Improved normalization of time‐lapse seismic data using normalized root mean square repeatability data to improve automatic production and seismic history matching in the Nelson field

Updating of reservoir models by history matching of 4D seismic data along with production data gives us a better understanding of changes to the reservoir, reduces risk in forecasting and leads to better management decisions. This process of seismic history matching requires an accurate representation of predicted and observed data so that they can be compared quantitatively when using automated inversion. Observed seismic data is often obtained as a relative measure of the reservoir state or its change, however. The data, usually attribute maps, need to be calibrated to be compared to predictions. In this paper we describe an alternative approach where we normalize the data by scaling to the model data in regions where predictions are good. To remove measurements of high uncertainty and make normalization more effective, we use a measure of repeatability of the monitor surveys to filter the observed time‐lapse data. We apply this approach to the Nelson field. We normalize the 4D signature based on deriving a least squares regression equation between the observed and synthetic data which consist of attributes representing measured acoustic impedances and predictions from the model. Two regression equations are derived as part of the analysis. For one, the whole 4D signature map of the reservoir is used while in the second, 4D seismic data is used from the vicinity of wells with a good production match. The repeatability of time‐lapse seismic data is assessed using the normalized root mean square of measurements outside of the reservoir. Where normalized root mean square is high, observations and predictions are ignored. Net: gross and permeability are modified to improve the match. The best results are obtained by using the normalized root mean square filtered maps of the 4D signature which better constrain normalization. The misfit of the first six years of history data is reduced by 55 per cent while the forecast of the following three years is reduced by 29 per cent. The well based normalization uses fewer data when repeatability is used as a filter and the result is poorer. The value of seismic data is demonstrated from production matching only where the history and forecast misfit reductions are 45% and 20% respectively while the seismic misfit increases by 5%. In the best case using seismic data, it dropped by 6%. We conclude that normalization with repeatability based filtering is a useful approach in the absence of full calibration and improves the reliability of seismic data.     

A data variance technique for automated despiking of magnetotelluric data with a remote reference

The magnetotelluric method employs co‐located surface measurements of electric and magnetic fields to infer the local electrical structure of the earth. The frequency dependent ‘apparent resistivity’ curves can be inaccurate at long periods if input data are contaminated – even when robust remote reference techniques are employed. Data despiking prior to processing can result in significantly more reliable estimates of long period apparent resistivities. This paper outlines a two‐step method of automatic identification and replacement for spike‐like contamination of magnetotelluric data; based on the simultaneity of natural electric and magnetic field variations at distant sites. This simultaneity is exploited both to identify windows in time when the array data are compromised as well as to generate synthetic data that replace observed transient noise spikes. In the first step windows in data time series that contain spikes are identified according to an intersite comparison of channel ‘activity’– such as the variance of differenced data within each window. In the second step, plausible data for replacement of flagged windows are calculated by Wiener filtering coincident data in clean channels. The Wiener filters – which express the time‐domain relationship between various array channels – are computed using an uncontaminated segment of array training data. Examples are shown where the algorithm is applied to artificially contaminated data and to real field data. In both cases all spikes are successfully identified. In the case of implanted artificial noise, the synthetic replacement time series are very similar to the original recording. In all cases, apparent resistivity and phase curves obtained by processing the despiked data are much improved over curves obtained from raw data.     

Model based analysis of lateral and vertical soil carbon fluxes induced by soil redistribution processes in a small agricultural catchment

Soil redistribution on arable land significantly affects lateral and vertical soil carbon (C) fluxes (caused by C formation and mineralization) and soil organic carbon (SOC) stocks. Whether this serves as a (C) sink or source to the atmosphere is a controversial issue. In this study, the SPEROS‐C model was modified to analyse erosion induced lateral and vertical soil C fluxes and their effects upon SOC stocks in a small agricultural catchment (4·2 ha). The model was applied for the period between 1950 and 2007 covering 30 years of conventional tillage (1950–1979) followed by 28 years of conservation tillage (1980–2007). In general, modelled and measured SOC stocks are in good agreement for three observed soil layers. The overall balance (1950–2007) of erosion induced lateral and vertical C fluxes results in a C loss of ?4·4 g C m^–2 a^–1 at our test site. Land management has a significant impact on the erosion induced C fluxes, leading to a predominance of lateral C export under conventional and of vertical C exchange between soil and atmosphere under conservation agriculture. Overall, the application of the soil conservation practices, with enhanced C inputs by cover crops and decreased erosion, significantly reduced the modelled erosion induced C loss of the test site. Increasing C inputs alone, without a reduction of erosion rates, did not result in a reduction of erosion induced C losses. Moreover, our results show that the potential erosion induced C loss is very sensitive to the representation of erosion rates (long‐term steady state versus event driven). A first estimate suggests that C losses are very sensitive to magnitude and frequency of erosion events. If long‐term averages are dominated by large magnitude events modelled erosion induced C losses in the catchment were significantly reduced. Copyright © 2011 John Wiley & Sons, Ltd.     

A retrospective analysis of contamination and periphyton PICT patterns for the antifoulant irgarol 1051, around a small marina on the Swedish west coast

Irgarol is a triazine photosystem II (PSII) inhibitor that has been used in Sweden as an antifouling ingredient since the 1990s. Early microcosm studies indicated that periphyton was sensitive to irgarol at concentrations regularly found in harbours and marinas. However, field studies of irgarol effects on the Swedish west coast in 1994, using the pollution-induced community tolerance (PICT) approach, failed to detect any effects of the toxicant in the field. A PICT study involves sampling of replicate communities in a gradient of contamination, and a comparison of their community tolerance levels, with an increase being an indication that sensitive species have been eliminated and replaced by more tolerant ones. Typically, short-term assays are used to quantify the community tolerance levels. Later PICT studies in the same area over a 10 year period demonstrate that irgarol tolerance levels have increased, although the contamination pattern has been stable. Our results support the hypothesis that that the PICT potential was low initially, due to a small differential sensitivity between the community members, and that a persistent selection pressure was required to favour and enrich irgarol-tolerant species or genotypes.     

Analysis of Signals from an Unique Ground-Truth Infrasound Source Observed at IMS Station IS26 in Southern Germany

Quantitative modeling of infrasound signals and development and verification of the corresponding atmospheric propagation models requires the use of well-calibrated sources. Numerous sources have been detected by the currently installed network of about 40 of the final 60 IMS infrasound stations. Besides non-nuclear explosions such as mining and quarry blasts and atmospheric phenomena like auroras, these sources include meteorites, volcanic eruptions and supersonic aircraft including re-entering spacecraft and rocket launches. All these sources of infrasound have one feature in common, in that their source parameters are not precisely known and the quantitative interpretation of the corresponding signals is therefore somewhat ambiguous. A source considered well-calibrated has been identified producing repeated infrasound signals at the IMS infrasound station IS26 in the Bavarian forest. The source results from propulsion tests of the ARIANE-5 rocket’s main engine at a testing facility near Heilbronn, southern Germany. The test facility is at a range of 320 km and a backazimuth of ~280° from IS26. Ground-truth information was obtained for nearly 100 tests conducted in a 5-year period. Review of the available data for IS26 revealed that at least 28 of these tests show signals above the background noise level. These signals are verified based on the consistency of various signal parameters, e.g., arrival times, durations, and estimates of propagation characteristics (backazimuth, apparent velocity). Signal levels observed are a factor of 2–8 above the noise and reach values of up to 250 mPa for peak amplitudes, and a factor of 2–3 less for RMS measurements. Furthermore, only tests conducted during the months from October to April produce observable signals, indicating a significant change in infrasound propagation conditions between summer and winter months.     

Low‐frequency passive seismic experiments in Abu Dhabi,United Arab Emirates: implications for hydrocarbon detection

Low‐frequency passive seismic experiments utilizing arrays of 3‐component broadband seismometers were conducted over two sites in the emirate of Abu Dhabi in the United Arab Emirates. The experiments were conducted in the vicinity of a producing oilfield and around a dry exploration well to better understand the characteristics and origins of microtremor signals (1–6 Hz), which had been reported as occurring exclusively above several hydrocarbon reservoirs in the region. The results of the experiments revealed that a strong correlation exists between the recorded ambient noise and observed meteorological and anthropogenic noises. In the frequency range of 0.15–0.4 Hz, the dominant feature is a double‐frequency microseism peak generated by the non‐linear interactions of storm induced surface waves in the Arabian Sea. We observed that the double‐frequency microseism displays a high variability in spectral amplitude, with the strongest amplitude occurring when Cyclone Gonu was battering the eastern coast of Oman; this noise was present at both sites and so is not a hydrocarbon indicator. Moreover, this study found that very strong microtremor signals in the frequency range of 2–3 Hz were present in all of the locations surveyed, both within and outside of the reservoir boundary and surrounding the dry exploration well. This microtremor signal has no clear correlation with the microseism signals but significant variations in the characteristics of the signals were observed between daytime and nighttime recording periods that clearly correlate with human activity. High‐resolution frequency‐wavenumber (f‐k) spectral analyses were performed on the recorded data to determine apparent velocities and azimuths of the wavefronts for the microseism and microtremor events. The f‐k analyses confirmed that the double‐frequency microseism originates from wave activity in the Arabian Sea, while the microtremor events have an azimuth pointing towards the nearest motorways, indicating that they are probably being excited by traffic noise. Results drawn from particle motion studies confirm these observations. The vertical‐to‐horizontal spectral ratios of the data acquired in both experiments show peaks around 2.5–3 Hz with no dependence on the presence or absence of subsurface hydrocarbons. Therefore, this method should not be used as a direct hydrocarbon indicator in these environments. Furthermore, the analyses provide no direct evidence to indicate that earthquakes are capable of stimulating the hydrocarbon reservoir in a way that could modify the spectral amplitude of the microtremor signal.     

Morphometric comparisons between automated and manual karst depression inventories in Apalachicola National Forest,Florida, and Mammoth Cave National Park,Kentucky, USA

Karst depression catalogs are critical to assessing the hydrology and geohazards of an area; yet, the delineation of these features within a landscape can be a difficult, time-consuming and subjective task. This study evaluates the efficacy of karst depression inventorying using an automated fill-difference method operating on high-resolution lidar-derived digital elevation models (DEMs). The resulting catalog is compared with existing karst depression inventories for two low-development areas of the USA, Mammoth Cave National Park (MACA) and Apalachicola National Forest (ANF), where karst depressions have been mapped previously using a manual closed-contour approach. The automated fill method captures 93 and 85 % of these previously mapped karst depressions at MACA and ANF, respectively. Field observations and topographic analysis suggest that the omitted features were likely misclassified within the existing catalogs. The automated routine returns 797 and 3377 additional topographic depressions, at MACA and ANF, respectively, which are not included in the existing catalogs. While the geology of ANF is mostly homogenous Quaternary deposits, the newly identified, typically smaller-scale depressions found within MACA tend to be disproportionally located in non-carbonate-dominated formations, where the development of karst may be restricted by geologic heterogeneity. Within both areas, the size distributions of the two inventories are statistically identical for features larger than ~10³ m² in area or ~3 m in depth. For individual depressions captured by both methods at MACA, the automated fill-difference routine tends to return a slightly larger estimate of depression size and aggregate small depressions into larger ones. Conversely, at ANF, some low-relief depressions may be disaggregated by the fill-difference technique, with a trend toward smaller estimated depression areas when the automated method is employed. The automated fill-difference method, operating on high-resolution lidar-derived DEMs, can reproduce and expand the existing inventories of karst depressions, while minimizing false detections that may be inherent within pre-existing catalogs.     

THE MARBLE BEACHES OF TUSCANY*

ABSTRACT. Beach‐nourishment operations designed to replace sediment lost through erosion change the identity and meaning of coastal landscapes. Seven beaches in Tuscany, nourished with marble‐quarry waste, reveal how an industrial byproduct is naturalized by particle rounding and sorting and can become a positive symbol of human‐altered nature. The marble was placed on formerly sandy beaches, resulting in different grain size and color of sediments, beach morphology, and value for human use. The abrasion rate of marble makes the nourished beaches unsatisfactory when viewed solely as protection structures, but the rapid particle rounding and aesthetic appeal of marble increase the acceptability of the beaches for recreation.