Thesis (M.S.) in Forestry--University of Maine, 2004. / Includes vita. Includes bibliographical references (leaves 83-88).

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Probabilistic decline curve analysis (PDCA) methods have been developed to quantify uncertainty in production forecasts and reserves estimates. However, the application of PDCA in shale gas reservoirs is relatively new. Limited work has been done on the performance of PDCA methods when the available production data are limited. In addition, PDCA methods have often been coupled with Arp’s equations, which might not be the optimum decline curve analysis model (DCA) to use, as new DCA models for shale reservoirs have been developed. Also, decline curve methods are based on production data only and do not by themselves incorporate other types of information, such as volumetric data. My research objective was to integrate volumetric information with PDCA methods and DCA models to reliably quantify the uncertainty in production forecasts from hydraulically fractured horizontal shale gas wells, regardless of the stage of depletion. In this work, hindcasts of multiple DCA models coupled to different probabilistic methods were performed to determine the reliability of the probabilistic DCA methods. In a hindcast, only a portion of the historical data is matched; predictions are made for the remainder of the historical period and compared to the actual historical production. Most of the DCA models were well calibrated visually when used with an appropriate probabilistic method, regardless of the amount of production data available to match. Volumetric assessments, used as prior information, were incorporated to further enhance the calibration of production forecasts and reserves estimates when using the Markov Chain Monte Carlo (MCMC) as the PDCA method and the logistic growth DCA model. The proposed combination of the MCMC PDCA method, the logistic growth DCA model, and use of volumetric data provides an integrated procedure to reliably quantify the uncertainty in production forecasts and reserves estimates in shale gas reservoirs. Reliable quantification of uncertainty should yield more reliable expected values of reserves estimates, as well as more reliable assessment of upside and downside potential. This can be particularly valuable early in the development of a play, because decisions regarding continued development are based to a large degree on production forecasts and reserves estimates for early wells in the play.

The possibility of urban decline in metropolitan post-war inner suburbs is currently being examined in the planning literature, particularly in the United States. Inner suburbs are built between 1946 and 1971 and are therefore older and structurally different from the later suburbs. At the same time, they lack the amenities of the core and the inner cities. This thesis aims to examine whether inner suburban decline is occurring in Canada. 15 largest Census Metropolitan Areas (CMAs) are selected for the purpose of this study. All CMAs are then separated into five urban zones: the core, the inner suburbs, the outer suburbs, and the fringe/exurbs. All zones are then assessed for decline based on relative changes in median household income, average dwelling values, and average gross rent in the period between 1986 and 2006. Subsequently, nine of the largest CMAs are also assessed for declines in the prosperity factor and the exclusivity factor. These variables are extracted via a factor analysis which includes variables measuring demographic, socio-economic, and housing characteristics. Results indicate that inner suburbs declined in median household income, the average value of dwelling, and the prosperity factor measures. In contrast, average gross rent and the exclusivity factor showed less clear results. Overall, the results obtained in this study suggest that Canada’s inner suburbs are experiencing decline. The possible causes of inner suburban decline remain poorly understood. A number of possible explanations are offered, ranging from the lack of urban appeal of the inner suburbs, the decline of the industrial employment sector, to aging housing stock, the movement of displaced low-income immigrants, and the aging of seniors with limited income. More research is necessary in order to establish plausible mechanisms beyond preliminary speculation. A number of policy approaches to inner suburban decline are outlined. Emphasis is placed on the revitalization of housing, greater cooperation between metropolitan regions and implementation of smart growth strategies. Further research avenues include the confirmation of the phenomenon in Canada, as well as policy case studies examining the success of planning approaches in arresting inner suburban decline.

The increasing interest and focus on unconventional reservoirs is a result of the industry's direction toward exploring alternative energy sources. It is due to the fact that conventional reservoirs are being depleted at a fast pace. Shale gas reservoirs are a very favorable type of energy sources due to their low cost and long-lasting gas supply. In general, according to Ausubel (1996), natural gas serves as a transition stage to move from the current oil-based energy sources to future more stable and environment-friendly ones. By looking through production history in the U.S Historical Production Database, HPDI (2009), we learn that the Barnett Shale reservoir in Newark East Field has been producing since the early 90's and contributing a fraction of the U.S daily gas production. Zhao et al. (2007) estimated the Barnett Shale to be producing 1.97 Bcf/day of gas in 2007. It is considered the most productive unconventional gas shale reservoir in Texas. By 2004 and in terms of annual gas production volume, Pollastro (2007) considered the Barnett Shale as the second largest unconventional gas reservoir in the United States. Many studies have been conducted to understand better the production controls in Barnett Shale. However, this giant shale gas reservoir is still ambiguous. Some parts of this puzzle are still missing. It is not fully clear what makes the Barnett well produce high or low amounts of gas. Barnett operating companies are still trying to answer these questions. This study adds to the Barnett chain of studies. It tests the effects of the following on Barnett gas production in the core area (Denton, Wise, and Tarrant counties): * Barnett gross thickness, including the Forestburg formation that divides Barnett Shale. * Perforation footage. * Perforated zones of Barnett Shale. Instead of testing these parameters on each well production decline curve individually, this study uses a new technique to simplify this process. Decline Map Analysis (DMA) is introduced to measure the effects of these parameters on all production decline curves at the same time. Through this study, Barnett gross thickness and perforation footage are found not to have any definite effects on Barnett gas production. However, zone 3 (Top of Lower Barnett) and zone 1 (Bottom of Lower Barnett) are found to contribute to cumulative production. Zone 2 (Middle of Lower Barnett) and zone 4 (Upper Barnett), on the other hand, did not show any correlation or influence on production through their thicknesses.

This thesis evaluates the applicability of forecasting production from low permeability and layered tight gas wells using the Arps hyperbolic equation at earlier times and then switching to the exponential form of the equation at a predetermined minimum decline rate. This methodology is called the minimum terminal decline rate method. Two separate completion types have been analyzed. The first is horizontal completions with multi-stage hydraulic fractures while the second is vertical fractured wells in layered formations, completed with hydraulic fractures. For both completion types both simulated data and real world well performance histories have been evaluated using differing minimum terminal decline rates and the benefit of increasing portions of production history to make predictions. The application of the minimum terminal decline rate method to the simulated data in this study (3 percent minimum decline applied to multiple fractured horizontal wells MFHW- and 7 percent applied to vertical fractured layered wells) gave high errors for some simulations within the first two years. Once additional production data is considered in making predictions, the errors in estimated ultimate recovery and in remaining reserves is significantly reduced. This result provides a note of caution, when using the minimum decline rate method for forecasting using small quantities of production history. The evaluation of real world data using the minimum terminal decline rate method introduces other inaccuracies such as poor data quality, low data frequency, operational changes which affect the production profile and workovers / re-stimulations which require a restart of production forecasting process. Real well data for MFHW comes from the Barnett Shale completions of the type which have been widely utilized since 2004. There is insufficient production history from real wells to determine an appropriate minimum terminal decline rate. In the absence of suitable analogs for the determination of the minimum terminal decline rate it would be impossible to correctly apply this methodology. Real well data for vertical fractured layered wells from the Carthage Cotton Valley field indicate that for wells similar to Conoco operated Panola County wells a feasible decline rate is between 5 percent and 10 percent. Further if a consistent production trend and with more than 2 years of production history are used to forecast, the EUR can be predicted to within plus/minus 10 percent and remaining reserves to within plus/minus 15 percent.

Proved reserves estimation is a crucial process since it impacts aspects of the petroleum business. By definition of the Society of Petroleum Engineers, the proved reserves must be estimated by reliable methods that must have a chance of at least a 90 percent probability (P90) that the actual quantities recovered will equal or exceed the estimates. Decline curve analysis, DCA, is a commonly used method; which a trend is fitted to a production history and extrapolated to an economic limit for the reserves estimation. The trend is the “best estimate” line that represents the well performance, which corresponds to the 50th percentile value (P50). This practice, therefore, conflicts with the proved reserves definition. An exponential decline model is used as a base case because it forms a straight line in a rate-cum coordinate scale. Two straight line fitting methods, i.e. ordinary least square and error-in-variables are compared. The least square method works better in that the result is consistent with the Gauss-Markov theorem. In compliance with the definition, the proved reserves can be estimated by determining the 90th percentile value of the descending order data from the variance. A conventional estimation using a principal of confidence intervals is first introduced to quantify the spread, a difference between P50 and P90, from the variability of a cumulative production. Because of the spread overestimation of the conventional method, the analytical formula is derived for estimating the variance of the cumulative production. The formula is from an integration of production of rate over a period of time and an error model. The variance estimations agree with Monte Carlo simulation (MCS) results. The variance is then used further to quantify the spread with the assumption that the ultimate cumulative production is normally distributed. Hyperbolic and harmonic models are also studied. The spread discrepancy between the analytics and the MCS is acceptable. However, the results depend on the accuracy of the decline model and error used. If the decline curve changes during the estimation period the estimated spread will be inaccurate. In sensitivity analysis, the trend of the spread is similar to how uncertainty changes as the parameter changes. For instance, the spread reduces if uncertainty reduces with the changing parameter, and vice versa. The field application of the analytical solution is consistent to the assumed model. The spread depends on how much uncertainty in the data is; the higher uncertainty we assume in the data, the higher spread. / text

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