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In the Enping 17 sag within the Pearl River Mouth Basin in the South China Sea, one wildcat well has been drilled to the Lower Paleogene Enping Formation (FM EP) and partially into the Wenchang Formation (FM WC) for deep formation hydrocarbon exploration. However, no commercial play was discovered. The reasons for this are clear if the petroleum systems modeling is examined. In FM EP, the main reason for failure is due to poor sealing. In FM WC, the failure is due to the lack of a good reservoir for hydrocarbon accumulation. Encountering a 9 m thick reservoir at a depth of 4650 m indicates that braided fluvial delta and lowstand turbidite sandstone may develop in FM WC. With the objective of establishing cap rock in FM EP and reservoir rock in FM WC, and in the absence of sufficient well data, an integrated framework for 3D seismic reservoir characterization of offshore deep and thin layers was developed. The workflow includes seismic data reprocessing, well-log-based rock-physics analysis, seismic structure interpretation, simultaneous amplitude variation with offset (AVO) inversion, 3D lithology prediction, and geologic integrated analysis. We present four key solutions to address four specific challenges in this case study: (1) the application of adaptive deghosting techniques to remove the source and streamer depth-related ghost notches in the seismic data bandwidth and the relative amplitude-preserved bandwidth extension technique to improve the seismic data resolution; (2) a practical rock-physics modeling approach to consider the formation overpressure for pseudoshear sonic log prediction; (3) interactive and synchronized workflow between prestack 3D AVO inversion and seismic processing to predict a 9 m thick layer in FM WC through more than 60 rounds of cyclic tests; and (4) cross validation between seismic qualitative attributes and quantitative inversion results to verify the lithology prediction result under the condition of insufficient well data.

Only one wildcat well had been drilled for deep formation hydrocarbon exploration (4400 m to 4800 m) and partially penetrated into Lower Paleogene Formation Wenchang (FM WC) without commercial oil discovery in the Enping 17 Sag, northern of the Pearl River Mouth Basin in the South China Sea. Although large thicknesses of grey lacustrine mudstone with abundant organic material are proven to be the regional source rock, lack of a good reservoir for hydrocarbon accumulation in FM WC is the failure reason for this well. Encountering a 9-m-thick sand layer at a depth of 4650 m indicates that braided fluvial delta and lowstand turbidite sandstone may develop in the FM WC, and the zones below upper section 1 of FM WC are proved to be overpressure. It protects pores in the deep reservoir from compaction. Condensate gas was observed by drill stem test (DST) in overpressure sand layer while no gas was measured in sand layers above the overpressure zone. Aiming at delineating effective reservoir in FM WC, an integrated workflow for 3D seismic reservoir characterization of offshore deep and thin layers was developed for this area without sufficient well data. The workflow includes seismic data reprocessing, well log-based rock physics analysis, seismic structure interpretation, 3D simultaneous amplitude variation with offset (AVO) inversion, 3D lithology prediction, and geological integrated analysis. We present four key solutions to address four specific challenges in this case study: 1) application of adaptive deghosting (AD) techniques to remove the source and streamer depth-related ghost notches in the seismic data spectrum and the bandwidth extension (BWXT) technique to improve the seismic data resolution; 2) a practical rock physics modeling approach to consider the formation overpressure for pseudo-shear sonic log prediction; 3) interactive and synchronized workflow between prestack 3D AVO inversion and seismic processing to predict a 9-m-thick layer in FM WC after 60+ rounds of cyclic tests; 4) cross validation between seismic qualitative attributes and quantitative inversion results to verify the lithology prediction result with limited well data.

Existing research in small-area demographic forecasting suffers from two important limitations: (1) a paucity of studies that quantify patterns of error in either total or age/sex-specific estimates and (2) limited methodological innovation aimed specifically at improving the accuracy of such forecasts. This paper attempts to fill, in part, these gaps in existing research by presenting a comparative evaluation of the accuracy of standard and spatially-weighted Hamilton–Perry forecasts for urbanized census tracts within incorporated New Mexico municipalities. These comparative forecasts are constructed for a 10-year horizon (base 1 April 2000 and target 1 April 2010), then compared to the results of the 2010 Census in an ex post facto evaluation. Results are presented for the standard Hamilton–Perry forecasts as well as two sets that incorporate two common variants of spatial weights to improve forecast accuracy. Findings are discussed in the context of what is currently known about error in small-area demographic forecasts and with an eye toward continued innovations.

While the housing-unit method continues to be the preferred method nationwide for producing small-area population estimates, this procedures lacks a method for making age/sex-specific estimates. This paper reports evaluation research on implementation of component-based methods for estimating census tract populations with age/sex detail. Two alternatives are explored: (1) the Component I method relying upon estimates of births, deaths, and net-migration and (2) the Component III method relying solely upon 1990 and 2000 Census counts. From an April 1, 2000 base, each method is used to make estimates moving forward to an April 1, 2010 estimate that is compared to the results of the 2010 Census. The two methods are compared in terms of accuracy and bias using both absolute and algebraic mean and median percentage errors. Results are reviewed and discussed in light of their implications for applied demographers tasked with making small-area demographic estimates.

Small-area population estimates are often made using geocoded address data in conjunction with the housing-unit method. Previous research, however, suggests that these data are subject to systematic incompleteness that biases estimates of race, ethnicity, and other important demographic characteristics. This incompleteness is driven largely by an inability to complete georeference address-based datasets. Given these challenges, small-area demographers need further, and to date largely unavailable, information on the amount of error typically introduced by using incompletely geocoded data to estimate population. More specifically, we argue that applied demographers should like to know if these errors are statistically significant, spatially patterned, or systematically related to specific population characteristics. This paper evaluates the impact of incomplete geocoding on accuracy in small-area population estimates, using a Vintage 2000 set of block-group estimates of the household population for the Albuquerque, NM metro area. Precise estimates of the impact of incomplete geocoding on the accuracy of estimates are made, associations with specific demographic characteristics are considered, and a simple potential remediation based on Horvitz-Thompson theory is presented. The implications of these results for the practice of applied demography are reviewed.

Accurate description of the distribution of housing units within sub-County geographies is an important component of small-area population estimation. This paper pilots the use of the Pearl-Reed logistic model to predict housing unit growth in urban Census tracts in Bernalillo County, New Mexico for 2007. The model is based upon 1990 to 2000 growth rates, constrained with respect to a priori estimates of an upper-limit of housing units that could potentially be built within a tract based on its land area. In spite of the simplistic nature of this model, it is found to perform quite well. Further development based on incorporation of additional economic, demographic, and sociologic data would likely improve the model substantially; however, in this study the model out-performed standard trend extrapolation procedures for the study area and displayed error measures comparable to those reported in the literature for extrapolation methods in general.

This chapter explores the possibility of using administrative records to produce sub-county, municipal-level population estimates. Geocoding of vital records data is combined with IRS summary statistics on filers and dependents at the zip-code level to produce two sets of vintage 2010 Component 1 estimates for all 103 municipalities within the State of New Mexico; one made with no remediation for incomplete geocoding and the other remediated for observed biases in geocoding experiments conducted at the zip-code level. These estimates are compared against the results of the 2010 Census using an ex-post-facto evaluation strategy and standard measures of error and bias. The performance of the non-remediated and remediated estimates are compared to a null model of holding the 2000 Census constant and to a vintage 2010 set of estimates produced by the U.S. Census Bureau using their distributive housing unit method (D-HUM). The results suggest that spatial remediation does little to improve accuracy at the municipal level, and although both sets of component estimates represented significant improvements over the Census 2000 constant estimates, neither out-performed the (D-HUM) procedure, which was considerably more accurate and less biased–especially within the most rapidlygrowing municipalities. While the production of the component method-based estimates might permit the estimation of sub-county components of change, the results of this research suggest that this potential improvement would come at the cost of overall accuracy.

New Mexico’s urban communities mirror a national trend of resegregation clustered by socioeconomic status and race. Previous research suggests a clear relationship between such clustering and measures of both poverty and educational attainment. This research explores the effect of spatially concentrated poverty on academic achievement in a New Mexico School District, utilizing spatial regression to test hypotheses concerning neighborhood clustering effects on school performance. Proficiency scores for reading, math, and science were collected for the 2004–2006 school years and linked to data on poverty from the 2005–2009 American Community Survey as well as from National Center for Education Statistics (NCES) reporting of the proportion of students receiving free or reduced lunch. Next, following the elementary school students in a pseudo-cohort fashion, it is seen that geography plays an important role on how, on the average, the probability of successful high school graduation depends on high-, middle-, and elementary-school parameters as well as community variables. The implications of the results for understanding relationships between poverty and academic achievement, and the geographic clustering of these patterns, are reviewed.

Estimates of age-specific fertility rates based on survey data are known to suffer down-bias associated with incomplete reporting. Previously, William Brass (1964, 1965, 1968) proposed a series of adjustments of such data to reflect more appropriate levels of fertility through comparison with data on children-ever-born by age, a measure of cohort-specific cumulative fertility. His now widely-used Parity/Fertility or PF ratio method makes a number of strong assumptions, which have been the focus of an extended discussion in the literature on indirect estimation. However, while it is clear that the measures used in making adjusted age-specific fertility estimates with this method are captured with statistical uncertainty, little discussion of the nature of this uncertainty around PF-ratio based estimates of fertility has been entertained in the literature. Since both age-specific risk of childbearing and cumulative parity (children ever born) are measured with statistical uncertainty, an unknown credibility interval must surround every PF ratio-based estimate. Using the standard approach, this is unknown, limiting the ability to make statistical comparisons of fertility between groups or to understand stochasticity in population dynamics. This paper makes use of approaches applied to similar problems in engineering, the natural sciences, and decision analysis--often discussed under the title of uncertainty analysis or stochastic modeling--to characterize this uncertainty and to present a new method for making PF ratio-based fertility estimates with 95 percent uncertainty intervals. The implications for demographic analysis, between-group comparisons of fertility, and the field of statistical demography are explored.

Demographic estimates of population at risk often underpin epidemiologic research and public health surveillance efforts. In spite of their central importance to epidemiology and public-health practice, little previous attention has been paid to evaluating the magnitude of errors associated with such estimates or the sensitivity of epidemiologic statistics to these effects. In spite of the well-known observation that accuracy in demographic estimates declines as the size of the population to be estimated decreases, demographers continue to face pressure to produce estimates for increasingly fine-grained population characteristics at ever-smaller geographic scales. Unfortunately, little guidance on the magnitude of errors that can be expected in such estimates is currently available in the literature and available for consideration in small-area epidemiology. This paper attempts to fill this current gap by producing a Vintage 2010 set of single-year-of-age estimates for census tracts, then evaluating their accuracy and precision in light of the results of the 2010 Census. These estimates are produced and evaluated for 499 census tracts in New Mexico for single-years of age from 0 to 21 and for each sex individually. The error distributions associated with these estimates are characterized statistically using non-parametric statistics including the median and 2.5th and 97.5th percentiles. The impact of these errors are considered through simulations in which observed and estimated 2010 population counts are used as alternative denominators and simulated event counts are used to compute a realistic range fo prevalence values. The implications of the results of this study for small-area epidemiologic research in cancer and environmental health are considered.