Posts by Bertrand (senior advisor)

April 6, 2016 by

A decade of innovation: from VISAGE to VERDAZO

VISAGE has rebranded to VERDAZO. See the press release. How We Got Started Some technologies are designed to disrupt. Ours was designed to do the opposite. When we founded Visage Information Solutions as a two-person shop in 2006, it wasn’t because of a desire to render another technology obsolete or upend established systems in the Oil & Gas industry. It was to create visual data analytics software that could help everyone make smarter, faster decisions with the datasets they were already using. We knew there were frustrations for many business users who often spent hours a day just gathering data (before they could even start to analyze it). There was a clear unmet need. Still, we were pleasantly surprised how quickly VISAGE struck a chord with our users. When they saw the analytic capabilities it contained, and how quickly it could make a difference in their day-to-day work, they jumped onboard. The Growth Years With each passing year as our business grew, we focused on enhancing our product so every new release of VISAGE offered more specific and more relevant value. We charged everyone on our team with understanding our clients’ data analysis challenges and ensuring VISAGE addressed them. And...

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March 21, 2016 by

Type Curves Part 8: EUR, Value, Uncertainty & Auto-forecasts

Editor’s Note: While VISAGE rebranded to VERDAZO in April 2016, we haven’t changed the VISAGE name in our previous blog posts. We’re proud of our decade of work as VISAGE and that lives on within these blogs. Enjoy. The steep decline rates of multi-stage horizontal wells result in production being more heavily weighted towards the early life of the well. The point at which 50% of the well’s EUR is produced (i.e. the “Half-life” of the well) occurs at approximately 20% of the well’s life (in this Montney example). The Half-life of a well is an important measure in that it correlates strongly with the point where 80% of a well’s value has been achieved (see example chart below courtesy of Rose & Associates). That is, roughly 80% of a the well’s value will occur in the first 20% of its life. While this will vary from play to play and with well design, it does make a salient point… the bulk of the value is achieved quite quickly.This has a variety of implications. If roughly 80% of the value occurs in the first 20% of the life of a well, then value will inherently be a bigger driver for investment decisions. It...

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February 15, 2016 by

Alberta Modernized Royalty Framework: C* Winners and Losers

Editor’s Note: While VISAGE rebranded to VERDAZO in April 2016, we haven’t changed the VISAGE name in our previous blog posts. We’re proud of our decade of work as VISAGE and that lives on within these blogs. Enjoy. With recent information released about the Revenue Minus Cost (RMC) approach of the Alberta Modernized Royalty Framework (MRF), there has been much discussion within the industry about the C* capital cost calculation. At VISAGE we have been donating our time and resources to industry associations to provide analytic insights into the C* calculation. Today I will share with you my own analysis and shed some light on the plays that are likely to be winners or losers of the C* calculation. My motive is to offer statistical insight, fuel discussion and explore opportunities that may minimize unfair advantages, or disadvantages, to any particular plays, and operators. C* = a1 × (TVD) + a2 × (TVD – Vdeep) + a3 × (TVD × TLL) The C* formula calculates the Drilling and Completion Capital Cost Allowance that can be recovered during the pre-payout 5% royalty period. A nice summary can be found in the GLJ blog The Modified Royalty Framework: What We Know and Don’t Know.  It...

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February 8, 2016 by

Type Curves Part 7: Survivor Bias

Editor’s Note: While VISAGE rebranded to VERDAZO in April 2016, we haven’t changed the VISAGE name in our previous blog posts. We’re proud of our decade of work as VISAGE and that lives on within these blogs. Enjoy. A common issue with Type Curves is that they are “Survivor Biased”, and can provide an unrealistic (optimistic) production outlook. Survivor Bias Definition: as depleted wells are excluded from the monthly average-production-per-well calculation, the Type Curve values are biased by the surviving wells (i.e. wells that are still producing). As such the Type Curve does not reflect the actual average production one might expect taking into account wells that are depleted (or shut-in permanently). This chart illustrates how Survivor Bias can cause latter-life production increases in a Type Curve. Few software products provide Survivor-Bias Controls that allow you to include zeros in the production average after wells are identifiably depleted. The challenge when dealing with public production data is how to identify that a well is depleted. At VISAGE we chose to use a “period of non-production” as the mechanism to identify a depleted well. The user can define the length of this period (e.g. if a well has not produced in...

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February 1, 2016 by

Type Curves Part 6: Operational/Downtime Factors on Idealized Curves

Editor’s Note: While VISAGE rebranded to VERDAZO in April 2016, we haven’t changed the VISAGE name in our previous blog posts. We’re proud of our decade of work as VISAGE and that lives on within these blogs. Enjoy. Type Curves have many complexities and can be developed to serve a variety of purposes. As such any decision maker who is using Type Curves (especially Idealized Type-well Curves) as part of any decision making process should be asking: How was this type curve developed? What does it represent? Is it being used to inform economic decisions or development plans? Yes… then has it been scaled to accurately reflect operational realities? (i.e. scaled with an Operational/Downtime Factor) There are several approaches to calculating an Operational/Downtime Factor for your Idealized Type Curve. Three example approaches include: 1) Downtime Approach This is = (Hours Producing / Hours Available). While this is an easy calculation to perform can be the least reliable. The main weaknesses are: This is not a “production-weighted” factor (e.g. if more downtime happens in the small producers, then you could be applying too large a factor to your large producers … and vice versa) The downtime percentage can vary year over year … should you apply different...

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January 18, 2016 by

Type Curves Part 5: Condensing Time (Idealized Type Curves)

Editor’s Note: While VISAGE rebranded to VERDAZO in April 2016, we haven’t changed the VISAGE name in our previous blog posts. We’re proud of our decade of work as VISAGE and that lives on within these blogs. Enjoy. A technique that is often used to compare wells or to augment the production decline profile of a well is “condensing time”. While this technique has its merits, it should be used with caution… it results in an “Idealized Type Curve” that may significantly over-represent the production (and value) you can expect from a well. You run the risk of falling short of your production targets if you don’t take into account realistic downtime expectations (I will discuss this in detail next week). This should give an even greater impetus for decision makers to ask, “How did you develop your type curve?”. The two most common techniques used to condense time include: 1) Removing Time Periods with no (or low) production; and 2) measuring production in the context of “Cumulative Producing Time”. Removing Time Periods By removing time periods with no production you are aligning producing months across the data set. While this is a good practice on Rate vs Cumulative Production charts (which do not effectively...

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December 14, 2015 by

Type Curves Part 4: Calendar Day vs. Producing Day

Editor’s Note: While VISAGE rebranded to VERDAZO in April 2016, we haven’t changed the VISAGE name in our previous blog posts. We’re proud of our decade of work as VISAGE and that lives on within these blogs. Enjoy. This will be the last blog of 2015, so I’ll take this opportunity to say thank you for reading, happy holidays and best wishes for a happy, healthy and successful new year! I’ll continue this series in 2016. Type Curves that use Calendar Day production rates can yield very different results than Producing Day production rates. Each approach has its own strengths and weaknesses. Understanding how best to leverage their strengths and be cautious of the dangers is an important part of creating and using type curves. You can also combine the two to get valuable insights about the operational efficiency of a collection of wells. Calendar Day Rate = (monthly volume) / (days in month) The strength of using Calendar Day (CD) rates is that they are inherently representative of operational reality (i.e. what actually happened). Thus they are well suited for comparing the operational performance of companies, vintages, technologies etc. The weakness is that if there is significant downtime the shape of the type curve may not reflect...

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December 7, 2015 by

Type Curves Part 3: Normalization

Editor’s Note: While VISAGE rebranded to VERDAZO in April 2016, we haven’t changed the VISAGE name in our previous blog posts. We’re proud of our decade of work as VISAGE and that lives on within these blogs. Enjoy. Normalization of data is a means to improve the comparability of wells or groups of wells. Today’s blog will cover three types of normalization: 1) Time Normalization: alignment of time periods (months) relative to a date or event 2) Dimensional Normalization: scaling production values relative to a well design parameter 3) Fractional Normalization: scaling production values relative to the peak rate Each approach serves a distinct purpose and together they can be used to build an informative narrative. 1) Time Normalization There are two common dates used to align time, “First Production Date” and “Peak Rate Date”. Each has its own strengths and weaknesses. First Production Time Normalization Strength: on larger well sets, this communicates the average production profile taking into account variability in time to peak. This is suitable for some comparisons (e.g. operator, vintage). Weakness: the resulting type-well curve may not accurately reflect production decline behavior (as shown in the example below). Peak Rate Date Time Normalization Strength: the type-well...

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November 23, 2015 by

Type Curves Part 2: Analogue Selection

Editor’s Note: While VISAGE rebranded to VERDAZO in April 2016, we haven’t changed the VISAGE name in our previous blog posts. We’re proud of our decade of work as VISAGE and that lives on within these blogs. Enjoy. The first step in developing your Type Curve is the selection of your analogue wells. Analogue wells should have a similarity on which a comparison may be based and should represent the range of possible outcomes (i.e. don’t just select the best wells). However, by selecting wells with similar characteristics you can reduce the range of uncertainty in your type-well curve. The more common well attribute categories that are considered when selecting analogue wells include: Geology Reservoir Well Design Well Density Operational Design Here are some insights and perspectives on these well attribute categories that should shed some light on the importance of a thoughtful analogue well selection process. Tools like VISAGE can help make this process much faster and easier. Geology and Reservoir While there is data from public data vendors that can contribute to your geological and reservoir understanding, it is largely going to come from studies and the knowledge base built by your internal geologists and geophysicists. Use of maps...

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November 16, 2015 by

Type Curves Part 1: Definitions and Chart Types

Editor’s Note: While VISAGE rebranded to VERDAZO in April 2016, we haven’t changed the VISAGE name in our previous blog posts. We’re proud of our decade of work as VISAGE and that lives on within these blogs. Enjoy. Type Curves or type-well curves are a foundation of reserves evaluations, development planning, production performance comparisons and completion optimization analysis. The dangers of not understanding the complexities of Type Curves, and failing to communicate how they were designed/developed, can result in large statistical variability, inconsistent information used in development decisions, and unattainable economic plans (especially in the unforgiving times of  low commodity prices). This blog series will focus on essential Type Curve considerations and analytic techniques that should be part of their development and use. The complete contents of this multi-week blog series will be presented at the upcoming SPE talk Understanding Type Curve Complexities & Analytic Techniques  on Dec. 1st, 2015. Please join us. There are many ways to build Type Curves and they can yield significantly different results. In the example below, the left image shows six versions of a Type Curve for the same data set. The right image shows how different the cumulative revenues are for the first...

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