Evaluation of a Statistical Method for Assessing Infill Production Potential in Mature, Low-Permeability Gas Reservoirs

Author:

Guan L.1,McVay D. A.2,Jensen J. L.2,Voneiff G. W.3

Affiliation:

1. ChevronTexaco, 4800 Fournace Place, Room E537, Bellaire, TX 77401

2. Department of Petroleum Engineering, Texas A&M University, 3116 TAMU, College Station, TX 77843-3116

3. MGV Energy, #2000 125-9th Ave SE Calgary, AB Canada T2G 0P8

Abstract

Background. Identifying the locations and amounts of unproduced gas in mature reservoirs is often a challenging problem, due to several factors. Complete integrated reservoir studies to determine drilling locations and potential of new wells are often too time-consuming and costly for many fields. In this work, we evaluate the accuracy of a statistical moving-window method (MWM) that has been used in low-permeability (“tight”) gas formations to assess infill and recompletion potential. The primary advantages of the technique are its speed and its limited need for data, using only well location and production data. Method of Approach. To test the method, we created a number of hypothetical reservoirs and calculated infill well potential using a reservoir simulator. We used the MWM to analyze these data sets, then compared results to those from the reservoir simulations. Results. The results validate empirical observations made using MWM during field evaluations. Depending on the level of reservoir heterogeneity, the MWM infill predictions for individual wells can be off by more than ±50%. The MWM more accurately predicts the production potential from a group of infill candidates, the MWM, however, more often to within 10%. We describe a procedure to estimate the number of wells needed to predict production potential to within a stipulated accuracy. The ability of MWM to accurately predict production performance for groups of wells shows that it can be a useful tool for scoping studies or identifying areas for more detailed evaluation.

Publisher

ASME International

Subject

Geochemistry and Petrology,Mechanical Engineering,Energy Engineering and Power Technology,Fuel Technology,Renewable Energy, Sustainability and the Environment

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Machine learning to improve natural gas reservoir simulations;Sustainable Natural Gas Reservoir and Production Engineering;2022

2. Incremental and acceleration production estimation and their effect on optimization of well infill locations in tight gas reservoirs;Journal of Petroleum Exploration and Production Technology;2021-06

3. Semi-Analytical Proxy for Vapex Process Modeling in Heterogeneous Reservoirs;Journal of Energy Resources Technology;2014-05-15

4. Infill-Drilling Potential in Tight Gas Reservoirs;Journal of Energy Resources Technology;2012-11-06

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