• Overview

    In the current depressed commodity price environment, doing more with less is key. Every dollar needs to be spent in the most efficient manner, and that includes streamlining the M&A process and quickly understanding which operators make the best acquisition targets. However, locating the best assets is often a time-consuming, labor-intensive process.

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    There are several strategies an E&P company could use to locate promising acquisitions. When an operator recently decided to expand its operations in the Eagle Ford, it decided to use a strategy of targeting underperformers in high-grade acreage, with the assumption that these assets would have the most room for improvement. The firm also wanted to identify optimal completion parameters that could boost future production of the assets it decided to acquire.

    An analyst in the land department was assigned to the project. Using Drillinginfo and the Graded Acreage Acquisition Target Identification workflow, the analyst was able to quickly distinguish ideal acquisition targets and understand how to apply best practices to boost future production.

  • Identify Underperformers in High-Quality Acreage

    The operator’s strategy was to target underperformers with high potential for improvement. In order to do that, the analyst needed to rank relative acreage quality, compute typical production for each acreage grade, and then identify how each operator in the Eagle Ford performed relative to median production in its acreage quality.

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    With Drillinginfo

    Using the DI Analytics Graded Acreage tool, the analyst was able to effortlessly categorize acreage by reservoir quality without having to provide a single input. This provided him with a refined benchmarking process for identifying both outstanding and marginal performers. Next, the analyst could easily visualize relative operator performance with the click of a button using the Operator Differential function in Graded Acreage, allowing him to hone in on the targets performing below average in high-quality acreage.

    Without Drillinginfo

    Without Drillinginfo, the analyst would have to manually compile and analyze large amounts of data in order to create a subsurface map that estimated acreage quality. The analyst would then need to collect operator performance data and plot the data on the map to identify the operators with low performance in high-grade acreage. This method is very tedious and time-consuming, and therefore not scalable.

  • Compare Operators by Six-Month Cumulative Oil

    After identifying several potential targets, the analyst wanted to understand each operator’s performance in greater detail. Creaming curves that graph six-month cumulative production for each operator let the analyst visualize specific production in relation to other operators. Using creaming curves in DI Analytics, the analyst was able to understand operator performance in greater detail.

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    With Drillinginfo

    Using the pre-loaded production data in DI Analytics, the analyst built creaming curves to visualize six-month cumulative production for horizontal wells in the order in which the wells were drilled, allowing him to see at a glance the specific production for each operator of interest. In this case, Operators 1 and 2 had the lowest six-month cumulative oil production. Depending on each operator’s financial position, one of these companies might make a promising target.

    Without Drillinginfo

    Without Drillinginfo, the analyst would need to export production data into a separate software program to build creaming curves. The task of exporting data from one platform to another would lead to wasted time and potential opportunity costs.

  • Benchmark Best Practices

    Now that the analyst had narrowed down his list of potential targets, he needed to understand why these operators were performing below average and identify strategies to improve future production should his company acquire these assets. By comparing operators’ completion techniques in DI Analytics, the company could quickly see what factors might be impacting current production and could potentially be used to boost future production for the acquisition targets.

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    With Drillinginfo

    By comparing operators’ completion techniques in DI Analytics, the analyst could quickly see what factors might be impacting current production and could potentially be used to boost future production for the acquisition targets. Based on his experience with other wells in the Eagle Ford, he had a hunch that differences in perforation intervals might be accounting for differences in production. Using the Best Practices tool in Graded Acreage, the analyst compared perforation intervals for the operators of interest alongside perforation intervals for other companies operating nearby. In this case, it did not look like perforation intervals were the reason for differences in production. After rejecting his first hypothesis, the analyst was able to analyze different production metrics to see if he could identify other completion techniques affecting production.

    Without Drillinginfo

    Without Drillinginfo, the analyst would have to sift through multiple data sources to manually compile well-level completion techniques and compare the respective production data in order to identify possible correlations. This is a tedious and time-consuming process.

  • Categorize Leaseholds and Compute Well Inventory

    While the analyst had identified two promising targets, he had to be sure that there would be space to drill additional wells should his company acquire either of the targets. To find out if either of his operators of interest had available space to drill additional wells in high-grade acreage, the analyst needed to know the number of wells remaining on the leaseholds and tie them to acreage quality.

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    With Drillinginfo

    Before moving forward with further due diligence, the analyst needed to know if either of the target operators had available space to drill additional wells in high-grade acreage. Using DI Analytics, he first graphed median well spacing for each operator and then compared it with total lease acreage in order to compute the remaining well inventory for each prospect. Using the Graded Acreage map layer, he determined the percentage of remaining acres in each grade of acreage. This analysis helped him narrow in on an ideal target with ample remaining high-grade acreage.

    Without Drillinginfo

    Without Drillinginfo, the analyst would have the costly and time-consuming task of cross-referencing multiple datasets, including operator leaseholds, maps of current well locations, and subsurface maps that approximate acreage quality. The manual nature of this process makes it less accurate and not scalable for large projects.

  • Run Well Production Economics Workflow

    Before making a final recommendation, the analyst wanted to provide his boss with an economic analysis of the proposed target. The operator would need to understand the potential internal rate of return and payout period of any acquisition it decided to make.

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    With Drillinginfo

    Having identified a potential target with available high-quality acreage, the analyst needed to understand the economics of the acquisition before issuing a recommendation to his company. Using the pre-loaded analytics-grade data included in DI Analytics and available through the Well Production Economics workflow, the analyst built a cash flow model based on oil and gas price and expected well production. With a few clicks, he generated a before- and after-tax IRR and payout period for each acquisition target. Being able to run the economic analysis within the same tool he used for his acquisition search meant he could understand the expected ROI within minutes of finding a potential target.

    Without Drillinginfo

    Without the simple Well Production Economics workflow, the analyst would need to export his existing production and cost data into other tools such as Microsoft Excel to run an economic analysis and predict IRR, payout period, and expected well cost. Exporting data back and forth between multiple tools would take valuable time that could lead to lost opportunities.

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    Summary

    The analyst and company executives were pleased with the results. Using the Acquisition Target Identification workflow in DI Analytics, the analyst was able to visualize subsurface quality and the companies operating in each acreage grade with the click of a button. After selecting several potential targets, the analyst was able to perform further due diligence around each asset, such as identifying six-month cumulative oil production and computing the remaining high-grade acreage on each leasehold. He also identified completion best practices the company could utilize to boost production should it move forward with his recommendation.

    The analyst turned in his recommendation to his boss along with an economic analysis of expected IRR and payout period for the recommended acquisition. His boss was impressed with the speed and accuracy with which he returned results and was able to beat competitors to an asset that would be profitable even in a low-price market.

    Contact a Drillinginfo specialist today to learn how DI Analytics can revolutionize your results, and request a personalized demo to experience the impact for yourself.