquiring this type of dataset as well. I hope you enjoy this and let's go ahead and kick things off.
As I said before, our first section is going to just be a gentle introduction. And the first question that we're asking is how and why are we acquiring XRF or elemental datasets? And what good practice and methods can we put in place to make sure that we have robust datasets, build good studies, and are able to interpret the data meaningfully?

02. Why Are We Acquiring Elemental Data on Rocks?

So this is a kind of dumb question really, but I think we need to ask it, is why are we acquiring elemental data on rocks? Before we go into the methodologies for acquiring that data, let's just take a step back and make sure we actually really need to do this. The best studies that I have worked on are ones where we have really clear goals and have really just thought through the whys and making sure that we set things up for success. And this is really an approach that I very much encourage.

03. Applications of Elemental Data

Let's look at some applications of elemental data. So the first probably primary application is in terms of chemostratigraphy. And in this case, what we mean is the characterization and correlation of geological rock units using variations in elemental composition. I know that's a mouthful. And everything else sort of falls out from that. So we can also use elemental data to purely make a lithological classification or characterization, so a chemical lithology. We can interpret provenance and paleoclimate from elemental data. We can estimate TOC and bottom water redox conditions at the time of deposition. We can do simple things. Like we can look at potassium, thorium and uranium elements we get in our dataset and plot a chemical gamma, so a gamma signature made from the elemental data vs. your downhole gamma tool. We can also look at mineral modeling and modeled mineralogy which has implications certainly in the petrophysical world, and obviously the results in understanding reservoir quality. So there's a lot of things we can do with this data. And that's just very much on the geological side. There are lots and lots of uses for elemental data both outside of the oil and gas industry, in mining, in critical minerals, inside the oil and gas sector in terms of engineering and completions, and in some cases drilling as well. But for this course we're gonna try not to go too crazy and limit it to geological applications, and we're going to revolve around this idea of characterization and correlation. Let's go ahead and move on.

04. What is Chemostratigraphy?

I think this is also a really decent question then. So we're acquiring elemental data on rocks. We've said we want to do that because we want to, in this case, characterize and then produce either a correlation or a model that uses that data. And we term to bucket that generally as chemostratigraphy. As a little side note here, chemostratigraphy can also be used to describe stable isotope stratigraphy. We are not discussing this. So this is chemostratigraphy using bulk elemental analysis.
And so how do we define that? Within the industry it's generally defined as a characterization of sedimentary units using inorganic geochemical or elemental data. This characterization allows us to identify different chemostratigraphic units based on changes in elemental concentrations, trends, ratios, and I'm gonna show you as we move through this course examples of all those kind of things. So that's the practical aspect of what it is, but why does it work?
What we're really playing on here is the idea that sediments are highly variable in their composition. And they're really sensitive to subtle changes in mineralogy, either through changes in source composition, different depositionalfacies, weathering profiles, post-depositional diagenesis. Elemental datasets allow us to pick up these variations even if they're relatively subtle. So we can take successions and sequences that look pretty much the same on logs or in core descriptions and show differences in composition and, as I say, subtle changes in accessory phase minerals that really allow us to define and characterize those sequences in a way that you can't do necessarily with a core description.
In terms of nomenclature, we tend to refer again at these changes as chemical units. And you're gonna see, as I work through this, I'm gonna talk about chemical packages and chemical units, and we'll oftentimes build these as a stratigraphic hierarchy. And so we'll look at examples where these can be characterized vertically in a single well or laterally across an area. And both are completely valid ways of utilizing this data.

05. Use of Chemostratigraphic Data

One of the things that I have fought through a lot of my career is trying to get people to move from traditional stratigraphic and characterization techniques to something like this, which is a little bit more progressive. And it involves change. And all change takes a little time, but it's really important to always ground ourselves in the benefits, what we're trying to do. This is an example pulled from a Shell publication in 1999 where chemostratigraphy was used to supplement lithostratographic and biostratigraphic correlation techniques, which were the main correlation techniques in the North Sea at that time. And one of the reasons I like to show this example is the effect of adding a different and new technique to understand these rocks. And you can see this is the old zonation where you had a 3-fold division primarily based on gamma log signature. After using a chemostratigraphic approach, so looking at variations in elemental concentrations and trends, this section can be divided up into a 5-unit sequence. And that correlation looks very different. It allows a little bit more detail, a little bit more understanding of how different sand and mudrock packages correlate. And that's really the birth of this type of analysis and why we firstly started looking at elemental datasets.

06. What Do We Need to do to Make it Work?

Next question then, what do we need to do to make it work? The first thing we need to do is we need to actually analyze stuff. And in our world that's cutting and core samples, sometimes field samples as well. So when we're looking at this type of analysis, we need a rock; we need something to run analysis on. One of the great things about elemental analysis in general, is it can be run on any lithology, any age range, deposited under anydepositional environments. That said, there are areas where this workflow has real value, and that is generally sequences that are very hard to correlate using traditional gamma log or lithological characteristics where you're in basically the same lithology where you have repetitive signatures. Mud rock sequences (we'll look at a lot of those), stacked sandstones as well, or where you have turbiditic sequences that are thick, rapidly deposited, repetitive cycle sediments. These are areas where this type of data is really important in terms of developing meaningful characterizations.
The other benefit to this kind of approach is it's not constrained by grain size or lithology. Again, there are different interpretive techniques for different lithologies just as there are different interpretive techniques for different depositional environments. But in terms of application, it is all good. We talked a little bit in the title about sample types. I would say, if I'm being completely honest in my career, the majority of samples that I have looked at have been cutting samples. And that's just because we got so many of them, we can get a much better lateral variability using cutting samples because we tend to have more of them from more wells. But I've also done work using field samples like produced solids, scale, obviously sidewall core and core analysis and direct core face scanning onXRF as well. So there's a whole variety of sampling strategies and samples that you can look at.
OK, so that concludes everything for this lesson. And in the next lesson we're going to talk about analytical techniques.