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  • Accelerate Completions Engineering Workflows with TIBCO and Petro.ai

    To me, Spotfire has always stood out as the ?best-in-class? visual analytics platform, not simply because of its ability to create charts, but it?s powerful extensibility with advanced analytics and custom app development. Using this, our partners, like Petro.ai, have even created standalone software applications to help specific industry verticals with advanced challenges and sometimes complicated business operations.


    Petro.ai offers a platform to help oil and gas (O&G) professionals from across the well lifecycle to integrate data, automate workflows, and apply machine learning to O&G applications. In this blog, I?ll walk through an example of how I used Petro.ai with TIBCO Spotfire to see how Completions Engineers can design better wells.

    To learn more about Petro.ai or TIBCO Spotfire, visit us in Denver at our joint meetup on September 24th or in Houston at the TIBCO Analytics Forum on October 28-30th.

    Well Completions Overview

    Of all the activities required to bring a well online, completions often have the greatest influence on overall profitability. Not only are completions the largest line item in terms of cost but it also has a direct impact on a well?s performance. Completions Engineers tasked with designing this phase of a well?s lifecycle must bring together a wide variety of data if they want to learn from past wells while designing future operations. This can be extremely challenging as many factors can contribute to a well?s performance. Below are just some of the variables that need to be considered:

    • Geology
    • Landing zone
    • Well spacing
    • Well orientation
    • Cluster spacing
    • Perforation size
    • Stage spacing
    • Pump rate
    • Proppant/ft
    • Fluid/ft
    • Parent-child effects

    Petro.ai Environment

    Petro.ai is cloud-ready and can be deployed on premises or in the cloud ? bringing analytics to your data. The platform uses a web interface to ingest a wide range of data types, and transforms that data into a cleaned data model that is corrected for coordinate reference system, time zone, units, and well aliases. Data can include production, completions and drilling data from a variety of sources including: SQL databases, flat files, or shared drives. TIBCO EBX or TIBCO Data Virtualization are also readily integrated with Petro.ai -- providing Master Data Management and Virtualized Views on federated data sources, expanding data access to engineering, geophysics, geology, finance, and business sources. EBX and Data Virtualization connectors for all data sources, includes recalcitrant applications like SAP, data lakes and specialized energy sector applications. This blended data are then ready for analytics and accessible through the Petro.ai web app, REST API, and through TIBCO Spotfire.

    PetroPanel in TIBCO Spotfire

    Figure 1: Users can access Petro.ai through the side panel which can be pulled up through the View menu

    A side panel inside Spotfire allows users to quickly access Petro.ai where they can create and save well groups, search for and load data from Petro.ai, and build ML models that can be saved and shared with colleagues. This integration brings true self-serve analytics to engineers and geologists as they now have easy, on-demand, access to the data they need.

    To illustrate these integrations, I?ll return to completions and look at a specific example. A workflow that a Completions Engineer might want to run through could include:

    1. Calculate well spacing using directional surveys
    2. Classify landing intervals
    3. Determine well orientation relative to the stress field
    4. Load completions summary data like total proppant or number of stages, lateral length
    5. Forecast production for existing wells to get EURs
    6. Build ML models to understand what levers really effect production and capital efficiency
    7. Save and share those models across their team or organization

    Walking through all the above would take more than a single blog post so here I?ll just look at how Petro.ai and Spotfire can be used to calculate and visualize well spacing.

    Automated 3D Well Spacing Calculations

    This analysis requires two sets of data; wellbore directional surveys and formation structure grids. I?ll load these data sources into Spotfire using Petro.ai?s integration panel.

    Figure 2: Load data into Spotfire through Petro.ai

    With the surveys and grids loaded I can quickly view the data using the 3D Subsurface visualization from Petro.ai. Even before calculating the well spacing I can at least view the surveys and tops together inside Spotfire.

    Figure 3: Directional surveys and formation tops shown inside Spotfure using Petro.ai's 3D viewer extension

    First, click ?Tools > Subsurface > Classify subsurface intervals.? Clicking this will bring up the ?Classify Subsurface Intervals? window. Here you be prompted to fill in the dropdown menus with the relevant information.

    Figure 4: Classify subsurface intervals (and Gun Barrel) menu

    The top left section is used to map columns to your wellbore surveys table. Select your wellbore surveys data table, then fill in the X, Y, and Z dropdowns. The top middle section is used to map columns to your wellbore surface grids. Select your horizons data table. This will be a table that has your horizon grid data points. The top right section is used to configure the output table and column names. Transfer Columns are simply the additional columns that will be displayed in the output data table, check the column name boxes to include any metadata columns that will help you identify your wells.

    The bottom half of the window enables the Gun Barrel calculations to run and allows the user to configure settings for the gun barrel. To enable the gun barrel, check the ?Enable Gun Barrel? checkbox. In the Wellbores section, select the Well identifier and MD from your Wellbore Survey data table. Next, use the input boxes to define buffer dimensions around the lateral. The right side, Gun Barrel ? Output, is used to name the spacing result table generated by the calculation. Use the input box to update the table name. At this point, you are ready to run the calculations: click the OK button.

    Creating a Gun Barrel View

    With all the proper data imported and mapped, it is now possible use the Gun Barrel workflow. It?s possible to run the interval classifier and gun barrel calculator in any Spotfire DXP using Petro.ai software, but I recommend starting with the Gun Barrel Spotfire template, as it?s preconfigured to automate this workflow.

    Select the Gun Barrel, mark a group of wells on the Map Chart, then click the ?Update Gun Barrel? button on the left side menu. And that?s it! You have kicked off the calculation for the 3D distance between the horizontal midpoints of each selected wellbore. Just monitor the Spotfire notifications area to see when the function execution completes.

    Figure 5: Map chart for selecting wells

    The map chart above is displaying the horizontal midpoint X-Y points. Depending on your preference, you can change these to the ?heel? or ?toe? if you have those points available to you in your data set. After running the gun barrel calculation, the results are displayed in three additional visualizations to help the user interpret these findings. Each will be explained below.

    Figure 6: Gun Barrel Spotfire Template

    The "Lateral Location" gives a 2-dimensional view of the wells, with toe, heel, and midpoint indicated.

    The ?Spacing Report? cross table shows a tabular view of the data. This takes each combination of wells and displays the 3D distance between each of the combination pairs (e.g. A to B, A to C, and B to C). This view also provides the distances dx, dy, and dz of each of the midpoints between the well pairs, and a flag indicating whether the combination crosses a horizon interval.

    And lastly, the "Gun Barrel" displays the data in a 2D vertical cross-sectional view through, and perpendicular to, all wellbore lateral section midpoints, allowing the user to view the horizontal midpoint wellbore paths head on.

    Next Steps

    We now have our spacing report as well as 2D and 3D representations of the well spacing. This valuable information can be folded into subsequent workflows to determine how well spacing might affect well performance. This is just an introduction to the many tools and templates available from Petro.ai and powered by the PetroPanel. Again, if you?d like to learn more about Petro.ai, check out www.petro.ai. TIBCO will also be co-hosting a meetup led by Petro.ai in Denver on September 24th. Petro.ai is a Platinum Sponsor of the TIBCO Analytics Forum which will be held in Houston on October 28-30th.

    7.png.45999283b0b1517c7bf601d0265d4557.png Neil Kanungo - TIBCO Data Science
    Neil specializes in data visualization and business analytics. Originally a customer of TIBCO himself, Neil offers a unique perspective on how organizations operationalize analytics at different levels, and how modern analytics software can empower TIBCO?s customers through their digital transformation. His interests include Geoanalytics, Design, and Industrial IoT.

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