With the increasing demand for natural gas and higher prices, more and more gas basins within North America are undergoing infill drilling. It is not uncommon for a company to have hundreds or thousands of infill candidates to choose from. But quantifying the drilling or recompletion potential in large producing gas basins is often a challenging problem, due to large variability in rock quality, well spacing, and well completion practices, and the large number of wells involved. Complete integrated reservoir studies to determine infill potential are often too time consuming and costly for many large tight gas basins.
In previous work, we evaluated the accuracy of a statistical moving-window technique that had been used in mature tight gas formations to assess infill and recompletion potential. We compared the moving window infill well predictions to those from reservoir simulation. Results indicated the technique accurately predicts the combined infill production estimate from a group of infill candidates, often to within 10%.
Here, we report on a reservoir parameter sensitivity study and show that the accuracy of predicted infill performance decreases as heterogeneity increases and increases as the number of wells in the project increases. Also, the search area and well spacing have a large effect on the accuracy of estimates from the statistical method. Because of its speed, accuracy, and reliance upon readily available data, however, the statistical technique can be a useful screening tool for large infill development projects.
Quantifying the drilling or recompletion potential in producing gas basins is often a challenging problem due to large variability in rock quality, well spacing, and well completion practices, and the large number of wells involved. Complete integrated reservoir studies to determine infill potential are often too time consuming and costly for many producing gas basins.
Instead of conducting detailed studies, some investigators have used empirical or statistical analyses to model variable well performance(1–7). McCain et al.(4) used a statistical, moving window method to determine infill potential in a complex, low-permeability gas reservoir. Later, Voneiff and Cipolla(5) applied a similar method to analyze well location and production data in the Ozona field. The benefits of this approach have also been demonstrated in other applications(6–7).
The technique employed in this work, herein referred to as Mosaic or moving window technique, is an extension of the method described by Voneiff and Cipolla(5). It is similar in that it consists of a multitude of local analyses, each in an areal window centred around an existing well (Figure 1). In this work, however, a more rigorous, model-based analysis is employed in each moving window. The model is based on a combination of the material balance equation and the pseudo-steady state flow equation, simplified by assuming that many properties are constant within an individual window.
In a previous work(8), the moving window technique used here was found to accurately predict infill well performance for a group of infill candidates, often to within 10%, but the predicted infill potential for individual wells in the group could be off by more than 50%.