Practical Methods to High-Grade Infill Opportunities Applied to the Mesaverde, Morrow, and Cotton Valley Formations

Abstract

Several gas basins within North America are under-going a period of infill drilling in order to supply the increasing demand for natural gas. It is not uncommon for an operator to have hundreds, if not thousands of infill candidates from which to choose. Is there a practical way to high-grade infill locations within a particular basin or field? This paper presents several practical techniques to high-grade infill drilling opportunities when faced with large data sets and little time. We will present results for the Mesaverde in the San Juan Basin, the Morrow in the Permian Basin, and the Cotton Valley in east Texas. We calculated the infill potential for each of the three example formations. Based on our experience, we have found that comparisons between actual and predicted individual infill well performance can vary significantly. Therefore, ranking infill candidates on predicted individual well performance may not necessarily yield the best overall results. Poor wells, predicted to be good wells, may still be drilled and good wells, predicted to be poor wells, may not be drilled at all. Alternatively, when considering an infill drilling program as a whole, we have found that the predicted performance for the group can be quite accurate. In this paper, we will present a method to divide a basin or field into smaller areas and predict the distributions of infill performance as a group for the smaller areas, rather than individual wells. We will then present two methods to rank the small areas to achieve the most economic results. The first method ranks the areas based on the average of the distribution. The second method uses decision tree analysis to calculate a typical infill well in an area using the P10, P50, and P90 values from the distribution. We considered actual recent drilling programs in each of the three formations, used the above techniques to eliminate predicted uneconomic wells, and compared the actual to predicted drilling program results. We found that the finding and development costs were lower, return on investment was higher, and the success ratio was higher when the techniques presented in this paper were used to high-grade drilling opportunities. The results of a study applying this analysis technique can be used when budgeting and planning near- and long-term drilling programs. Introduction Backround. The well spacing for the Mesaverde and Cotton Valley formations is currently being reduced from 160 acres to 80 acres per well and the Morrow formation is being reduced from 320 acres to 160 acres per well. We have performed infill potential studies for each of these formations using a technique called Moving Domain™. Moving Domain™ has been successfully applied to the Ozona (Canyon) Gas Sands1, East Texas Cotton Valley2, and the Austin Chalk3 to quanitify infill potential. Moving Domain™ combines statistical analysis with historical production performance to provide a realistic outlook of infill potential. The technique replicates the thought process that a reservoir engineer might go through when faced with the task of spotting an infill location1. Moving Domain™ is an automated process making a 1,000-well study a manageable task.4

Effective EOR Decision Strategies With Limited Data: Field Cases Demonstration

“…Our approach was to integrate the available data and use analytical tools and some small conceptual models. This is not unlike moving-mosaic techniques used for infill drilling (Voneiff and Cipolla 1996;Guan et al 2002;Hudson et al 2000;Hudson et al 2001;Wozinak et al 1997) or the quality-map approach (da Cruz et al 2004). A frequent approach to analytical simulation consists of modeling a well pattern (generally five-spot patterns), with the assumption that the obtained results represent the average performance of a particular sector of the reservoir.…”

Effective EOR Decision Strategies with Limited Data: Field Cases Demonstration

“…Our approach is to integrate the available data and use analytical tools and some small conceptual models. This is not unlike moving mosaic techniques used for infill drilling (Voneiff and Cipolla, 1996;Guan et al, 2002;Hudson, Jochen and Jochen, 2000;Hudson, Jochen and Spivey, 2001;Wozinak, Wing and Schrider, 1997) or the quality map approach (Da Cruz, Horne, and Deutsch, 2004). A frequent approach to analytical simulation consists of modeling a well pattern (generally 5-spot patterns), with the assumption that the obtained results represent the average performance of a particular sector of the reservoir.…”

Evaluation of a Statistical Infill Candidate Selection Technique

“…Despite these assumptions and limitations, this moving domain technique has been applied to the Ozona (Canyon) gas sands, 5 Milk River formation in Canada, 8 Cotton Valley in east Texas, 4,9 Mesaverde formation in the San Juan Basin, 9 They estimated ultimate recoveries (EUR) on a barrels/acre basis by calculating every well's EUR and drill density. Then they analyzed the depletion and infill potential.…”

“…[1][2][3][4][5][8][9][10] In particular, the moving domain technology, described by McCain et al 4 and enhanced by Voneiff and Cipolla 5 has been applied to several tight gas reservoirs to quantify infill potentials. 4,5,8,9 Although the moving domain technology has been applied to several tight gas reservoirs, we could not find any quantitative assessment of the validity and accuracy of the technique in the literature.…”