Evaluation of a Statistical Infill Candidate Selection Technique

Guan, L. (Texas A&M University) | McVay, D.A. (Texas A&M University) | Jensen, J.L. (Texas A&M University) | Voneiff, G.W. (MGV Energy Inc.)

OnePetro 

Abstract 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. In this work we evaluate the accuracy of a statistical moving window technique that has been used in tight gas formations to assess infill and recompletion potential. The primary advantages of the technique are its speed and its reliance upon well location and production data only. The statistical method was used to analyze simulated low-permeability, 100-well production data sets; moving window infill well predictions were then compared to those from reservoir simulation. Results indicate that moving window infill predictions for individual wells can be off by more than 50%; however, the technique accurately predicts the combined infill production estimate from a group of infill candidates, often to within 10%. The accuracy of predicted infill performance decreases as heterogeneity increases and increases as the number of wells in the project increases. The cases evaluated in this study included real-world well spacings and production rates and a significant amount of depletion at the infill locations. Due to its speed, accuracy and reliance upon readily available data, the moving window technique can be a useful screening tool for large infill development projects. Introduction The most accurate way to determine infill-drilling potential in a gas basin is to conduct a complete reservoir evaluation involving geological, geophysical, and reservoir analyses and interpretations. This includes developing a geological model of the study area, estimating distributions of static reservoir properties such as porosity and permeability, constructing and calibrating a reservoir simulation model of the area, and then using the reservoir model to predict future production and reserves at potential infill well locations. While it may be accurate, this approach can be prohibitively time-consuming and expensive. For some large, low-permeability gas basins with large data sets (sometimes over 1,000 wells) and complex geology, the cost and time requirements of a conventional reservoir evaluation study are not acceptable. McCain et al. used a statistical, moving-window method to determine infill potential in a complex, low-permeability gas reservoir. Later, Voneiff and Cipolla applied a similar method to analyze well location and production data for rapid assessment of infill and recompletion potential in the Ozona field. While the feasibility and value of this approach has been demonstrated, a systematic assessment of the validity and accuracy of the technique has not been presented in the literature. The objective of this work was to quantify the accuracy of the moving window technology for selecting infill candidate wells in low permeability gas reservoirs. We did this by calculating infill well performance with the moving window method from simulated data, from which we could readily determine the "best" infill candidates for comparison.

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