To give you greater control when editing declines in the DCA Wrangler, we have added an option to lock the Y-Axis at a specific maximum. The ability to lock this axis gives us some neat follow on features.
We’ve added a slick new feature to help you manage all that Well Decline and Type Curve data in Spotfire. Result Tables in the DCA Wrangler represent the current working set of data produced by the DCA Wrangler. In this article, I will show you how to:
In Spotfire, the filter panel allows one to easily remove ranges of values from your data. We can gain even further granularity and control of what we hide from a dataset by applying the “Limit data using expression” window. However, the “Limit data using expression window” doesn’t play nice when you want to replace a data table by matching columns with different names.
When we use replace data functionality and the limiting expression uses a matched column, the expression doesn’t update the column name (as it does with other expressions), which leads to unexpected results. Call this one of those “endearing” Spotfire intricacies.
Fortunately, we can get around this issue by creating a Show/Hide calculated column and rerouting our limiting expression through a calculate column, which will update when you replace data.
A key part of analytics in the oil and gas industry is evaluating opportunities at different locations. Space is always present when looking for profitable development projects. We usually look at the already in production wells and try to find some spatial trends. To stay competitive, we need to find better ways to access the data of different areas and its wells. For instance, we can transform the spatial information to compact objects that store the location and shape of each well and lease. These objects can be feed to different calculations and analyses as geometries. For Spotfire, it also has some advantages, you can use the feature layers of the map chart. In this case, we can visualize the leases as polygons and wells as lines.
When building a Multiple Linear Regression model, we want to limit the correlation between predictor (X) variables. Luckily, Spotfire has a tool that makes identifying the correlation (called multicollinearity) effortless. I will walk you through the tool, and you can see the resulting template here.
Linear Regression models are the simplest linear models available in statistical literature. While the assumptions of linearity and normality seem to restrict the practical use of this model, it is surprisingly successful at capturing basic relationships and predicting in most scenarios. The idea behind the model is to fit a line that mimics the relationship between target variables and a combination of predictors (called independent variables). Multiple regression refers to only one target variable and multiple predictors. These models are popular not only for solving the prediction task but also for working as a model selection tools allowing to find the most important predictors and eliminate redundant variables from the analysis.
We often use Excel templates to calculate the Call and Put option prices with required parameters and the relationship between the parameters are expressed in Excel Functions. One tricky problem with the Excel Function is that if you change the cell position, accidentally click on a wrong cell, or if you want to export the results and utilize the templates again, the embedded functions may change accordingly and generate wrong answers.
Just wanted to share this development journey with you trying to build a template. I wanted to create a WordPress connector for DataShopTalk so that I could download all the articles and get a good idea of the content of the blog. Connecting directly to the MySQL database was not really ideal, I wanted to setup an API from WordPress so that a data function could connect to it.