Experience RS

A Solution For Predicting Well Performance

Predicting well performance is challenging as several different factors can affect the outcome. How much proppant should be used before over-capitalization happens? How close can wells be spaced before production cannibalization occurs? Is this rock worth developing under the current commodity price environment? Should I co-complete or stagger development to minimize offsetting well communication?

Multivariable analysis (MVA) helps answer these complex questions. The workflows leverage machine learning algorithms that allow for interpretation far beyond graphs and tables. With the appropriate software and expansive clean datasets, machine learning algorithms move an analysis from daunting to feasible, producing actionable insight. The general steps to complete an MVA are shown in Figure 1.

Collecting and cleaning data usually consumes the most time and labor when running an MVA. It takes an enormous investment to collect data for all the variables that influence well performance and to organize this information into a model. The data within Enverus Core and Enverus Next, compiled from thousands of different sources and cleaned to meet a standard of excellence, drastically cuts the draw on resources to initiate an MVA. These platforms allow users to include production, completion, geological, spacing and timing variables within the MVA model, providing the most sophisticated way to answer the industry’s toughest questions.

FIGURE 1 | Typical MVA Workflow

 

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