Reservoir models typically utilized for desorption-controlled reservoirs such as coals and gas shales possess dual-porosity/ single-permeability characteristics. In this case dual-porosity means that two in-situ locations exist that can be used for gas storage, adsorbed within the matrix and in the free form in the cleat system. Single-permeability, which refers to the cleat system, is the only permeability network that gas or water must flow through to reach the wellbore. While this approach to modeling coals and shales has become accepted practice, experience has shown that the models can frequently be in gross error when forecasting well or field performance based on limited reservoir and/or production data; gas production is usually over-predicted and water production under-predicted. The implications for economic decision-making in an exploration mode are obvious, and there are many examples of projects that have suffered from this very problem. Further, reservoir parameters derived from history-matching, when historical gas production does exist, are commonly found to be inconsistent with measured permeability and gas sorption/content data. While there has been considerable effort focused on improved data collection procedures, such as well testing and gas content measurement for example, these problems persist. While performing reservoir studies in the Antrim shale and low-rank coal plays throughout the world, it became clear that the accepted assumption of gas desorbing directly from the coal matrix into the cleat system is not entirely valid. In practice, gas production occurs much later than the models predict, and cannot be adequately explained though the normal parameters of sorption time, permeability, relative permeability, etc. Analysis of core and other data suggests that another porosity and permeability system is required to account for this effect, specifically within the matrix blocks themselves. An advanced, triple-porosity/dual-permeability model has therefore been developed, in which gas desorbs from the internal matrix block surfaces, migrates via conventional Darcy flow through micro-permeability matrix, and into the cleat system where it then flows to the wellbore. Water can also be stored both within the matrix blocks and in the cleat system. In essence, this model requires that desorbed gas must work its way through the matrix before reaching the cleat system, and must establish a relative permeability to gas within the matrix block before it can do so. This geometry is similar to conventional dual-porosity models, with the addition of an adsorbed gas component. Comparisons of this new model versus the historical modeling approach confirm that the new model predicts lower gas and higher water production rates, consistent with field evidence. Further, more accurate production forecasts can be achieved using measured well test information (for the cleat permeability), low cleat porosities (which are known to exist), and lab-derived porosity and permeability data for the matrix block properties. This paper presents the historical accuracy problem with reservoir simulation in desorption-controlled reservoirs, the practical theory behind the new model, comparisons between the new and conventional models, and some example applications.
Coalseams represent an attractive opportunity for near-term sequestration of large volumes of anthropogenic CO2 at low net costs. There are several reasons for this:Coals have the ability to physically adsorb large volumes of CO2 in a highly concentrated state.Coals are frequently located near large point sources of CO2 emissions, specifically power generation plants.The injection of CO2 into coalseams actually enhances the commercial methane recovery process.The recovery of coalbed methane is enhanced when the injected gas contains nitrogen, a major constituent of power plant flue gas. A joint U.S. Department of Energy and industry project to study the reservoir mechanisms and field performance of CO2 sequestration in coalseams has recently been initiated. The project involves laboratory and field-testing to define critical reservoir mechanisms, including multi-component (CO2-CH4-N2 ternary) sorption behavior. Two existing fields in the San Juan Basin, the most prolific coalbed methane basin in the world, are currently under CO2 and/or N2 injection. These two fields, the Tiffany Unit (operated by BP) – now under N2 injection (but with mixed CO2/N2 injection being studied), and the Allison Unit (operated by Burlington Resources) – under CO2 injection since 1995 will be thoroughly studied via reservoir simulation to understand CO2 sequestration and enhanced coalbed methane recovery performance, using both pure CO2 and N2, as well as CO2/N2 mixtures. This paper presents the fundamental reservoir mechanisms of CO2 sequestration and enhanced recovery of methane from coalseams, and the field performances to date of the Tiffany and Allison Units. Introduction and Background The concentration of carbon dioxide (CO2) in the atmosphere is rising and, due to growing concern about its effects, the U.S. and over 160 other countries ratified the Rio Mandate in 1992, which calls for"...stabilization of greenhouse gas concentrations in the atmosphere at a level that would prevent dangerous anthropogenic interference with the climate system". Since under virtually any stabilization or market scenario fossil fuels will remain the mainstay of energy production for the foreseeable future even modest stabilization will require enormous reductions in greenhouse gas (GHG) emissions resulting from fossil fuel use; energy-related CO2 emissions resulting from fossil-fuel combustion account for 82% of all U.S. GHG emissions1. Further, in addition to emissions reductions via fuel-switching, conservation, and efficiency improvements, achieving atmospheric stabilization that is deemed acceptable will require large-scale, low-cost sequestration of carbon, a need for which no cost-effective technology exists today. As a result, the U.S. Department of Energy (DOE) developed its carbon sequestration R&D program, which addresses the entire carbon sequestration ‘life cycle’ of capture, separation, transport, and storage or reuse.
TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractThis paper summarizes the development of a methodology for the restimulation candidate selection in tight gas sands. The methodology incorporates virtual intelligence techniques (artificial neural networks, genetic algorithms and fuzzy logic) to achieve this objective. Artificial neural networks are used to develop a representative model of the completion and hydraulic fracturing process in a specific field. Genetic algorithms are used as a search and optimization tool to identify the missed incremental production based on the neural network model. Finally fuzzy logic is used to capture the unique field experiences of the engineers as well as detrimental parameters (if such parameters are indeed present) and incorporate them in the decision making process. Approximate reasoning approach is used at the decision making level to identify the restimulation candidates. Once the methodology is introduced, it is applied to an actual tight sand field in the Rocky Mountain region and the results are presented.
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