Blog

INNOVA x ROGII Integration!

Nov 18, 2023

The 2023 Geosteering World Cup Champions!

Nov 16, 2023

AAPG Ice Madrid

Nov 7, 2023

ADIPEC 2023

Oct 8, 2023

GWC 2023

Sep 28, 2023

ROGII Tech: Abu Dhabi

Sep 13, 2023

ROGII at IMAGE 2023

Aug 30, 2023

Strategic Partnership with TGS!

Aug 17, 2023

TGS and ROGII Inc Announce Strategic Partnership *

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HOUSTON, Texas (17 August 2023) – TGS, a global provider of energy data and intelligence, announced today a strategic partnership with ROGII Inc. to jointly provide an integrated solution that allows engineers and geologists to easily access TGS licensed well data within ROGII’s cloud-based platform, Solo Cloud. This will enable customers to quickly identify the right data for their geosteering and well planning workflows.

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ROGII Inc. focuses on creating a multi-disciplinary, collaborative environment through Solo Cloud, ensuring geoscientists, drillers, completions engineers and many more can work together in on one centralized dataset. Its intuitive interface, featuring automatic data loading and project synchronization, ensures rapid access to the latest information, minimizing complexity and enhancing productivity.

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TGS well data can be accessed within ROGII’s Data Manager application by enabling the TGS wells layer on the map to observe the diversity of the dataset. Logging into the user's respective TGS account provides seamless access to importing data directly into a project within their Solo Cloud account or examining it with the gun barrel plot feature.

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Jan Schoolmeesters, EVP of Digital Energy Solutions at TGS, said, "Our partnership with ROGII marks a significant step in enhancing the accessibility and usability of valuable well data for drilling engineers and development geologists. This collaboration aligns seamlessly with TGS's commitment to providing cutting-edge solutions that empower professionals to make smarter, more informed decisions."

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Julian Stahl, Vice President of Global Sales at ROGII, said, “We are excited to release this new partnership to our mutual end-users. TGS's extensive, curated well database powered by ROGII's automated, machine learning workflows will take well placement analytics to the next level. We constantly strive to reduce clicks for our users. This seamless, backend database integration will greatly reduce ETL workflows across the geoscience discipline."

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With the largest subsurface dataset in the energy industry, TGS uniquely combines unparalleled volumes of quality-controlled well data with proven geological expertise. For more information, visit TGS Well Data online at https://www.tgs.com/well-data-products.

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About TGS

TGS provides scientific data and intelligence to companies active in the energy sector. In addition to a global, extensive and diverse energy data library, TGS offers specialized services such as advanced processing and analytics alongside cloud-based data applications and solutions. For more information about our products and services and who we are, visit TGS.com.

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*Press release taken from official TGS website

https://www.tgs.com/press-releases/tgs-and-rogii-inc-announce-strategic-partnership?utm_content=260978339&utm_medium=social&utm_source=linkedin&hss_channel=lcp-220114

ROGII Tech - Houston

Aug 11, 2023

ROGII at DEVEX Conference

Jun 20, 2023

EAGE Vienna

Jun 6, 2023

Pan American Energy Drills With High Precision Using ROGII Technologies

May 24, 2023

ROGII Tech: Doha, Qatar

May 12, 2023

Geosteering Training in Bakersfield, California

ROGII Tech: Oklahoma

Apr 12, 2023

ROGII Tech: Calgary

Apr 10, 2023

EAGE Digital. London, UK

Apr 6, 2023

MEOS GEO 2023

Feb 22, 2023

Lunch'n'Learn in Winter Park!

Feb 16, 2023

NAPE Expo 2023

Feb 10, 2023

Technical Session in Malaysia

Jan 16, 2023

Solo WITSML™ Store is now certified by Energistics against the requirements for WITSML™ v1.4.1.1!

Nov 17, 2022

CONEXPLO 2022, Argentina

Nov 15, 2022

New Data Manager feature - Gun Barrel Plot!

Nov 14, 2022

Middle East GWC 2022 Winner

Nov 12, 2022

Geosteering Workshop in Stavanger

Nov 10, 2022

Solo Cloud Replaces Dated Drilling Operation Workflows!

Nov 5, 2022

Houston, Texas (November 5, 2022)

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A large operator in Houston deployed ROGII’s Solo Cloud three years ago in one of their many asset teams to evaluate collaborative, cloud-based workflows for their geologic operations team. Solo Cloud is hosted on Amazon Web Services with complete disaster recovery and business continuity protocols audited annually by the accredited SOC 2 Type 2 certification process.

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Changing existing workflows in large operators, especially for real-time operations where any mistake can cost millions, is always a difficult task. However, the operator realized that their existing workflows,communication and data storage mechanisms had reached the limits and had great potential to be optimized through evolving cloud solutions and technology. Their introduction of Solo Cloud was largely focused on streamlining communication and data flow.

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Prior to deploying Solo Cloud, typical communication mechanisms revolved around emails, PDFs, Microsoft Teams Messaging, WhatsApp,SMS and more. These channels of communication came into existence organicall yover many years. With increasing rigs, the operator realized that bits and pieces of information were scattered across multiple platforms that were not queryable, secure, archivable and ultimately not centrally manageable.

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While drilling, real-time data was being streamed from the rig into cloud based WITSML servers to be distributed to the various end user sat the operator office. However, the data ended up in individual projects stored locally on the operator’s disk space. Multiple rigs could be within the same square mile and yet, data still was siloed into individual projects on users’ computer.

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The operator realized the potential in centralizing all data in one cloud location, enabling true, real-time collaborative interpretation workflows with mobile and web apps for seamless communication with all stakeholders.

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After implementing Solo Cloud, real-time WITSML data was streamed directly into Solo Cloud’s series of databases (PostgreSQL, S3,ClickHouse and more). The data is instantly available to multiple operations geologist and drillers with advanced conflict resolution if a single dataset is edited simultaneously by multiple users. This collaborative environment drastically cut down on manual PDF reports, long phone calls and messaging threads. Communication was centralized through ROGII’s native cloud apps.Geologist, drillers, service companies, and managers were all able to view the same dataset from their computer, web browser, or native phone app ensuring that hazards are avoided in a timely manner.

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Since the implementation in the single asset, the operator now has standardized all their assets’ drilling operation workflows in Solo Cloud. The data is centrally managed with full decision and change logs readilyavailable. Furthermore, the operator has direct access to their large volume ofdata in 3^rd party analytical solutions like Spotfire and PowerBI for customized analytics through API and PythonSDK connections.

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Seismic in StarSteer

Nov 3, 2022

ADIPEC Exhibition 2022

Nov 3, 2022

SPWLA Golf Tournament

Oct 28, 2022

Solo Cloud Streamlines Real-Time Data Workflows

Oct 28, 2022

Dallas, Texas (October 28^th, 2022)

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A Dallas,TX-based operator is deploying ROGII technology hosted on Amazon Web Services’ (AWS) cloud service. Using several SoloCloud-connected applications, the operator can simultaneously stream live data in from many geographically remote field locations, view and interpret the data, broadcast project updates onto mobile devices in real time, and make available the data to other company domains for further analysis as well as other internal software applications. All the while, the data stays 2-way encrypted while in transit and redundancy is minimalized.

At the rig site while drilling, live surface and downhole data is collected and aggregated via ROGII’s Solo Box service. With Solo Box, the operator creates formatting schemas that execute automatically when new data is received. This ensures that only clean, formatted, and quality checked wellsite data reaches ROGII’s WTSML Server hosted on AWS, which maintains a 99.998% uptime reliability. The clean data is then streamed from the WITSML Server to the operator’s AWS-hosted Solo Cloud database account via the Solo Feed connector. Solo Cloud will then update various projects across several applications, and govern access based off an individual user’s assigned permissions.

The operator uses several licenses of ROGII’s StarSteer, a locally installed geosteering application, to interpret their data and confirm ideal well placement. For contingency and business continuity, they maintain one SaaS StarSteer license, utilizing an AWS Virtual Machine. Any updates to projects by any one of the authorized StarSteer users are automatically pushed up to Solo Cloud and communicated to all other users in real-time, ensuring that there is only one version of the project and no data redundancies. While the operator maintains a central real-time operating center, they also augment their staff with 3^rd-party consultants. Through a series of permission controls, they authorize the consultants to operate a small project subset without needing to grant access to the entire project, while still being able to maintain real-time oversight.

For internal communications outside of the technical staff,the operator utilizes ROGII’s StarLite iOS mobile application to broadcast geosteering interpretation updates to field personnel as well as to various managerial levels. StarLite connectsusers to Solo Cloud and provides a complete view of a StarSteer project. From their mobile devices, StarLite usersaccess real-time geosteering performance statistics in lieu of static PDF reports.

The operator also maintains a team dedicated to drilling optimization and has developed an in-house, proprietary application to achieve their goals. To incorporate the geological and geophysical data into their optimization models, the operator has chosen to integrate ROGII’s Solo API into their in-house application, which allows them to tap into their Solo Cloud database and stream pertinent data into their application for further analysis.

Starting as raw measurement data point on location, datamakes its return to the rig as an actionable drilling optimization solution for the operator. A network of various ROGII hardware, software and cloud products, all connected on AWS, make the workflow possible.

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Latin America GWC Winners!

Oct 25, 2022

Meeting KPI’s by Leveraging Cloud Computing Technology

Oct 21, 2022

Paloma Resources Identifies Efficiency Gains After Deploying ROGII’s Solo Cloud!

Oct 20, 2022

Houston, Texas (October 20^th, 2022).

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ROGII has an innate ability to identify tomorrow’s problems and address them today. Before the oil and gas industry identified the need to increase collaboration, ROGII was working on a solution that would break down the walls and change the game for operators. It didn’t take long for clients, like Paloma Resources, LLC, to grasp the value of SoloCloud (powered by AWS) and deploy it for their drilling operations.

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Harrison Ohls,Geologist at Paloma Resources, LLC states “ROGII’s Solo Cloud has been incredibly helpful for our operations geology team. Before Solo Cloud, our team had to spend time sending data and projects back and forth between users, which required many steps to format and compress the data properly. With Solo, we now have easy access to a single project in the cloud. The flow of data between all geologists working on a project is seamless and instantaneous, allowing for different users to compare their ideas and interpretations with others.”

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Harrison touches on another very important aspect of Solo Cloud aside from collaboration and that is the data being stored in a single database. Not only is Solo Cloud breaking down the walls for colleagues to collaborate, but their data is now integrated as well. This opens the door far beyond the immediate task at hand by allowing corporations to look at all of the data together to identify trends they may not have identified before.

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Harrison continues, “SoloCloud has been helpful in unexpected areas as well. For example, our IT group needs to make constant adjustments and upgrades to our office computer network,which requires a network shutdown. In the past, because our StarSteer project was stored on that network, we would have to coordinate IT’s maintenance withour operations. This downtime was inefficient and could potentially contribute to an issue with drilling. Now that everything is on the cloud with AWS, this is no longer an issue.”

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Based in Houston, TX,Paloma Resources is not new to hurricanes or flooding. “This inclement weather can cause power outages at our offices and shut down our network. Before ourdata was on the cloud, we were always at risk of losing our data access during these storms; if this happened while drilling a well, it could be disastrous. It’s one extra reason why we are glad we made the switch to Solo,” continues Harrison.

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Because of Solo Cloud and AWS, our clients can work without the risk of losing their data. It is ourgoal at ROGII to provide our clients with state-of-the-art software solutions that they are happy with. “We couldn’t be happier with our upgrade to StarSteer and Solo Cloud and suggest that anyone debating about making the switch to go ahead and make the plunge. It’s worth it,” confirms Harrison.

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DTM x PetroNinja Collaboration!

Oct 5, 2022

4th Annual Geosteering World Cup Wrap Up

Sep 28, 2022

HOUSTON, TX (September 28^th,2022)

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Every year, ROGII conducts a global geosteering competition called the Geosteering World Cup, where experts in the field of geosteering compete to be the best in the world and be crowned the champion. In this competition, all participants drill their own well through a pre-defined 3-dimensional geologic model, receiving simulated data in real-time at consistent increments. Using the data, they interpret the wellbore position and predict where it should bein order to drill the best possible well. With participants from over 20 countries,the only way to ensure that this competition runs without any issues is to use our Solo Cloud solution hosted on AWS.

This year ROGII‘s 4^th Annual Geosteering World Cup was the biggest event to date. The competition consisted of over 200 participants and 1 machine learning robot. During the event, there were roughly 10,000 changes made to the cloud every minute, with a processing time of about 0.2 seconds per change. Participants drilled approximately 800 simulated wells in 1.5 hours, which is equivalent to 2 weeks of drilling by all United States operators. To put this on a footagescale, that is about 4 million lateral feet drilled. At the end of the event, analytics provided by AWS was used to identify the champion based on the function of in-zone percentage and high ROP. Though not the winner, ROGII’s robot placed 26^th place,which is a big leap for machine learning.

The data from the competition is publicly available for data analytics/machine learning tasks because it is stored on Solo Cloud. Not only is this competition a great opportunity for high-load testing and a way to test and improve automation through machine learning, it is a great to way bring people together. ROGII’s goal is to bring data and people together to continue to strive for excellence. SoloCloud, powered by AWS, makes this possible.

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Figure 1. Cross-section view of participants “as-drilled” wellbores. The color back drop is a seismic image that is used to help geosteerers visualize the subsurface structures and bed dips. The greencolor-filled zone represents the oil-bearing target interval. The individual purple lines represent the final well paths for each participant. The blacksolid line represents the best human well path, while the red line represents the robots. The goal of the participants is to attempt stay within the green target interval for the entire well.

ROGII's Mile High Lunch n' Learn in Denver!

Sep 23, 2022

GWC 2022 Is Over!

Sep 22, 2022

SPWLA Chapter

Sep 10, 2022

Seismic on Solo Cloud!

Sep 2, 2022

IMAGE 2022!

Sep 2, 2022

ROGII x Stratagraph at Eureka Heights!

Aug 26, 2022

StarSteer 2022.2. New Release!

Aug 24, 2022

Geosteering World Cup Webinar

Aug 15, 2022

A Day with Terra Guidance!

Jul 27, 2022

StarSteer Testimonial!

Jul 7, 2022

URTeC 2022!

Jun 23, 2022

SPWLA Norway 2022

Jun 13, 2022

Training Day in Latin America!

May 27, 2022

Webinar: New Release Features StarSteer 2022.1!

May 19, 2022

Golf Tournament in Houston.

NAPE 2022 Houston

Feb 11, 2022

StarSteer 2021.3 Release. New Features!

Dec 28, 2021

Stochastic Inversion in StarSteer 2021.3!

Dec 14, 2021

ROGII Holiday Party

Dec 8, 2021

Congreso Mexicano del Petroleo

Dec 1, 2021

New StarSteer Videos added daily to Youtube

Nov 22, 2021

Introducing Starlite Rewind!!

Oct 12, 2021

StarSteer 2021.2 Is here!

Sep 15, 2021

Stochastic Inversion in StarSteer 2021.2

Aug 24, 2021

Announcing the release of Solo Connect 3.0!

Apr 28, 2021

StarSteer 2021.1 has Launched!

Apr 21, 2021

Solo Cloud 2021 and Beyond

Mar 19, 2021

Rogii is proud to welcome ADNOC to our distinguished StarSteer community

Jan 13, 2021

StarSteer 2020.4 is Live!

Jan 13, 2021

4Cast Part 4 - Applying Predictive Models to Enhance Decision Making

Dec 2, 2020

Part 4: Applying Predictive Models to Enhance Decision Making
By Andrei Popescu, 4Cast Product Owner

Welcome back to the 4th and final edition of this series on data analytics as a key to the future success of the Energy sector. It’s been a little longer so far than I thought when I initially set out to write these articles, but in truth, we have barely scratched the surface of this deep and complex topic. If you haven’t already, I’d invite you to check out the previous 3 sections:

Part 1: What is Predictive Analytics

Part 2: Data Mining and EDA

Part 3: Data Modelling

Just to recap quickly, we discussed predictive analytics broadly as a variety of statistical techniques that help us to analyze current and historical facts in order to make predictions about future events. We then outlined the data set we had to work with and some specific steps which we would tackle to increase our understanding of the drivers behind production:

Define the target variable that we want to predict which can help inform our strategic decisions - in this case, our target will be Length Normalized 12-month Production
Compile and visualize the available data in a consistent format, and one that can be directly compared to our target - since 12-month Production is measured at the Well level, our data should also be organized and reported at the Well level
Identify existing trends/patterns within our data, and define dependent relationships
Select and preprocess input variables - these input variables should be quantities that can be known with a high degree of confidence prior to drilling new wells
Build, test, and refine data models until we have one which can accurately predict historic results based on the defined input variables
Simulate a large number of potential future development options, and use the data model to predict the results
Identify the simulated option which is predicted to achieve the optimal result

Last week we took care of Steps 4 and 5, and we trained both a Random Forest and a Multiple Linear Regression model to predict length normalized 12-month production using the following variables as inputs:

Feature Variables: Proppant Concentration, Fluid Concentration, Stage Spacing, Total Number of Stages, and Average Porosity

So where do we go from here? We now have a model that can predict our future results with a reasonable amount of confidence, so how do we best utilize this model in our future decision making? We could of course manually run a handful of different potential development scenarios through our model, and see how they are expected to perform. If the scope of our future development is fairly limited, this may prove to be good enough, as it allows us to run each specific scenario and see which of them is predicted to achieve the best results. What happens, however, if the question we are faced with is much broader? What if we need to recommend the location and design for the most cost-effective wells to Management as opposed to choosing between a few different pre-planned options?

In this case, there is a myriad of different possibilities we would want to consider in order to ensure that we provide a robust and rigorously evaluated recommendation. As you can imagine, even with the help of our predictive model, manually designing and testing each individual scenario would be incredibly time-consuming, and would undoubtedly leave a large number of potential options on the table un-tested.

Fortunately, 4Cast can come to our rescue once again in this situation. We have at our disposal a tool that allows us to simulate an immense number of different scenarios (upwards of 2 million) very quickly and easily. What this means, is that we can model the results of millions of potential scenarios in a matter of minutes, and then spend our time where it really matters - identifying which of those scenarios is most optimal based on the known constraints of our upcoming development cycle.

Before we move along in our workflow and use 4Cast to simulate our potential future development, let’s discuss the theory behind this algorithm a bit. I want to be perfectly clear that we aren’t going to be generating random or arbitrary well parameters to run through our model as this wouldn’t be effective or useful. There are 2 main reasons that we can’t employ a simple randomization algorithm to generate our simulated scenarios:

Our model would be ineffective at generating predictions using random inputs since the inputs would deviate wildly from the data we used to train our model.
The simulated scenarios wouldn’t be at all realistic, so evaluating these possibilities is a waste of our time. For example, if we simulated potential well parameters using strict randomization, we could easily end up with the following inputs:
0.1 Tonnes/m proppant
1.5 Tonnes of proppant/m3 fluid
200m Stage spacing
85 Stages
3% porosity
This scenario is complete nonsense. All other variables aside, if we look just at 85 Stages with 200m Stage spacing that comes out to a 17,000m Well!

To make the best use of both our model and our time, what we want to do is evaluate scenarios that are both realistic (i.e. we could actually see ourselves drilling them), and exist reasonably within the bounds of our training data. Don’t get me wrong, we definitely want our simulated scenarios to deviate from what we’ve already done, otherwise why simulate new development in the first place? But we want to make sure that the range of scenarios we generate is at least somewhat within the range of our training data. After all, how can we expect our model to be accurate if the inputs we present it with are completely unprecedented for it? We created a machine learning model, not a self-aware AI :)

The best way to illustrate how 4Cast can help us achieve this is to simulate a small number of development scenarios first, say 150 of them, and compare those to our existing Wells (see below). The inputs for this function are all of our historic data points (same data we used to train the model), and the output is a set of feature variables (proppant concentration, fluid concentration, stage spacing, # of stages, porosity) for 150 potential future Wells. Below are a series of scatter plots showing the relationships of those variables within our historic data, and those same relationships in our simulated data.

Fig. 1 - Scatter plots comparing the total # of stages (x-axis) vs. proppant concentration (y-axis). The top plot shows the trend for our existing development (wells in our project area), while the bottom plot shows the data we simulated using the multivariate interpolation algorithm in 4Cast. Notice that the overall trend from our real data is preserved in the simulated data, but we have many more points which fill in the gaps of our actual data.

As we can see from the plots above, the multivariate interpolation algorithm does an excellent job of preserving trends that are underlying within our existing data, while also filling in the gaps in our data set which will help us produce a wider and more useful range of predictions. If we plot the remaining variable pairs in a similar fashion, we will continue to see this same pattern where we get clusters of simulated data that have similar parameters to our existing ones but differ just enough to give us a full range of realistic development possibilities. This is an incredibly powerful tool as it allows us to hypothetically execute any “design tweaks” we are thinking of applying, and see what the result of those changes is predicted to be by utilizing our predictive model. As we first discussed way back in the very first part of this article series: if we have a reasonable expectation of what the impact of our proposed design changes will be, we can make better and more informed decisions with respect to what design changes we actually want to commit capital to and execute.

So now that we have this incredible tool at our disposal, we have the freedom of simulating and evaluating virtually limitless different development options in order to identify which one(s) are optimal. For this evaluation, I’ll use the multivariate interpolation algorithm to simulate one million potential new Wells. When I say that we’re “simulating new Wells”, we are of course simulating new sets of Feature variables. Once we employ this algorithm, we will have one million new unique sets of: Total Number of Stages, Stage Spacing, Proppant Concentration, Fluid Concentration, and Average Porosity. If we had built our model to consider different inputs when generating predictions, we would of course want to generate those inputs instead.

We can now take these unique data points and run them through the predictive model we built in last week’s article. This will give us a broad range of possible outcomes to weigh against each other. As you can imagine, with 1MM different scenarios and outcomes, evaluation can be a bit tricky. Fortunately, 4Cast has us covered once again! We can use a heat map to help consolidate all of the information we have into a form that is more manageable and useful to us in terms of making decisions. One of the strengths of the heat map is that it allows us to compare the variables over which we have control against each other, while simultaneously setting constraints for the variables which we do not have direct control over. In our case, we actually have control over most of our input variables (proppant/fluid concentration, stage spacing, # of stages), with the porosity being the only constraint that we can’t directly influence. Let’s say in our potential development areas, the porosity ranges from ~2.5% - 4%, and we want to evaluate what the best options are for setting our other parameters up to maximize length normalized production while minimizing cost. Below we can see the optimal design for maximizing length normalized production is predicted to have 55 stages, 0 tonnes/m proppant concentration, 0.2 tonnes/m^3 fluid concentration, 54 meter long stages, and should be drilled in 4% porosity:

Fig. 2 Heat Map showing results of one million different predictions from our Random Forest model. Each bin’s color represents the length of normalized production. In this case, the x-axis shows the total number of stages and the y-axis shows proppant concentration. The Filters on the right can be used to set up any constraints that are present in the other variables (in this case, we are seeing only results based on the porosity of 2.5% to 4%). Hovering over any of the bins shows the average parameters of wells that fall into that range of normalized production.

The Heat Map in 4Cast allows us to swap between the various input variables we have defined our model to use on the X and Y axes, while also actively filtering the remaining variables based on any other constraints that may exist with regards to our development. If, for example, we wanted to investigate optimal ratios of proppant loading per meter with fluid concentration, we can simply change the axes of the map display and see how these parameters look when cross plotted.

Fig. 3 - Same plot as above but looking at a fluid concentration (x-axis) vs. proppant concentration (y-axis). The highest production per meter is predicted with a proppant concentration of 0 tonnes/m and a fluid concentration of 0 tonnes/m3 of fluid.

So there you have it, we’ve gone through and executed each of the steps we outlined back in week 1. I assure you that while this article series was prepared and published over the course of a month, the process itself was relatively quick and streamlined by utilizing the power of Solo and 4Cast. Depending on the initial data set you have available, an analysis like this could be reasonably carried out in a matter of days or even hours! I realize that this analysis wasn’t by any means exhaustive, and realistically there are many more variables and inputs we would likely want to consider, however, the concepts we discussed and the steps we carried out are more or less the same even with a more complex and varied data set. Off the top of my head, here some additional inputs which would likely be very useful to include in our analysis if we had access to them, and may warrant further investigation:

Geomechanical parameters and/or geophysical attributes for better reservoir characterization
Regional pressure trends
Observed drilling characteristics (mechanical specific energy, etc.)
Completion method (plug and perf vs. sliding sleeves, cluster design, etc.)
Observed frac hits/production interference
Cost data to help better identify optimal ROI (optimizing for the net return instead of production)

The list could go on for pages from here as there are so many different variables that can affect the outcome of our Wells. The idea is that by utilizing the approach and methods described in this article series, we can start to compare these various parameters (across disciplines) against each other and start to identify and rank order the relative importance of each. By doing this, we will not only gain a better understanding of our reservoir in a broad sense, but we will also be able to employ more methodical strategies to “engineering” our completion design in order to achieve the most optimal results.

I’ll end the article series here for now, with the caveat mentioned above: there are certainly many more variables at play which we could consider and use to provide a more in-depth and robust analysis. Please feel free to reach out to me directly or leave some comments below if you’d like to continue the discussion! If any of what we’ve discussed has been of interest to you and you’d like to learn more about 4Cast and how you and your Team can apply it to your own data, please contact us at northamerica@rogii.com to set up a demo and free trial. Thanks again for joining me throughout this series!

2020 Geosteering World Cup Winners Announced

Nov 17, 2020

4Cast Part 3 - Predictive Modelling and Leveraging the Results

Nov 17, 2020

Part 3: Predictive Modelling and Leveraging the Results
By Andrei Popescu, 4Cast Product Owner

Welcome back the 3rd installment of this series on data analytics as a key to the future success of the Energy sector. Hopefully you’ve found it informative thus far, and if you haven’t already I’d invite you to check out the previous 2 sections:

Part 1: What is Predictive Analytics

Part 2: Data Mining and EDA

Just to recap quickly, we discussed predictive analytics broadly as a variety of statistical techniques that help us to analyze current and historical facts in order to make predictions about future events. We then outlined the data set we had to work with and some specific steps which we would tackle to increase our understanding of the drivers behind production:

Define the target variable that we want to predict which can help inform our strategic decisions - in this case, our target will be 12-month Production
Compile and visualize the available data in a consistent format, and one that can be directly compared to our target - since 12-month Production is measured at the Well level, our data should be organized and reported at the Well level also
Identify existing trends/patterns within our data, and define dependent relationships
Select and preprocess input variables - these input variables should be quantities that can be known with a high degree of confidence prior to drilling new wells
Build, test, and refine data models until we have one which can accurately predict historic results based on the defined input variables
Simulate a large number of potential future development options, and use the data model to predict the results
Identify the simulated option which is predicted to achieve the optimal result

Last week we took care of Steps 1 through 3 (and to some extent 4), and came up with the following variables to move forward with:

Feature Variables: Proppant Concentration, Fluid Concentration, Stage Spacing, Total Number of Stages, and Average Porosity

Target Variable: 12-month Production (normalized for lateral length)

So, let’s pick up right where we left off and move to pre-processing our variables. Depending on your definition of data preprocessing, many aspects of it can certainly be considered a part of the initial data mining process which we discussed last week. For our purposes we will generalize data preprocessing as referring to one or more of the following tasks:

Data cleaning
Imputation
Transformation - categorization (binning) or continuation
Feature scaling

The above is by no means an exhaustive list, simply a representation of some of the techniques I’ve found most useful in my projects.

We discussed some aspects of data cleaning and organization in the last article (Part 2: Data Mining and EDA) when we reviewed the data structure of 4Cast. If you’re creating your workflows and preparing data for modeling in Python or R, you will need to spend whatever time is necessary compiling your data into some form of a spreadsheet (usually one or more CSV, txt, or JSON files) and use some custom scripting to combine the data into an organized format where your feature variables can be directly related to your target variable. None of this sounds fun or exciting, but without getting on my soapbox again (see last week’s article) this is the most critical step to ensuring success throughout the rest of the workflow, with few “shortcutting” opportunities.

One incredible advantage that we have working in 4Cast is that simply by using this as our analytical platform, we are working with a data set that is properly cleaned and organized on Solo Cloud, and very easy to QC in 3D (see below). In addition to this, simply by executing regular operational workflows (drilling/geosteering wells, recording log data, etc.) we continually add more and more data to our cloud database to help our future modeling efforts without any duplication of work or interruption to operational execution.

Fig. 1 - Input data structure for predictive modeling. Feature variables outlined in Green, Target variable outlined in Red.

Fig. 2 - Input data visualized in 3D. Wells are coloured based on their calculated proppant concentration (calculated last week). Cooler colors are lower proppant concentration, while hotter colors are higher.

So given that data cleaning is done, let’s look at some of the other operations. Imputation, or replacing missing portions of data with substituted values can be a very useful tool to fill in gaps in data. Some common ways of going about this would be to substitute either the mean or mode of the data we have available in the places where it isn’t. For example, if we had proppant volumes available for 95% of our wells, but not the last 5%, we could calculate either the mean or the mode of the data we do have and substitute it in for the Wells where it’s missing. In our case, the largest gap we have is actually in the production data we have available - every well with production data has all of the other variables (features) fully defined. We’re certainly not going to substitute production values for our missing Wells since the whole purpose of this exercise is to try and predict production in the first place.

Data transformation can be very useful as well. Some common methods for transformation include categorization (binning), and continuization. The two processes are essentially inverses of each other, but let’s outline an example of categorization as I find it to be the more intuitive of two. Imagine we had a data field such as “Facies” available, and we may have our different facies types numbered as Facies 1, 2, 3, etc. This could present a number of problems when it comes to training a machine learning model. For one thing, the model won’t know to treat this value as discrete in the first place, so it may output a result that says the optimal well should be drilled in facies 2, which isn’t a reasonable or useful output. Furthermore, the model could go even further rogue and assume that the order and magnitude of the numbers actually matter. Categorization can eliminate these issues by allowing the model to treat these types of variables as discrete values, and not assign any importance to the order of magnitude of them. With our data set, we are again in an easy position as all of our variables are continuous, though if we had a Facies Log in either our Typewells or Lateral I would certainly use the operations in 4Cast to include this as an input (much like we did with Porosity).

The last step I mentioned above is feature scaling, also sometimes referred to as “normalization” or “standardization”, and we will go ahead and apply this type of preprocessing to our data. Feature scaling can take a number of different forms. For example, data normalization would take all of our training set data and scale it so that all the values fall between 0 and 1 (or sometimes -1 and 1). Standardization is similar but sets the mean of the data to 0, and the rest of the data falls within 1 standard deviation. We will be applying normalization to our data set, and I’ll cover exactly why we want to do this in a moment, but first I want to be very clear about the order of operations. When training a machine learning model, we will be using a “Training” data set, and a “Testing” data set. It is important to split the data into these two sets prior to applying feature scaling. This is because we do not want the data which we use to validate our model to influence the scaling algorithm applied to our data, since the idea is that this is brand new data that has never been seen before. So what we will do is apply feature scaling (in our case normalization) to the training data set, and then when we run the testing data set to validate our model, we will simply use the same exact normalization algorithm on our testing set as we did on the training set - regardless of what values are actually in the testing set.

So why are we bothering with feature scaling? As you’ll see in the upcoming steps, we’re going to evaluate 2 types of models simultaneously - a Random Forest model, and a Multiple Linear Regression model. The reason we need to apply normalization to our features is very clear if we take a closer look at the equation upon which our multiple linear regression model will rely. The equation has the following general form:

y = b0 + b1 x1 + b2 x2 + … + bn xn

So in our case, y is Normalized production, and x1, x2, etc. are stage spacing, porosity, proppant concentration, etc.. What does this look like if we take one of our data points at random, and plug it into this equation:

We get:

2899.8 (Norm prod) = b0 + b1(0 prop conc.) + b2(0 fluid conc.) + b3(54 stage spacing) + b4(57 stages) + b5( porosity)

Our input/output values range in magnitudes from 10^-2 for porosity, to 10^3 for Normalized production. With this type of variance, it will be incredibly difficult to accurately determine which factor is playing the greatest importance in predicting the Target (i.e. which bn matters most). By applying feature scaling, we set everything on the same magnitude as all of our values will lie between 0 and 1. We can see below the results of applying a simple normalization algorithm to our data set in order to normalize our variables to a scale of 0 to 1:

Fig. 3 - data prior to feature scaling (normalization)

Fig. 4 - data post feature scaling

Awesome. So now we have all of our data cleaned up, organized, and preprocessed. Let’s build some machine learning models! As I’ve been doing all of my work in 4Cast, I’m using Orange as my platform for applying these algorithms and building the models. 4Cast and Orange have a seamless connection which allows for all of this to be done incredibly quickly, and in a visual format - no complicated code required! Below is a snapshot of my workflow.

The first few steps are fairly straightforward - we are essentially defining the feature and target variables as we have outlined previously. Next we use the data sampler to split the data set into a training set (75% of the data) and a testing set (25% of the data). From there, we normalize the training set only - again, we don’t want our testing data to have any influence on the normalization process. Instead, we preserve the normalization function applied to the training and pass it to the model for application to future data.

Fig. 5 - model building pipeline. Notice that we split the data into a training and testing set prior to applying feature scaling. The scaling methods are then also passed to each model for use with future data inputs. Each model is evaluated using 10-fold cross-validation, then the model predictions for both the training set and the testing can be compared to the historic data in cross plots (below).

As you can see above, we have several tools for evaluating our models. The 10-fold cross-validation gives us some fast, quantifiable summaries of the results. Both models show reasonable correlation coefficients, with the Random Forest model edging out our Multiple Linear Regression by a bit.

Fig. 6 - Results of model cross-validation. R2 (R-Squared) shows the Random Forest model to be a more reliable model for predicting our target. On the right is a rank order of the importance of each variable in the models - overall, the main drivers are the total number of stages and the proppant concentration.

The last thing we want to do in order to test our models is to present them with brand new data that they haven’t “seen” yet - this is where our testing data set comes in. If you refer back to Fig. 5, you’ll see two outputs coming from the Data Sampler. The lower one represents our training set which was used to train these models, the upper one is the remaining 25% of our data which we will now pass to our models to generate new predictions. If our models are robust, they should be able to take this brand new data and accurately predict the normalized production based on the input variables. I’ve summarized the results in the scatter plots below:

Fig. 7 - Results of Test data set. In both graphs, the X-Axis shows the actual (historic) Normalized Production values. The Y-Axes show the Random Forest predicted production (top) and the Linear Regression predicted production (bottom). Both models do a reasonable job of predicting the production based on the inputs, with the Random Forest model showing a slightly better correlation.

There you have it! We’ve now generated two perfectly viable machine learning models which can be used to predict Length Normalized Production based on the following inputs:

Proppant Concentration
Fluid Concentration
Stage Spacing
Total Number of Stages
Average Porosity

It seems a little too easy, right? The reality is that 4Cast and Solo Cloud did most of the heavy lifting when it comes to the hardest part of this whole process which is data management and organization. I would also be remiss if I didn’t mention that there is certainly much more work that can be done to further refine these models, not the least of which includes expanding our data set to include more data points, and also more refined data types. Given our data set here, the main drivers are overwhelmingly the Total Number of Stages and the Proppant Concentration, but there is a myriad of other potential variables we can (and should) include in this type of analysis. For example, if we had seismic attribute maps, we could sample these values to our Wells to see what effect that property has on our outcome. Perhaps we should include the completion timing or order in which the wells were drilled as a variable. We haven’t even considered parent-child relationships, or whether any of these Wells were knocked down by offset frac hits. Needless to say, the possibilities for taking this simple workflow to the next level are nearly endless, but now we have a platform within which we can explore all of these possibilities ad nauseum.

I’ll leave it there for now as I think I’ve gone on for long enough. I’ll be back next week to preview an incredible tool that can help us make efficient use of our new models in future decision making. As usual, please feel free to reach out to us at northamerica@rogii.com or contact me directly if you have any questions, comments, or simply want to discuss. Thanks again for your time, and looking forward to keeping the discussion going!

Geosteering World Cup Finalists

Nov 16, 2020

We'd like to publicly congratulate our finalists for the North America and Latin America region.

Tune in tomorrow to see the finals and see what Region takes home the Geosteering World Cup!

The event goes live tomorrow at 11:00 AM CST! If it's anything like the semi-finals, you definitely won't want to miss it!

To watch the live event click Here.

Here's a full list of the Finalists competing in tomorrow's World Cup

North American Finalists

Evans Darkwa: Evans works at Diversified Well Logging, LLC he has 15 years of work experience as a Geologist. He enjoys reading and is a soccer fan.

Kevin Pelton: Kevin has 10 years of work experience. He enjoys camping and playing board games with friends.

Mike Bodnar: Mike works at Cabra Consulting in Calgary. He has 8 years of work experience and enjoys rock licking and collecting chopsticks.

Ethan Cook: Ethan works at Antero Resources and he has 1.5 years of geosteering experience. He enjoys spearfishing and playing chess.

Patrick Tobin: Pat works at Oxy and has 10.5 years of work experience. He enjoys playing basketball and eating pasta.

Brad Moon: Brad has 40 years of experience and is currently working as a Consultant. He enjoys hiking mountains, singing, and politics.

“Mystery”: Works at Matador Resources.

Chris Bender: Chris works at Gordon Technologies LLC. He has 13 years of work experience in M/LWD and the last 9 in geosteering. He enjoys flying drones commercially, learning Python for data engineering, and taking cybersecurity classes.

Jeremy Kassouf: Jeremy works at Chevron and he has 12 years of work experience. He enjoys whitewater kayaking, rafting, live music, and attending music festivals.

Kevin Glass: Kevin has 5 years of work experience in Oil & Gas. He enjoys mountain biking, skiing, hiking, etc.

Tyler Shade: Tyler works at Raptor Consulting and has 4 years of work experience and enjoys skiing and cycling.

Kevin Strode: Kevin has 10 years of work experience. He enjoys spending time with his daughters, reading fantasy novels, and building stuff in Minecraft with his daughter.

Karma Doescher: Karma is the Director of Geosteering at MCWL Paladin Geological. She has 6 years of experience in Geosteering, Operations, and Mudlogging. Her hobbies include coddiwompling and working on her top-secret invention.

Jesse Merchant: Jesse works at Calyx Energy III, LLC and has 11 years of work experience. He loves any excuse to be outside.

Jacob Leader: Jacob has been at Cimarex Energy for 6 years. He enjoys pottery and playing tennis.

Bill Honsaker: Bill is a Geologist with 9 years of work experience. He enjoys road/gravel cycling.

Jon Krystinik: Jon is an Independent Geosteering Geologist with 20 years of E&P experience. He has steered over 1,000 horizontal wells and enjoys family, autocross, and spending time out on the outcrop.

Dakota Kubler: Dakota Kubler is a Geosteering Consultant he enjoys fantasy football and hiking.

Ryan Rambo: Ryan is an Operations Geologist at WPX Energy.

“Enigma”: Player 97 works at Matador Resources.

Bill Larzelere: Bill works at Continental Resources has 8 years of work experience and enjoys playing soccer as a hobby.

Nicholaus Driscoll: Nicholaus has 8 years of work experience and enjoys woodworking.

Christopher Padilla: Chris has 10 years of work experience and currently works as a Freelance Operations Geologist. He enjoys playing guitar and camping.

Katrina Ostrowicki: Katrina Ostrowicki has 8 years of experience working at Chevron as a geologist, with 2 years of that being a Geosteering Specialist. She has experience geosteering Marcellus, Utica, Permian Basin, and Vaca Muerta wells. Her hobbies include aerial silks, fostering dogs from the local Houston animal shelter, and hiking with her fiancé and foster dog.

Sarah Regen: Sarah works at GeoVision as Geosteering Manager. She has 8 years of experience and enjoys cheesemaking and studying for her grad school classes.

Matt Minnett: Matt is employed at Shell Canada Ltd., has 6 years of Geosteering experience and enjoys hockey, hiking, and camping.

Christopher Perry: Chris works at Ovintiv.

Ghazanfar Zahid: Ghazanfar has 20 years of work experience in the oil and gas industry, 15 of those years were spent providing geosteering services for Haliburton and Baker Hughes. He enjoys hiking and swimming.

Danica Watson: Danica has been at Cimarex since March as a Geosteering Specialist. Before that, she was at Apache for 3 years as an Operations Geologist and Geosteerer. Prior to that she worked at Chesapeake as a Geosteerer for 2 years. She has over 6 years of work experience and enjoys reading, play trivia, hiking, and skiing.

Jason Harms: Jason has 9 years of work experience and enjoys skiing and mountain biking.

Latin America Finalists

Andrés Damián Askenazi Vera: Andrés is a Geologist with ten years of experience in the oil industry, mainly in unconventional projects, and the last ones developing Geosteering tasks. He enjoys traveling to mountainous areas and nature reserves as well as trail running.

Jorge Ramón Miguel Estrade: Jorge has 5 years of work experience as a Wellsite Geologist. He enjoys camping and cycling.

Andrea Infante: Andrea is a Geologist with 8 years in the oil and gas industry. She started her career in mining and has since dedicated her time working on geosteering projects in conventional and unconventional reservoirs. She enjoys any outside activities like trekking, nature, and swimming.

Noelia Caterina Di Giuseppe: Noelia has 3.5 years of work experience and enjoys singing and reading novels.

4Cast Part 2 - Data Mining and EDA

Nov 5, 2020

Part 2 – Data Mining and Exploratory Data Analysis (EDA)
By Andrei Popescu, 4Cast Product Owner

Let’s start by recalling the broad definition of predictive analytics from last week’s article: a variety of statistical techniques that help us to analyze current and historical facts in order to make predictions about future events. And now let’s take that definition a step further by pointing to the specific statistical techniques I’m referring to. I believe we can break down the process into 4 key components:

1.     Data mining (collection) and clean-up

2.     Exploratory Data Analysis (EDA)

3.     Training/refining data models

4.     Generating and evaluating predictions

Something I’ve seen commonly throughout my career is a heavy focus on getting to steps 2, 3, and 4 (the fun stuff), with as little time and resources spent on part 1 as possible – after all, we need to do more with less, and we need to do it quickly, right? I’ll take a hard stance here and say that part 2 will be very difficult, and the results of 3 and 4 will be mostly useless if we don’t change our mentality. Everybody’s heard the adage “garbage in, garbage out”, but the problem is that nobody believes that their data is garbage. The harsh reality that most don’t consider is that while their data may be excellent if it’s not compiled and organized in the appropriate manner, it’s as good as garbage. A Forbes study on actual time spent by data scientists would agree with me here, the graph below shows that roughly 80% of the process (and time/effort spent) is taken up by collecting, cleaning, and organizing data. Only then do we get to start having fun!

So let’s discuss the first component - data mining and clean-up. What this means is that our data not only needs to be compiled in one place, but it also needs to be organized in a consistent format. In last week’s article, we defined the following Steps we would be tackling:

1.     Define the target variable that we want to predict which can help inform our strategic decisions - in this case, our target will be 12-month Production

2.     Compile and visualize the available data in a consistent format, and one that can be directly compared to our target - since 12-month Production is measured at the Well level, our data should be organized and reported at the Well level also

3.     Identify existing trends/patterns within our data and define dependent relationships

4.     Select and preprocess input variables - these input variables should be quantities that can be known with a high degree of confidence prior to drilling new wells

5.     Build, test, and refine data models until we have one which can accurately predict historic results based on the defined input variables

6.     Simulate a large number of potential future development options, and use the data model to predict the results

7.     Identify the simulated option which is predicted to achieve the optimal result

We also completed Step 1 on the spot - we decided that our target variable to be predicted would be 12-month Production. Great. This week we’ll focus on Steps 2 and 3. So, given this, our next course of action is to compile the rest of our data in a consistent format, and one that can be directly compared to our target - so we need to organize our data on a well level basis - this step is where 80% of the project time is usually spent!

Fortunately for us, this is exactly where 4Cast and Solo Cloud help to make this process both easy and visual. Most companies in North America are already using StarSteer to drill and geosteer their wells (and if you aren’t perhaps you should be 😊) since 4Cast shares a common database with StarSteer through Solo Cloud, the first step in building a project is already complete as we can just connect to an already existing project in Solo. By doing this, we instantly have access to all of the trajectory, log, and geosteering data which has been created, compiled, and QC’d as a part of the standard operational workflow.

Of course, we want to bring in and calculate additional data to help with our analysis. In our example, we’ll start with the following high-level data which is usually publicly available:

·        Stage depths (start and end MD of each stage)

·        Fluid/Proppant volumes per stage

·        12-month Production per Well

Fig. 1 - 4Cast data structure accommodates variables to be defined and stored either at the Stage or Well Level. There is no limit to the number of columns we can define/import/calculate.

As you can see above, the data structure in 4Cast allows us to store any data we have at either the Stage or Well level, and it will automatically use certain Stage level attributes to calculate new attributes at the Well level. In this case, we used the Stage depths to calculate Completed Length, and the fluid/proppant volumes per Stage to get a total value for each Well - remember, since our Target variable (production) is measured at the Well level, we need our Feature variables to be organized in the same way. It’s also worth noting, that these column definitions aren’t set in stone - we can import/define custom columns depending on the data we have available, and also calculate new ones using our existing variables, as well as our Log data and Geosteering Interpretations.

Given that we have access to our Geosteering Interpretations, as well as the Typewells and Logs that were used to steer these Laterals, a logical next step would be to calculate some new variables using this information. In this data set, I had the benefit of Bulk Density and Density Porosity Logs in my Typewells. 4Cast allows us to use these Logs along with our Geosteering Interpretations to model these properties along all of the Wells in the project very quickly and easily. The results can be seen here:

Fig. 2 - Average Density Porosity calculated on a per Well basis for all Laterals. Results range from 0.5% (Blue) to 4.5% (Red). All calculations are automatically output to the Stage/Well spreadsheets, the 3D Colour View allows us to quickly and easily visualize and QC any of the data from the spreadsheets.

As you can see, we are already well on our way through Step 2 - we have compiled our available data in an organized and consistent format, and we can quickly QC it to identify broad trends or outliers. In the case of Density Porosity (shown above) our Wells are modeled to have an average of anywhere from 0.5% to 4.5% porosity, which is in line with expectations for this formation. To recap, the variables we now have available for our Wells are as follows:

Feature Variables: Completed Length, Number of Stages, Total Proppant, Total Fluid, Average Porosity, Average Bulk Density

Target Variable: 12-month Production

With a total of seven relevant feature variables, many of which we can meaningfully control in our future development plans (how long we drill our Wells, how many stages we pump, etc.), we could reasonably try to start training a data model to predict production right away. But before we get too ahead of ourselves, it would be a good idea to do some simple Exploratory Data Analysis (EDA) in order to familiarize ourselves with the data set. EDA is a broad term that could refer to a number of different techniques, but for our purposes, we’ll keep it straightforward - we want to investigate and summarize the existing trends in our data using visual methods. The goal is to potentially refine our input variables further, prior to moving on to training and refining a statistical model. There are a number of ways we could go about this next step, but my personal favorite is simply to start creating scatter plots of our various variables:

Fig. 3 - Plot of Completed Length vs. Total Proppant. Points are sized based on the Number of Stages and colored based on 12-month Production. A strong correlation here is not unexpected but indicates multicollinearity in our data set, which could cause problems for our future model.

I’ll spare you the summary of all the cross-plots I created (suffice it to say there were many) and we can focus on the important learnings from this exercise. By doing this simple analysis, we uncover that we have numerous variables that are actually quite closely correlated. This is an issue called multicollinearity - where changes in one of our independent variables are associated with shifts in another. Below is a summary of the dependencies which stand out:

Fig. 4 - Completed Length is closely tied to Total Proppant, Total Fluid, and Total # of Stages. Additionally, Average Porosity and Average Bulk Density have a nearly 1-to-1 relationship.

Some of these dependencies should come as no surprise, however, it’s important to note these and think about the effect these co-dependencies will have on our statistical model. Given that a large part of our goal is to understand the effects that each individual parameter has on 12-month Production, we should consider modifying our input variables. The reason for this is that including too many co-dependent variables will make it difficult for the model to distinguish the individual effects of each variable because the co-dependent variables tend to change in unison.

Looking at porosity and bulk density, we can see that these variables have essentially a perfectly dependent (or 1-to-1) relationship. In this case, we would actually be best served to simply remove one or the other altogether to avoid overfitting the model. With regards to Completed Length, it has a fairly close tie to the Total Proppant, Total Fluid, and Total # of Stages in our Wells. Again, this is to be expected but is also a little concerning since we can pretty easily extrapolate these co-dependencies to conclude that any model we build will assign a massive amount of importance to these variables as a group. This will result in us learning that longer Wells with more Stages, Fluid and Proppant pumped will give us better Production, but won’t actually tell us which of these variables individually is most important. I’m not sure about you, but to me, that sounds like an altogether useless result. So instead of blindly plugging our data into a machine learning algorithm and blaming “useless AI” for not giving us relevant answers, let’s instead see if we can’t modify our data set somewhat.

A simple thing to try at first would be to normalize Completed Length entirely. So specifically, let’s calculate the following variables to replace the co-dependent ones we identified above:

Fluid Concentration = Total Proppant ÷ Total Fluid
Proppant Concentration = Total Proppant ÷ Completed Length
Stage Spacing = Completed Length ÷ Number of Stages

While we’re at it, since we want to normalize Completed Length out, we should also change our Target to be 12-Month Production on a per Length basis:

Normalized Production = (12-month Production ÷ Completed Length) * 100

Now, we can not only remove Completed Length from our input variables (since it is represented within our new Features and new Target), but if we look at a summary of our Input Variable dependencies, we see that our Inputs are far more independent of each other.

Fig. 5 - We can see the correlations between our new Target Variables are much lower than seen with the previous variables.

Our new data set is as follows:

Feature Variables: Proppant Concentration, Fluid Concentration, Stage Spacing, Total Number of Stages, and Average Porosity

Target Variable: Normalized Production

I think I’ll leave it there for this edition. To recap, we’ve compiled our data set, investigated some of the underlying relationships, and did some simple analysis and data manipulation to ensure that our input data set is one well-suited to predictive modeling, and promising in terms of giving us valuable insights to take forward. We’ll save data preprocessing, predictive modeling, and evaluation of our results for next week. Thanks again for your time in reading this, and I look forward to hearing from you and continuing the discussion. As always, be sure to follow us to get the latest and greatest on everything Rogii related, and please don’t hesitate to reach out to our Team, or to me directly if any of the content here has piqued your interest!

4Cast and the Role of Predictive Analytics in the Energy Industry’s Future

Oct 28, 2020

Part 1 – What is predictive analytics?

- Andrei Popescu, 4Cast Product Owner

Big Data, Machine Learning, Cloud Computing, Predictive Analytics. We hear “buzz words” like these more and more in our industry these days. To many, these words imply progress, a technical (or technological) advancement in the way we manage and operate our fields compared to current practices, which can drive efficiencies and ultimately increase profit margin. By and large, I think the general consensus is that we should be applying these concepts and techniques in our operations, but the field seems not only split on how to best do this, but also to some extent on what these things even mean for the oil and gas sector.

As we’ve recently released our new platform 4Cast, I’d like to take a deeper look specifically into predictive analytics, and how it can be applied to oil and gas data sets to help make more optimal development decisions in our fields. While I’ll focus on core concepts of predictive analytics, I’ll note that we will of course touch upon the other buzz words mentioned above as well. After all, 4Cast is a cloud-based platform designed specifically to streamline the application of statistical analysis and Machine Learning algorithms to oil and gas data. I think I just hit most of the aforementioned buzz words in a single sentence 😊

So, what is predictive analytics? Broadly speaking, it’s a variety of statistical techniques that help us to analyze current and historical facts in order to make predictions about the future, or other otherwise unknown events. I got that from Wikipedia, but I can’t think of a more accurate way to put it. More concisely for our purposes: we want to use our existing Well data to predict what will happen with our future Wells. The main reason we would want to do this is to make better, more informed development decisions. If we have a reasonable and reliable expectation of the outcome of our actions, we can “simulate” multiple possible scenarios, and choose the scenario that results in the most desirable outcome from among the simulations.

Fig. 1 - A developed asset with 90+ Wells drilled over the course of several years. Completion systems and design varies across all wells, as do the results. Wells are colored based on 12-month cumulative production.

Let’s take for example a situation, as shown above, an asset with several years of existing development. This year, we have a certain fixed budget to drill and complete new wells, and we need to manage that budget in order to maximize production while minimizing cost - a situation many can relate to, I’m sure. In order to make the best use of our budget, we need to understand the effects that different parameters will have on our outcome. In this case, the parameters are the known characteristics of our reservoir (geological, petrophysical, etc.), Well design (drilling target, well spacing/stacking, etc.), and Completion design (proppant/fluid concentration, stage/cluster spacing, etc.), and the outcome is production.

A common approach to try and increase our understanding is to turn to “Frac Modelling”, or using defined physical and mathematical equations or relationships to define the size and shape of our fractures in the subsurface. Don’t get me wrong, I think this is a noble endeavor and certainly has merit as one of many variables involved in the overall picture. However, I believe that by focusing too much on this singular aspect, which is incredibly difficult to validate, we risk missing out on a large number of insights our data can provide us without knowing the exact size and shape of each individual fracture.

Consider that we have access to decades worth of data where various Well and Completion designs, which are known quantities, were applied. The production results of these different trials are also known with a high degree of certainty. Combine these knowns with additional geologic and geophysical data which can constrain reservoir quality and wellbore point of contact. What we have then is a large data set with many known, independent variables and a dependent variable – this is exactly what we need in order to put predictive analytics and the rest of the buzz words to practical use. Given data sets where we know the inputs (or many of them at least) and we know the results of those inputs, we can use well defined and rigorously tested statistical techniques to increase our understanding of the effects of each of our known variables on the outcome. Specifically, we can train and refine data models using machine learning algorithms to be able to predict outcomes, based on the known input variables.

Fig. 2 - A general outline for a predictive modeling workflow that can be applied to any dataset where we have known independent variables alongside known results. We’ll go into more detail for each of these steps in the subsequent articles using the example asset shown in Fig. 1.

Using the example situation from above of wanting to maximize production while minimizing cost, let’s first define our strategy for doing this in concrete steps:

Define the target variable that we want to predict which can help inform our strategic decisions - in this case, our target will be 12-month Production
Compile and visualize the available data in a consistent format, and one that can be directly compared to our target - since 12-month Production is measured at the Well level, our data should be organized and reported at the Well level also
Identify existing trends/patterns within our data, and define dependent relationships
Select and preprocess input variables - these input variables should be quantities that can be known with a high degree of confidence prior to drilling new wells
Build, test, and refine data models until we have one which can accurately predict historic results based on the defined input variables
Simulate a large number of potential future development options, and use the data model to predict the results
Identify the simulated option which is predicted to achieve the optimal result

In the subsequent articles in this series, I’m going to tackle each of the steps we just outlined using 4Cast, and a real data set. By doing so, we’ll take a deeper dive into what I believe are the key components of predictive analytics. Hopefully, this framework and example will help you to outline whatever questions are most important to your Team and apply an analytical approach to answering those questions.

Thanks for taking the time to read through my introductory thoughts on this topic, and hopefully I’ve piqued your interest to come back for the next edition. I would welcome anyone who has additional insights or would like to discuss anything I’ve mentioned here in greater detail to please chime in in the comments or reach out to us directly. Please be sure to follow us for the latest news on 4Cast, and to catch the follow-up articles in the coming weeks.

Geosteering World Cup FAQ

Oct 23, 2020

Geosteering World Cup Master FAQ

When will the Geosteering World Cup take place?

The Geosteering World Cup will consist of an Opening Round and a Final Round. The first round will take place on November 10th (Worldwide) from 1:00 – ~3:00 PM CST (North America region). The highest scoring competitors from the initial round will advance to the final round which will take place on November 17th (Worldwide) at 1:00 PM CST (North America region). Please look below for other region times.

Who can participate in this event?

This is a free event that is open to everyone to participate in. Participants will be divided up into regions depending on their country of residence.

What is the regional breakdown?

We have broken down the competition into 4 regions, they are as follows: North America, Latin America, Europe, & the Middle East/Asia/Oceania. Competitors from all regions are able to compete in either heat during the opening round as is more convenient for them.

Can I participate if I don’t have a StarSteer license?

Absolutely, we designed this competition to be open to everyone. If you do not have StarSteer installed on your computer or your license has expired we will provide you with a temporary license key to practice before the competition & participate in the event. If your computer cannot run StarSteer due to technical specs we will supply you with access to a Virtual Machine to facilitate this access. Participants in need of StarSteer will receive access 10 days before the competition.

How will the cup be conducted?

The cup will be hosted on ROGII’s Solo Cloud Platform and using ROGII’s StarSteer Geosteering Software. At 1:00 PM CST the competition will go live for the following regions: North America and Latin America, please see below for Europe & Middle East competition times. Competitors are encouraged to have logged into their accounts prior to the competition to identify any issues. Once the event begins competitors will have 2 hours to steer 2 wells, 1 conventional & 1 unconventional. The basins these wells are generated from will be announced prior to the Opening Round.

Is there a difference per region?

There are no differences between regions however the wells steered in the two different heats will be different as they are not happening simultaneously. Users from all regions can compete in either heat as is convenient for them. In the final, all competitors will be competing with each other directly using the same data.

What data will be used for the competition?

We will be using synthetic data for the competition generated from current public data. The basins which will be drilled in the competition will be announced prior to the Opening Round.

How will I be scored?

Participants will be scored on a combination of 2 factors: the highest percentage drilled in the Target Zone and average ROP. Each participant will receive their composite score as well as the solution after each well is completed.

How will my scores be compared to other participants?

Participants will receive their individual composite score for each well, their overall placement in the round, & position within the competitor pool.

What tools are expected to be used during the GWC?

Tools used during the competition for the conventional well include, but aren't limited to, MWD & LWD data. The competition may include steering with additional LWD data besides gamma.

Can you teach me to geosteer?

Unfortunately, due to a large number of registrants, we cannot teach participants in an individual manner. We will provide resources to learn on your own & provide technical assistance via email up to a week before the competition. If you are interested in learning more about our software or geosteering make sure to keep an eye out for our future ROGII U Geosteering courses & webinars.

What can I do to practice before the competition?

A project will be released 2 weeks before the competition using elements similar to those displayed in the Cup. Completion of this project is not mandatory to participate in the event. You will also have access to StarSim, our geosteering simulator when you receive StarSteer access.

When will the Competition briefing occur?

ROGII will host multiple briefings on November 5th to ensure all participants have StarSteer installed & are ready for the competition.

When will I be notified if I make it to the finals?

Participants that have made it to the finals will receive a notification by email on November 11, 2020.

When will the winner be announced?

The Geosteering World Champion will be announced on November 20 via LinkedIn & ROGII News.

What does the winner receive?

The overall World Champion will receive the 2020 Geosteering World Cup Trophy along with an Apple Watch. The champion will be recognized as the “Geosteering World Champion” & Champion of his/her region. Winners for the other three regions will be identified as “Regional Champions” and will receive an Apple Watch & a Certificate recognizing their accomplishment.

Will my scores be published publicly?

No, the only scores that will be made available to the public via social media and our website are those of the World Champion & Regional Winners. After the final round has been completed, we will list the top 20 finishers in order of rank. However, you are free to publish your scores & share them publicly as you wish. If for some reason you wish to compete anonymously please contact us directly and we can make arrangements for you.

What are the competition times?

North America
                                                     i. Houston, TX: 1:00 PM (CST)

ii. Denver, CO: 12:00 PM (MST)

iii.     Calgary, AB: 12:00 PM (MST)

iv.     Pittsburgh, PA: 2:00 PM (EST)

Latin America
i.     Buenos Aires, AR: 3:00 PM (AST)

Europe
i.     Moscow, RU: 2:00 PM (Moscow Standard Time)

ii.     Paris, FR: 1:00 PM (CET)

iii.     Hamburg, DE: 1:00 PM (CET)

                                                  iv.     Oslo, NO: 1:00 PM (CET)

Middle East/Asia/Oceania
i. Abu Dhabi, UAE: 3:00 PM (GST)

                                                   ii. Doha, QA: 2:00 PM (AST)

                                                  iii. Dhahran, SA: 2:00 PM (AST)

                                                  iv. Perth, AU: 7:00 PM (AWST)

Who can I contact if I have more questions?

You can email us at worldcup@rogii.com for any further questions.

This sounds Fantastic! Where can I sign Up?

We're glad to hear! Participants can Register Here

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Geothermal Geosteering

Feb 20, 2020

Stephen Clark
Senior Technical Analyst, ROGII

Geothermal is not generally something that gets much press in the renewable energy discussion. Historically, it has only been viable in regions with a shallow heat source and porous, permeable rock for water to flow through. This only leaves a handful of places in the world where geothermal energy can function at commercial levels. Eavor Technologies out of Calgary has leveraged unconventional petroleum technologies in horizontal drilling as well as Rogii’s StarSteer geosteering software to potentially expand the geothermal commercial envelope exponentially.

Eavor’s technology consists of creating a closed loop system of subsurface wells which takes advantage of a thermosiphon effect whereby hot fluids rise in the inlet well while cool fluids fall in the outlet well. In order for all of this to happen, two mirror image horizontal wells must be drilled to sufficient depth and steered to connect at their toe in the subsurface. The precision geosteering required to do this was provided by Chinook Consulting out of Calgary. Additionally, specialized magnetic ranging sensors (a technology widely used in thermal oil developments) were used to provide terminal intersection guidance once the wellbores were within 30 meters of each other. The importance of precision geosteering towards the successful intersection of 2 wellbores 2+ km underground can’t be overstated. Inherent directional survey rounding errors and the wells’ cones of uncertainty are common challenges when navigating reservoirs with horizontal wells. “Precision geosteering throughout the lateral sections allows the two wellbores to head at each other at the same stratigraphic level, setting the stage for magnetic ranging in the later stages of intersection,” Calin Dragoie of Chinook explained.

Well overlay across Calgary (photo credit: Chinook Consulting)

This innovative application of unconventional petroleum technologies like horizontal drilling, advanced mud systems, magnetic ranging sensors, and StarSteer could allow geothermal energy to greatly expand its potential footprint and scale up the energy intensity of geothermal developments. Further, the ability to sidetrack and connect mirror-image daughter wells from both the inlet and outlet wells allows for the scaling of this technology to be customized to suit the area it is meant to service.

For more information on Eavor Technologies, Chinook Consulting, or Rogii (StarSteer), please visit their respective websites:

·https://eavor.com/technology/

· https://chinookpetroleum.com/

·https://rogii.com/

ROGII Tech - Houston

Pan American Energy drills horizontally positioned well with high precision in the Vaca Muerta Formation

Stephen ClarkSenior Technical Analyst, ROGII

Stephen Clark
Senior Technical Analyst, ROGII