Characterization of Tight Reservoirs
Authors: Lee W.J. , Hopkins C.W. 
Published: December 2007

Distinguished Author Series

Summary
This paper gives methods to characterize tight gas reservoirs in sufficient detail to allow an engineer to make accurate long-range production forecasts. These forecasts are the basis for sound engineering and business decisions. Because of the complexity and variability of tight gas reservoirs, we can present only general procedures for developing reservoir descriptions. Accordingly, we illustrate a reservoir characterization method with three examples of successful tight gas reservoir studies. The procedures in these examples can be modified as needed for other specific formations or areas.

Introduction
Production rates from many tight reservoirs are marginal, but these reservoirs account for a large percentage of the long-term gas supply. Because of the marginal economics, efficiency is the key to drilling and producing these tight reservoirs. To optimize production, we must have a good understanding of the reservoir, but often the economics cannot support collecting the data necessary to describe the reservoir properly. A reservoir engineering study for tight reservoirs requires us to balance data collection costs with the level of detail necessary to describe the reservoir accurately. One must determine what level of reservoir characterization is needed to optimize production from tight reservoirs efficiently. Unfortunately, because of the diverse nature of tight reservoirs, there is no single answer. The question must be answered on a case-by-case basis.
Reservoir studies of tight reservoirs are performed to meet many different objectives. Because tight wells require hydraulic fracturing, fracture treatment optimization studies are quite common. A reservoir study is sometimes performed in conjunction with a detailed geologic study to help identify key well characteristics or field trends to be used as exploration tools and to predict reserves. A reservoir study can identify infill-well potential and the potential for increased productivity and reserves as the result of the installation of compression or liquid lift equipment. Finally, reservoir studies can resolve conflicting data or determine why some wells are notproducing as expected.
Unfortunately, analysis of tight reservoirs is one of the most difficult problems facing a reservoir engineer. Many tight formations are extremely complex, producing from multiple layers with permeabilities that often are enhanced by natural fracturing. Unfortunately, low productivity and marginal economics often prevent expenditures of money and time to collect the data needed for a detailed reservoir study. Because the permeability of these formations is low, many standard formation evaluation techniques do not provide adequate results. Standard log-based correlations for permeability or other productivity indicators often fail in tight reservoirs, so correlations must be developed on an area-specific basis. Many tight shale reservoirs have productive gross intervals exceeding 300 ft, making it difficult to determine where the gas is produced, thus complicating completion decisions. Even in tight gas sands made up of interbedded sands and shales, layering can have a pronounced effect on well production. Natural fractures often occur in these tight formations, making wells that appear similar on logs perform quite differently.
When we do not describe the reservoir in sufficient detail, the production forecasts we generate are frequently wrong. Unless we can predict postfracture well performance accurately, we cannot optimize the fracturing process. Sound business decisions regarding compressor installation, infill drilling, or remediation treatments are not possible. Unfortunately, for layered reservoirs, oversimplified reservoir descriptions frequently result in an overestimated well productivity.
Fig. 1 shows predicted 20-year performance for a Devonian shale well for three different reservoir descriptions: "lumped" one-layer, 3-layer, and 10-layer reservoirs. All three predictions are based on the same gas in place and the same total permeability-thickness product, kh. Note that the lumped one-layer model over predicts the gas recovery by a factor of two. The four-layer model prediction is closer to actual but is still high by about 17%.Any business decisions based on the single-layer prediction would be seriously in error.

Background
Development of tight gas reservoirs has been increasing substantially over the last decade. Because of this trend, the Gas Research Inst. (GRI) and the U.S. DOE have been funding detailed research in tight gas sands and shales throughout the U.S. This research has led to significant advances in hydraulic fracturing and a better understanding of the complexity of the tight reservoirs.
The importance of describing a layered reservoir has been discussed in the literature for several decades. Much of this discussion has centered on pressure-transient analysis of layered reservoirs (with and without crossflow)and descriptions of the nonideal buildup test pressure responses often observed in the field. Lefkovits et al. presented analytical solutions for flow in layered reservoirs and identified several characteristic features of reservoirs with discrete, noncommunicating layers. Other investigators presented numerous solutions describing pressure and flow rate that include the effects of interlayer crossflow, stimulation, or unsteady- (transient) or pseudo-steady-state (boundary-dominated) flow. Comprehensive analytical reservoir models have been developed specifically to model the pressure or flowrate response from layered reservoirs.
Although much theory has been presented in the literature, case studies documenting layered reservoir analyses are not as common. Much has been presented on fracture treatment optimization in tight reservoirs, but generally the impact of layering is not discussed. The majority of layered reservoir analyses involve history matching data by use of reservoir simulators, although the recent availability of comprehensive analytical models should make layered analyses easier and more cost-effective.
Even with sophisticated computer programs for analysis of layered reservoirs, this analysis is still not straightforward. Cost-effective data collection methods for describing layered reservoirs are not developed easily because of the diverse nature of tight reservoirs and widely varying production volumes.
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