With the rise of unconventionals and the increase in wells permeating already tapped fields, Well Spacing has become the hot topic du jour. But, what is well spacing? Does it refer simply to how many wells are in one area? If so, is that area defined by a circle or rectangle or other definition? What is the make-up of the nearby wells? Today, we will examine some common terms and approaches to analyzing well spacing.
Two templates that utilize the features that we will discuss in today’s post are Well Spacing Feature Calculations and Horizontal Well Spacing Model.
Key terms we will discuss today: Voronoi Diagram, circular and rectangular radius, Area of Interest, intersect area, intersecting wells, closest wells, closest distance, aggregated well statistics.
Jason is a Junior Data Scientist at Ruths.ai with a Master’s degree in Predictive Analytics and Data Science from Northwestern University. He has experience with a multitude of machine learning techniques such as Random Forest, Neural Nets, and Hidden Markov Models. With a previous Master’s in Creative Writing, Jason is a fervent believer in the Oxford comma.
In this blog, I’m going to show you how to create well sticks as a layer in your map chart visualization. The only table that you will need are your well headers that has both surface and bottom hole locations.
Data science in Oil and Gas is central stage as operators work in the new “lower for longer” price environment. Want to see what happens when you solve data science questions with the hottest new database and powerful analytics of Spotfire? Read on to learn about our latest analytics module, the DCA Wrangler. If you want to see it in action, scroll down to watch the video.
Layering Data Science on General Purpose Data & Analytics
Ruths.ai is a startup focused on energy analytics and technical data science. We are both TIBCO and MongoDB partners, heavily leveraging these two platforms to solve real-world problems revolving around the application of data science at scale and within the enterprise environment. I started our plucky outfit a little under four years ago. We’ve done a lot of neat things with Spotfire including analyzing seismic, and well log data. Here, we’ll look at competitor/production data.
MongoDB provides a powerful and scalable general purpose database system. TIBCO provides tested and forward thinking general purpose analytics platforms for both streaming and data at rest. They also provide great infrastructure products which isn’t in focus in this blog.
Ruths.ai provides the domain knowledge and we infuse our proprietary algorithms and data structures for solving common analytics problems into products that leverage the TIBCO and MongoDB platforms.
We believe that these two platforms can be combined to solve innumerable problems in the technical industries represented by our readers. TIBCO provides the analytics and visualization while MongoDB provides the database. This is a powerful marriage for problems involving analytics, single view or IOT.
In this blog, I want to dig into a specific and fundamental problem within oil and gas and how we leveraged TIBCO Spotfire and MongoDB to solve it — namely Autocasting.
What is Autocasting?
Oil reserves denote the amount of crude oil that can be technically recovered at a cost that is financially feasible at the present price of oil. Crude oil resides deep underground and must be extracted using wells and completion techniques. Horizontal wells can stretch two miles within a vertical window the height of most office floors.
For those with E&P experience, I’m going to elide some important details, like using “oil” for “hydrocarbons” and other technical nomenclature.
Because the geology of the subsurface cannot be examined directly, indirect techniques must be used to estimate the size and recoverability of the resource. One important indirect technique is called decline curve analysis (DCA), which is a mathematical model that we fit to historical production data to forecast reserves. DCA is so prevalent in oil and gas that we use it for auditing, booking, competitor analysis, workover screening, company growth and many other important tasks. With the rise of analytics, it has therefore become a central piece in any multi-variate workflow looking to find the key drivers for well and resource performance.
At the heart of any resource assessment model is a robust “autocasting” method. Autocasting is the automatic application of DCA to large ensembles of wells, rather than one at a time.
But there’s a problem. Incumbent technologies make the retrieval of decline curves and their parameters very difficult. Decline curve models are complex mathematical forecasts with many components and variation. Retrieving models from a SQL database often requires parsing text expressions. And interacting with many tables within a database.
Further, with the rise of unconventionals, the fundamental workflow of resource assessment through decline curves is being challenged. Spotfire has become a popular tool for revamping and making next generation decline curve analysis solutions.
Autocasting in Action
What I am going to demonstrate is a new autocast workflow that would not be possible without the combined performance and capability of MongoDB and Spotfire. I’ll be demonstrating using our DCA Wrangler product – which is one of over 250 analytics workflows that we provide through a comprehensive subscription.
Its important to note that software exists to decline wells and database their results. People have even declined wells in Spotfire before. What I hope you see in our new product is the step change in performance, ease-of-use, and enablement when you use MongoDB as the backend.
First, we have a home run solution for decline curves that requires a MongoDB backend. In the near future, more vendor companies will be leveraging Mongo as their backend database.
Second, I hope you see the value in MongoDB for storing and retrieving technical data and analytic results, especially within powerful tools like Spotfire. Plus, how easy it is to set up and use.
And Lastly, I hope you get excited about the other problems that can be solved by marrying TIBCO with MongoDB – imagine using Streambase as your IOT processor and MongoDB as your deposition environment. Or even store models and sensor data within Mongo and use Spotfire to tweak model parameters and co-visualize data.
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.
In the oil and gas industry, ArcGIS is king. In terms of capabilities there’s no question that when you see a map lying around a corporate office, it was printed from ArcGIS. Over the years Spotfire has done quite a bit in the way of There’s quite a bit of Those of you handy with Spotfire may know the difficulties in replicating the large graphs. Below I’ve included some tips for those Spotfire developers that have found themselves crossing into that area.
When you get the link from them, be sure that it ends with /MapServer/WMSServer?request=GetCapabilities&service=WMS. This is key, otherwise you will be nosing around the MapServer with no success.
Understand WMS Layers
While Spotfire handles shapefiles, you may find youfself asking how can I create more dynamic maps without all these tables? WMS layers are the answer to that. If your ArcGIS team already has a MapServer, ask them to publish WMS services for the layers that you want. For example if you are asking for leases be sure to recommend the color and outline that you are looking for. WMS layers can be stacked on top of each other much like in ArcGIS, but as far as data goes, the power truly likes in the marker plotting in Spotfire.
Set the Zoom Visibility Controls
If you have multiple layers in the map chart, you will want to control whether some layers should be visible at certain zoom levels. For example, if you have feature layers that encompass larger portions of the United States, they may not be necessary at a well level. Use the zoom visibility feature to reduce the impact of these layers at a higher zoom:
Printing the Big Picture
This was a bit of a personal journey and by that I mean trial and error. My colleague recommended the simplest method by far, export the map chart as a PDF, noting to set the paper size to A0. For us Americans, I recommend the following infographic:
An A0 -sized landscape PDF export is just about what your typical land management executive wants to see for their particular areas. Export to PDF, print on the plotter, done.
Caching for Performance
Be sure to cache these layers as well, performance can be an issue when you are dynamically pulling more than one WMS layer. This also depends upon your latency as well.
That’s all for now! Let me know if you guys have any more advice on the topic!
This is quick video how you can use Spotfire to assist oilfield operators in determining the flow rates of gas-drive wells using inflow performance relationship (IPR) and tubing performance relationship (TPR) of reservoir, wellbore, and production data.
It is quite easy to include formation tops in the Ruths.ai Well Log Visualization. The neatest way to do that is to have a data table that contains the formation top depth for each well contained in the data table that has the well log data. In its most basic form, the formation tops data table should contain at least 3 columns: Formation Name, Top Depth and Well Name. Here is a video of how to add formation tops to Ruths.ai Well Log Visualization: