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We have used laboratory and synchrotron x-ray diffraction to investigate the structural and chemical changes undergone by polycrystalline RbH(2)PO(4) upon heating within the 30-250 °C temperature interval. Our data show no evidence of the previously reported onset of partial polymerization at T = 96 °C (Park et al 2001 J. Phys.: Condens. Matter 13 9411) which was proposed as an explanation for the high-temperature proton conductivity enhancement in phosphate-based solid acids. Instead, we found that a tetragonal [Formula: see text] monoclinic polymorphic transition initiates at T≈90 °C. The transition is complete at T≈130 °C, and the new monoclinic RbH(2)PO(4) polymorph is stable upon further heating to T = 200 °C. Moreover, its crystal structure is isomorphic to that of monoclinic CsH(2)PO(4). This remarkable similarity suggests that the microscopic structures and dynamics responsible for the high-temperature superprotonic behavior of RbH(2)PO(4) could be the same as those of its Cs-based counterpart.

As permanent downhole gauges have become more reliable, increasingly operators are installing them on injection/production wells. Each time the well is shut in, often solely for operational reasons, pressure transient analysis (PTA) can be performed on the resulting falloff/buildup transient lasting from a few hours to a few days. When the well is flowing, the permanent gauge provides a long term record of pressure behavior during injection/production. Usually the well rate is also determined on a continuous basis, daily or monthly, although often at a much lower data rate than the pressures recorded by the permanent gauge. Recorded pressure and flow rate data are typically used for production data analysis (PDA). Due to the difference in data collection between PTA and PDA, these analyses are performed independently, yielding multiple interpretations from a diverse group of people and software programs. At times the results may conflict, and creating one consistent well and reservoir characterization can be quite challenging and time consuming. A unified interpretation of both analyses would reduce analysis time and increase confidence in the results. In this study, relatively short-duration PTA data and long-term PDA data are combined to provide a more complete analysis than either PTA or PDA alone can provide. The unified analysis is displayed on a combined plot of pressure change and its derivative from a single transient flow period (preferably a falloff/buildup), and rate normalized pressure (RNP) and its derivative based on injection/production data. Data processing techniques follow a stepwise procedure to construct the combined plot. The result is a virtual drawdown response that can be diagnosed like pressure and pressure derivative and matched with an appropriate model for the well and its drainage area. The method is most effective when flow remains single phase or at least segregated in the reservoir. Apart from the unified PTA and PDA approach, this study also offers significant improvements in the RNP data processing that leads to a much more conclusive PDA. Synthetic examples illustrate the effectiveness of the technique for known models. Then two field examples show its practical application. Introduction While the log-log diagnostic plot presentation of pressure change and its derivative with respect to the natural log of the superposition time is almost universally accepted as a starting point for modern pressure transient analysis (PTA) of pressure buildup data, there are a number of presentations and approaches commonly used for production data analysis (PDA), including Arps (1945) decline curve analysis, Fetkovich (1980) rate decline type curves combining transient and boundary dominated rated decline behavior, Palacio and Blasingame (1993) type curves allowing for variable rate production by graphing productivity index versus material balance time, rate normalized pressure (RNP) and derivative, and the integral RNP and derivative, used as a means to reduce noise in the RNP response. The Arps and Fetkovich decline curve analysis is empirical in nature, but more recent approaches enable quantification of important reservoir characteristics such as permeability and the well drainage volume.