A common purpose of type wells is to predict the production profiles of multi-fracture horizontal wells (MFHW) in a selected geologically similar area (GSA) in a resource play. This paper presents a unique workflow that leads to type well production forecasts as analytically scaled rate-time profiles for an identified range of reservoir and completion properties.

The workflow uses production data from all MFHW's in a GSA to build more representative type wells. First, we sort and bin the wells, accounting for observed flow regimes (transient and boundary-dominated flow). We then extract physical properties from the data using curve-matching techniques. Using these properties, we can forecast the production profile of any well already in BDF. For wells still in the transient flow regime and for undrilled wells, we probabilistically forecast performance using distributions of observed properties (such as permeability and fracture length) from existing wells in BDF and Monte Carlo simulation.

We demonstrate that the analytical scaling factors resulting from type-curve matching can be used to construct a probability distribution of production forecasts from type wells. We can scale the type well production profiles to a given set of reservoir and completion properties, including observed average sets of properties from well analyzed or based on altered completion designs. We illustrate the workflow with a data set of 126 MFHW gas wells in the Denton County, Texas, Barnett shale. Using information extracted from this data set, we forecasted production for undrilled wells and validated the forecasts using a sequential accumulation plot with information from10 analog wells.

Type wells are important tools for decision makers and engineers to determine economic feasibility of proposed development projects and estimate reserves. As opposed to purely empirical methods used for type well construction, our analytically-based workflow provides an integrated GSA characterization, reliable production forecasts and better tools for decision making with reduced uncertainty.

You can access this article if you purchase or spend a download.