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Analyzing Oilfield Operator Performance: The Creaming Curve


Oilfield operators have many decisions to make about how to drill and complete their wells: how long of a lateral, how many perforation stages, what is optimal spacing, what type of proppant to use, how much fluid, etc. These decisions can be influenced by whether an operator seeks to maximize the total production over the life of the well, or to maximize internal rate of return.

Creaming curves summarize the results of many decisions on drilling and completing a well by graphically displaying an operator’s production performance over time. These graphs allow a user to quickly identify whether an operator is performing better or worse than its peers, to isolate changes in completion techniques, follow an operator’s learning curve in developing a play, and predict when an operator has exhausted its best acreage and production will start to plateau.

Examples of Creaming Curves

Drillinginfo creaming curves measure incremental production from wells in the order in which they are drilled. Various production metrics can be used: peak rate, six month cumulative oil, twelve month cumulative oil, etc. Figure 1 is a creaming curve for three operators in the Eagle Ford. It shows cumulative peak month oil production for wells drilled from January 2009 to January 2015. Using Drillinginfo proprietary Graded Acreage, we can compare operators normalized for reservoir quality (Drillinginfo Geology and Analytics teams grade reservoir quality to a single square mile resolution and categorize into Grades A through J, where A is the highest quality); all of these wells were drilled in Grade A acreage, the sweet spot of the play, so differences in production should be a result of engineering practices, not geology.

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Figure 1 Creaming Curve for Peak Month Oil (bbl)

If a creaming curve has a 45 degree trajectory, each new well produces the same peak month production as the previous well did. A steeper trajectory indicates new wells are adding increasingly greater peak month production. In Figure 1 above, Operator C (dark blue line) started out performing worse than the other operators and then improved techniques to outperform peak month production compared to Operators A and B.

Inflection points that change the trajectory are of interest, as they indicate changes in operational techniques. If we review completion reports for wells 50 and 120 of Operator C, which appear before and after climbing the operational “learning curve,” we will expect to see different completion techniques.

Which operator is performing the best?

Because peak rates are highly correlated with total production, Figure 1 is a compelling graphic to conclude that Operator C is performing the best of the three operators. However, peak month production does not tell the complete story. Figure 2 is a creaming curve for the same three operators in Grade A acreage showing well performance after 12 months.

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Figure 2 Creaming Curve for 12 Month Cum Oil (bbl)

Now we see that Operator B is outperforming its peers at the twelve month mark in terms of total production, and when we did this again for 24 month production, the comparison showed Operator C falling even further behind its peers.

The creaming curves illustrate a trade-off between maximizing high initial production versus maximizing total production over the life of the well. Obviously higher peak rates shorten the payback period of an investment, but do they also result in higher net present values? We can model decline curves and compute cash flows to determine the relative financial results.

For our models, we select wells drilled in 2013 by each operator in high grade acreage to have sufficient production to forecast declines. Using DI Analytics, we estimate decline curves, forecast production for twenty years, and build cash flow models for a single well from each operator. We hold economic inputs constant for each operator, assuming $55 oil price, $3 gas, $7 million well cost, 10% discount rate, and the same tax rates and royalty interests.

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Figure 3 NPV versus IRR

The net present value (NPV) and internal rate of return (IRR) for a single 2013 well for each operator are shown in Figure 3. Operator B achieves the greatest NPV of approximately $2.3 million, and Operators A and C have NPVs of $1.23 million and $990K, respectively. Operator B, A and C earn 39%, 25% and 19% IRRs, respectively. However, Figure 4 depicts how the sensitivity to oil price differs depending on the production profile: high peak rates with more rapid declines make sense when oil prices are higher. As oil prices rise, Operator C increases its IRR at a faster rate than the other two operators do. Given that oil averaged approximately $100 per barrel in 2013, Operator C’s strategy appears to be a good choice.

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Figure 4 After Tax IRR versus Oil Price


Creaming curves are a quick way to understand how an operator is producing its wells versus another operator. However, the “right” strategy to pursue may depend on the price environment. While wells with very high peak rates pay back investment quicker, is this the optimal approach in a low oil price environment? Many of the wells included in the creaming curves above were drilled in a $100 oil environment, and perhaps we will see a change in production profiles as more wells are drilled and completed in a $50 oil environment.

Your Turn

What do you think? Leave a comment below.

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Tanya Andrien

Tanya is the Product Owner for Finance and Analytics at Drillinginfo. She engages with financial services clients to utilize Drillinginfo data and software in their investment decisions and research activities, and she manages product development for the Analytics products. Prior to Drillinginfo, Tanya was a Lecturer at the McCombs School of Business at the University of Texas at Austin, and the Associate Director of the McCombs Energy Center, where she was responsible for alumni outreach, student programming, and business operations. Prior to working for McCombs, Tanya was a Director with Duff and Phelps, a financial advisory firm. She has over ten years of consulting experience across a number of industries, including financial institutions, consumer products, medical devices, oil and gas and software.