There are several aspects of unconventional reservoirs that make them hard to understand. The physics of flow in such ultralow-permeability reservoirs is essentially unknown. Uncertainties and well counts are large, information along horizontal boreholes is minimal, and production variability is huge. For these reasons, we used production and completion data to answer several pertinent questions about unconventional reservoirs.

High well counts increase the volume of data, which makes it overwhelming to analyze on a typical spreadsheet. Integrating efficient data management with seamlessly integrated analysis tools makes it much easier to understand the data and interpret the results. Data mining and data analytics are used to recognize underlying trends, determine sweet spots, and identify optimum completion parameters. Smart well clustering is also employed to create sets of wells that are grouped together in a logical fashion to generate type curves.

Most wells, especially in the newer plays, exhibit transient flow regimes, so fit-for-purpose decline-curve analysis is applied to get an accurate estimate of the production forecast. A guided process first quality checks the data, identifies the flow regime, and then predicts the performance into the future. Some wells that are not performing optimally must be restimulated, and, typically, there is only a 15% chance that the right candidates are selected. Production and completion data are analyzed using a workflow to screen and significantly lower the number of candidates under consideration.

A large percentage of studies focus on the reservoir only; however, production problems are not always about the reservoir. A comprehensive review of a large well network in the Eagle Ford created from public data such as coordinates, well tests, and pipeline dimensions, shows that slugging, liquid loading, network bottlenecks, etc. affect the system performance as a whole and can significantly lower production if not corrected. Results from an analysis of over 15,000 wells from the Barnett, Bakken, and Eagle Ford led to recommendations for effective use of public data in production strategies.

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