Wells : Holes drilled in the ground.
Reservoir : A petroleum reservoir or oil and gas reservoir is a subsurface pool of hydrocarbons contained in porous or fractured rock formations
Well logs are geophysical measurements along the well. These are usually 1D measurements in the sense that a measurement is made at the bottom, then the tool is pulled up a little bit and then another measurement is made. This continues until the entirety of the well is measured. Often, many different types of tools are used that measure different properties, resulting in multiple well logs for a single well. They measure things like how fast sound travels between two parts of the well or how much signal is bounced back and measured by a tool after a certain type of radiation is given off by a tool. These measurements are then turned into rock properties like density, grain size, etc.
Tops : Tops are markers for the tops of things. Specifically, tops of geologic units. Everything below a top is one geologic layer and everything above a top is another geology layer.
Different Types of Tops:
Tops can divide different categories of layers. Sometimes the layers are based on characteristics of the rock.
Lithology is term for describing what a rock is made up of. Facies is another term that refers to categories of rocks in a well with similar characteristics. Geologist’s favorite way to place a top, however, is to place tops based on time. A top can separate rock deposited at one point in time from rock deposited at the next point of time. This is a stratigraphic or chronostratigraphic top! Also, called a time surface.
The first, lithologic-tops are based on what the rock is made up of, which can be measured, at least indirectly. Flow is a little harder to predict from well logs but the ability of fluids to flow is fundamentally also based on physical characteristics, which can be measured, just with more difficulty as very small characteristics at the level of pores in rocks are what matter.
Stratigraphic tops are based on age of the rocks. There is no measurement that relates to time that can be done routinely in well logs. You can collect fossils and interpret time based on the fossils found in different units (biostratigraphy). You can find volcanic ash and date it by looking at how much one type of element has turned into a different type of element due to decay (geochronology). However, neither of these can be done on all, or even most, wells. They’re too expensive and time consuming. Not all depth points will have fossils or ash layers for dating.
How then does one go from well logs to stratigraphic tops representing time surfaces? That requires a model, and a head to put it in.
In practice, chronostratigraphic (mapping out time surfaces) well log correlation (correlation means interpreting where a top in well A exists in well B) is a combination of lithostratigraphic correlation (looking at 2 wells and matching curves of the well logs using the assumption that similar looking curves, have similar properties, and are the same layers) and application of conceptual models. These conceptual models cover how sediment is transported and deposited. They are very helpful for stratigraphic correlations as they predict the spatial distribution of different types rocks deposited at the same time and how those spatial associations can change over time. These conceptual models come from two places, outcrop studies (going out in the field and looking at rocks) and modern analogues studies (going out to the valley, rivers, lakes, oceans, etc. and seeing how sediment gets deposited). The geology name for these conceptual models is depositional environments.
In our context, supervised machine-learning means, instead of letting the computer going on fun field trips like geologists to gradually build up mental conceptual models to use for correlating well logs chronostratigraphically, we’ll give the computer a dataset of already human-picked tops for one time surface and ask the computer to figure out a model that lets it mimic the geologist.
Features in a machine-learning context are new data characteristics built from the original data. An basic example might be the sum of three other original data characteristics. Feature creation is a very common part of machine-learning. Rarely would you only use original raw data.
Unlike some of the demo datasets traditionally used in machine-learning demos where each row of the dataset is an independent entity and features are only created within each row, a key aspect of building features for stratigraphy applications is that a lot of valuable information can be gleaned if one creates features based on comparisons or aggregate observations from multiple depth points or even across wells. One type of comparison is between each depth point in question and the depth points above, below, and around it within different length windows. Another type of comparison is between the characteristics of the well that holds the depth point being predicted for and the neighboring wells.
Reference : http://justingosses.com/predictatops/