Interference for adjacent wells may be beneficial to Coalbed-Methane production. The effect is the acceleration of de-watering which should lead to earlier and higher gas rate peaks. It is inherent that permeability anisotropy exists in the coalbed methane formation. It means that the placement of wells (wells configuration) has an effect on the development of coalbed methane field. The effect of Palmer-Mansoori Theory is increasing effective permeability at lower pressures due to matrix shrinkage during desorption. This effect should increase the gas recovery of coalbed methane production. Palmer and Mansoori model should be considered and included to coalbed methane reservoir simulation. These effects and phenomena can be modeled with the CMG simulator. A systematic sensitivity study of various reservoir and operating parameters will result in generalized guidelines for operating these reservoirs more effectively.

Numerical reservoir simulation has been used to describe mechanism of methane gas desorption process, diffusion process, and fluid flow in a coalbed methane reservoir. The reservoir simulation model reflects the response of a reservoir system and the relationship among coalbed methane reservoir properties, operation procedures, and gas production. This work presents a procedure to select the optimum well spacing scenario by using a reservoir simulation. This work uses a two-phase compositional simulator with a dual porosity model to investigate well-spacing effects on coalbed methane production performance and methane recovery. Because of reservoir parameters uncertainty, a sensitivity and parametric study are required to investigate the effects of parameter variability on coalbed methane reservoir production performance and methane recovery. This thesis includes a reservoir parameter screening procedures based on a sensitivity and parametric study. Considering the tremendous amounts of simulation runs required, this work uses a regression analysis to replace the numerical simulation model for each wellspacing scenario. A Monte Carlo simulation has been applied to present the probability function. Incorporated with the Monte Carlo simulation approach, this thesis proposes a well-spacing study procedure to determine the optimum coalbed methane development scenario. The study workflow is applied in a North America basin resulting in distinct Net Present Value predictions between each well-spacing design and an optimum range of well-spacing for a particular basin area.

Optimizing well spacing in unconventional gas reservoirs is difficult due to complex heterogeneity, large variability and uncertainty in reservoir properties, and lack of data that increase the production uncertainty. Previous methods are either suboptimal because they do not consider subsurface uncertainty (e.g., statistical moving-window methods) or they are too time-consuming and expensive for many operators (e.g., integrated reservoir characterization and simulation studies). This research has focused on developing and extending a new technology for determining optimal well spacing in tight gas reservoirs that maximize profitability. To achieve the research objectives, an integrated multi-well reservoir and decision model that fully incorporates uncertainty was developed. The reservoir model is based on reservoir simulation technology coupled with geostatistical and Monte Carlo methods to predict production performance in unconventional gas reservoirs as a function of well spacing and different development scenarios. The variability in discounted cumulative production was used for direct integration of the reservoir model with a Bayesian decision model (developed by other members of the research team) that determines the optimal well spacing and hence the optimal development strategy. The integrated model includes two development stages with a varying Stage-1 time span. The integrated tools were applied to an illustrative example in Deep Basin (Gething D) tight gas sands in Alberta, Canada, to determine optimal development strategies. The results showed that a Stage-1 length of 1 year starting at 160-acre spacing with no further downspacing is the optimal development policy. It also showed that extending the duration of Stage 1 beyond one year does not represent an economic benefit. These results are specific to the Berland River (Gething) area and should not be generalized to other unconventional gas reservoirs. However, the proposed technology provides insight into both the value of information and the ability to incorporate learning in a dynamic development strategy. The new technology is expected to help operators determine the combination of primary and secondary development policies early in the reservoir life that profitably maximize production and minimize the number of uneconomical wells. I anticipate that this methodology will be applicable to other tight and shale gas reservoirs.

The ever increasing energy demand brings about widespread interest to rapidly, profitably and efficiently develop unconventional resources, among which tight gas sands hold a significant portion. However, optimization of development strategies in tight gas fields is challenging, not only because of the wide range of depositional environments and large variability in reservoir properties, but also because the evaluation often has to deal with a multitude of wells, limited reservoir information, and time and budget constraints. Unfortunately, classical full-scale reservoir evaluation cannot be routinely employed by small- to medium-sized operators, given its timeconsuming and expensive nature. In addition, the full-scale evaluation is generally built on deterministic principles and produces a single realization of the reservoir, despite the significant uncertainty faced by operators. This work addresses the need for rapid and cost-efficient technologies to help operators determine optimal well spacing in highly uncertain and risky unconventional gas reservoirs. To achieve the research objectives, an integrated reservoir and decision modeling tool that fully incorporates uncertainty was developed. Monte Carlo simulation was used with a fast, approximate reservoir simulation model to match and predict production performance in unconventional gas reservoirs. Simulation results were then fit with decline curves to enable direct integration of the reservoir model into a Bayesian decision model. These integrated tools were applied to the tight gas assets of Unconventional Gas Resources Inc. in the Berland River area, Alberta, Canada.

From the Proceedings of the 1975 Meetings of the Arizona Section - American Water Resources Assn. and the Hydrology Section - Arizona Academy of Science - April 11-12, 1975, Tempe, Arizona / Conclusions drawn from a water resources study of the woody mountain area are: the average coefficients of transmissibility and of storage of the principal aquifer are approximately 30,000 gpd/ft and 0.05 respectively; drawdown in wells is greater than predicted using theoretical calculations due to the turbulent flow near the well bore in the fractured Coconino aquifer; the computed interference between pumped wells in the field ranges from 10.5 ft. To 19.7 ft. Interference would be negligible between wells spaced at distances greater than 6,000 ft. For pumping periods as long as two hundred days; the negative boundary effect of off-set on the oak creek fault may be balanced by the recharge effect of groundwater located in the highly permeable fractured zone adjacent to the fault; and the quantity of recharge water to the well field is greater than withdrawals from the wells.