Total Play Analysis

The process begins with the collection and entering of a basic dataset comprising pressure/rate data into Object Reservoir’s Knowledge Architecture (ORKA) and OR’s analytic modeling software, ORKA: Limits™. By using this basic dataset, the time required collecting and inputting the data is reduced; but when combined with OR’s shale gas workflow, it provides fully validated physics-based analytics–everything required for predictive modeling work. This dataset is organized automatically within the ORKA system into a fully searchable well database, including the merging and time synchronization of production and pressure data.

Once the data has been acquired, loaded, and prepared, a series of diagnostic plots are produced in Limits. Upon completion of the diagnostic plots, the wells are then categorized into performance groups, hypotheses are developed for these classifications and outlying well performance, and early estimates of similarity parameters are cataloged and mapped. The results of this exercise are automatically fed back into the ORKA framework and become searchable parameters within the well database. Values for similarity groups obtained from the diagnostic plots (in conjunction with identified feasible ranges of permeability, fracture spacing and producing fracture surface area) form the basis of the next step: the construction and calibration of the Limits Predictive Model.

The Limits Predictive Models generate forecasts for the wells within the designated study area. These forecasts are generated using stochastic, and if desired, Monte Carlo forecasts, creating a range of probable well performance inside which actual well performance will fall. The performance of the well is still modeled using actual rock properties and is not dependent on type curve fitting or arbitrary decline curves even into the later flow regimes (external linear transient and external depletion). The math used in these forecasts is fully validated and based entirely on actual rock physics and properties.

Any outlying wells identified while forming the similarity groups may be candidates for further investigation in the Detailed Well Analysis.