Production data analysis is emerging as one of the oil and gas industry’s most powerful tools for well performance evaluation. The use of a theoretically rigorous production data analysis tool is allowing operators to find significant additional financial value in their wells with data they already have in hand. Accurate analysis of that production data can be performed very quickly, at several levels of detail, and at less cost than traditional solutions of well testing and simulation. The method makes possible the observation of time-dependent skin damage, changing transmissibility, interference, liquid loading, and drainage geometry—as well as the quantitative evaluation of effective fracture length, drainage volume, permeability thickness, skin, and recoverable reserves. Production data analysis is readily accomplished with data, already acquired in the normal course of business, although most electronic data collection systems already record with "near well-test" quality resolution. Even very low-resolution monthly data often provides reliable interpretations. By contrast, traditional shut-in well testing incurs costs that include lost production, well services fees, and (very often) formation damage.

The primary focus of this presentation is to show a variety of the diagnostic signatures that arise in performing production data analysis. The presentation will show actual well history examples including both oil and gas wells, high and low permeability, onshore and offshore, from several different countries, although the origin, operator, etc. will not be discussed. Examples with different signatures arising from water production, such as an active aquifer, a failed completion, and free water production from a producing zone will be shown. One of the examples shows the impact of a shut-in on the productivity of a gas well in multiphase flow. The impact of reservoir geometry on well performance is observable in one example, and another shows the effect of changing transmissibility versus changing skin. The theoretical basis for the method will be presented with simulation generated examples. In most cases, the visual assessment will be concluded with a quantitative evaluation.