Practical Considerations for Production Forecasting in Unconventional Reservoir Systems — Processing of Large Group of Wells Using Production Diagnostics and Model-Based Analysis

Abstract

Abstract Production forecasting in unconventional reservoir systems is not an easy task and should not be soley accomplished using empirical decline curve analysis. Advanced analytical and numerical models have facilitated the analysis of unconventional systems; however, it is almost always impractical to analyze and forecast hundreds of wells using these techniques. The focus of this work is to demonstrate a practical and timely methodology using model based production analysis as the foundation for the analysis and forecasting of each producing well in a particular area/field in an unconventional reservoir. The methodology is founded on a thorough diagnostic assessment of all available data, thorough production analysis of key wells, and extension to the remaining wells using key information from the diagnostic analysis. The methodology includes three main components: production diagnostics, model-based analysis for representative wells, and production forecasting. Production diagnostics is performed on a single well basis to identify flow regimes and performance metrics of each well, and performed on a multi-well basis to compare performance and identify characteristic behavior which can lead to well groupings and the selection of representative wells for analysis. Representative well(s) from each group are analyzed using model-based analysis incorporating non-linear behaviors associated with the production performance. A systematic analysis procedure is followed to account for the uncertainty on well/reservoir parameters affecting production behavior by utilizing an experimental design methodology. Multiple history matches are obtained accounting for uncertainties such as drainage area, permeability, etc. Following model-based analysis, production forecasts are developed for the corresponding history matches. Based on either a statistical distribution of EUR values or a deterministic approach, characteristic low/mid/high time-rate profiles can be derived. These profiles are extended to other wells via scaling factors to determine EUR values. Coming full circle, the scaling factors are then compared to well/reservoir data using maps and cross plots of performance metrics obtained from diagnostics to aid in the performance based analysis of the group.