Statistical Correlations in Tight Gas Sands
Stephen A. Holditch
Texas A&M University, College Station, Texas
Tight gas reservoirs have been exploited in the United States since the 1960’s. However, it was in the 1970’s when higher prices for natural gas and tax incentives from the government led to the rapid increase in drilling and completion activity in tight gas reservoirs. In the late 1970’s, Masters and Grey published the concept of the resource triangle to explain the distribution of natural gas in the Deep Basin in Alberta (Fig. 1). However, the concept of the resource triangle applies to all oil and gas basins and the consequences of the distribution should be understood by anyone trying to develop tight gas sands (TGS) or any other unconventional reservoirs.
To maximize profit from a TGS, one must have data, such as permeability and in-situ stress on every layer of rock above, within and below the pay zone. Measuring such data from cores and well tests on every well is cost prohibitive. However, if a company runs the proper logging suite, the cuts and analyzes enough core and well test to calibrate the logs, one can properly characterize the layers of rock. With proper characterization, the completion engineer can design the optimum completion. Fig. 2 illustrates what data are needed for each layer of rock.
The most important parameters to evaluate are formation permeability and in-situ stress by layer. There have been many different equations published for correlating core permeability to open hole logging values. Even though most of the equations were developed using data from high permeability formations, the equations can be used for low permeability formations by determining the correct values for the correlation constants and coefficients. One method developed by Yao using core and log data from the Travis Peak and Wilcox formation work well and can be used with a minimum logging suite of a gamma ray, a porosity tool and a resistivity tool that measures multiple depths of resistivity. It has also been found that one can use the effect of mud filtrate invasion upon standard logging tools to obtain reasonable estimates of formation permeability.
After estimating values of formation permeability form logs, well tests or production data, it is often useful to look at the distribution of permeability vs. depth and vs. aerial extent to better understand the tight gas reservoir. Data are available from public records for many tight gas sands. These data sets are part of the FERC records when companies filed for tight gas sand designations for various reasons, such as tax credits or severance tax relief. The data from four formations in Texas have been obtained and evaluated. These formations are the Travis Peak and Cotton Valley in East Texas, the Wilcox Lobo in South Texas and the Cleveland in North West Texas. These data sets clearly show the permeability data are distributed log-normally. The median values of permeability for all four data sets range from 0.028 md to 0.085 md. The arithmetic averages for the data sets range from 0.179 md to 7.378 md. If one evaluated these data, it is clean that the median value best represents the measure of central tendency.
In addition to permeability, it is important to develop correlations for mechanical properties, such as in-situ stress and Young’s modulus. Numerous papers have been written on how to develop the data sets needed to derive the necessary correlations. We need the data to design the optimum completion and fracture treatment. In this presentation, I will show a Travis Peak data set of in-situ stress that can be correlated to the gamma ray log and the formation permeability. In a tectonically relaxed area, the in-situ stress will be a function of the lithology and the reservoir pressure. In a sand-shale sequence like the Travis Peak, the lithology can be predicted with the gamma ray log.
In summary, tight gas sand wells are economically marginal to drill and produce unless the wells are drilled, evaluated, completed, stimulated and operated in an optimal manner. To make correct decisions, one needs correct data on all the rock layers. It is important to run the correct logs, cut cores and run well tests to build correlations for interpreting the logging data. The most important parameters to evaluate on a layer by layer basis are permeability, in-situ stress, porosity and water saturation.
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Yao, C. Y. and Holditch, S. A.: "Reservoir Permeability Estimation From Time-Lapse Log Data," SPE 25513 presented at the 1993 Production Operations Symposium, Oklahoma, OK, March 21-23.