This thesis presents the development of a semi-analytical technique that can be used to estimate the gas-in-place for volumetric gas reservoirs. This new methodology utilizes plotting functions, plots, extrapolations, etc. - where all analyses are based on the following governing identity. The 'governing identity' is derived and validated by others for pi less than 6000 psia. We have reproduced the derivation of this result and we provide validation using numberical simulation for cases where pi greater than 6000 psia. The relevance of this work is straightforward using a simple governing relation, we provide a series of plotting functions which can be used to extrapolate or interpret an estimate of gas-in-place using only production data (qg and Gp). The proposed methodology does not require a prior knowledge of formation and or fluid compressibility data, nor does it require average reservoir pressure. In fact, no formation or fluid properties are directly required for this analysis and interpretation approach. The new methodology is validated demonstrated using results from numerical simulation (i.e., cases where we know the exact answer), as well as for a number of field cases. Perhaps the most valuable component of this work is our development of a "spreadsheet" approach in which we perform multiple analyses interpretations simultaneously using MS Excel. This allows us to visualize all data plots simultaneously - and to "link" the analyses to a common set of parameters. While this "simultaneous" analysis approach may seem rudimentary (or even obvious), it provides the critical (and necessary) "visualization" that makes the technique functional. The base relation (given above) renders different behavior for different plotting functions, and we must have a "linkage" that forces all analyses to "connect" to one another. The proposed multiplot spreadsheet approach provides just such a connection.

New decline curve models have been developed to overcome the boundary-dominated flow assumption of the basic Arps’ models, which restricts their application in ultra-low permeability reservoirs exhibiting long-duration transient flow regimes. However, these new decline curve analysis (DCA) methods are still based only on production rate data, relying on the assumption of stable flowing pressure. Since this stabilized state is not reached rapidly in most cases, the applicability of these methods and the reliability of their solutions may be compromised. In addition, production performance predictions cannot be disassociated from the existing operation constraints under which production history was developed. On the other hand, DCA is often carried out without a proper identification of flow regimes. The arbitrary application of DCA models regardless of existing flow regimes may produce unrealistic production forecasts, because these models have been designed assuming specific flow regimes. The main purpose of this study was to evaluate the possible benefits provided by including flowing pressures in production decline analysis. As a result, it have been demonstrated that decline curve analysis based on pressure-normalized rates can be used as a reliable production forecasting technique suited to interpret unconventional wells in specific situations such as unstable operating conditions, limited availability of production data (short production history) and high-pressure, rate-restricted wells. In addition, pressure-normalized DCA techniques proved to have the special ability of dissociating the estimation of future production performance from the existing operation constraints under which production history was developed. On the other hand, it was also observed than more consistent and representative flow regime interpretations may be obtained as diagnostic plots are improved by including MBT, pseudovariables (for gas wells) and pressure-normalized rates. This means that misinterpretations may occur if diagnostic plots are not applied correctly. In general, an improved forecasting ability implies greater accuracy in the production performance forecasts and more reliable reserve estimations. The petroleum industry may become more confident in reserves estimates, which are the basis for the design of development plans, investment decisions, and valuation of companies’ assets.