Intra-survey reservoir fluctuations – implications for quantitative 4D seismic analysis     

Veronica Omofoma  Colin MacBeth  Hamed Amini 《Geophysical Prospecting》2019,67(2):282-297

During the time taken for seismic data to be acquired, reservoir pressure may fluctuate as a consequence of field production and operational procedures and fluid fronts may move significantly. These variations prevent accurate quantitative measurement of the reservoir change using 4D seismic data. Modelling studies on the Norne field simulation model using acquisition data from ocean-bottom seismometer and towed streamer systems indicate that the pre-stack intra-survey reservoir fluctuations are important and cannot be neglected. Similarly, the time-lapse seismic image in the post-stack domain does not represent a difference between two states of the reservoir at a unique base and monitor time, but is a mixed version of reality that depends on the sequence and timing of seismic shooting. The outcome is a lack of accuracy in the measurement of reservoir changes using the resulting processed and stacked 4D seismic data. Even for perfect spatial repeatability between surveys, a spatially variant noise floor is still anticipated to remain. For our particular North Sea acquisition data, we find that towed streamer data are more affected than the ocean-bottom seismometer data. We think that this may be typical for towed streamers due to their restricted aperture compared to ocean-bottom seismometer acquisitions, even for a favourable time sequence of shooting and spatial repeatability. Importantly, the pressure signals on the near and far offset stacks commonly used in quantitative 4D seismic inversion are found to be inconsistent due to the acquisition timestamp. Saturation changes at the boundaries of fluid fronts appear to show a similar inconsistency across sub-stacks. We recommend that 4D data are shot in a consistent manner to optimize aerial time coverage, and that additionally, the timestamp of the acquisition should be used to optimize pre-stack quantitative reservoir analysis.  相似文献   

Deep neural network application for 4D seismic inversion to changes in pressure and saturation: Optimizing the use of synthetic training datasets     

Gustavo Côrte  Jesper Dramsch  Hamed Amini  Colin MacBeth 《Geophysical Prospecting》2020,68(7):2164-2185

In this work, we tackle the challenge of quantitative estimation of reservoir dynamic property variations during a period of production, directly from four-dimensional seismic data in the amplitude domain. We employ a deep neural network to invert four-dimensional seismic amplitude maps to the simultaneous changes in pressure, water and gas saturations. The method is applied to a real field data case, where, as is common in such applications, the data measured at the wells are insufficient for properly training deep neural networks, thus, the network is trained on synthetic data. Training on synthetic data offers much freedom in designing a training dataset, therefore, it is important to understand the impact of the data distribution on the inversion results. To define the best way to construct a synthetic training dataset, we perform a study on four different approaches to populating the training set making remarks on data sizes, network generality and the impact of physics-based constraints. Using the results of a reservoir simulation model to populate our training datasets, we demonstrate the benefits of restricting training samples to fluid flow consistent combinations in the dynamic reservoir property domain. With this the network learns the physical correlations present in the training set, incorporating this information into the inference process, which allows it to make inferences on properties to which the seismic data are most uncertain. Additionally, we demonstrate the importance of applying regularization techniques such as adding noise to the synthetic data for training and show a possibility of estimating uncertainties in the inversion results by training multiple networks.  相似文献   

Embedment effects of flexible foundations on control of structures     

Fereidoun Amini  Masoud Shadlou 《Soil Dynamics and Earthquake Engineering》2011

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A Method for Automatic Road Extraction of High Resolution SAR Imagery     

M. Saati  J. Amini  M. Maboudi 《Journal of the Indian Society of Remote Sensing》2015,43(4):697-707

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Environmental changes over the past 25 000 years in the southern Middle Atlas,Morocco          下载免费PDF全文

Jalal Tabel  Carla Khater  Ali Rhoujjati  Laurent Dezileau  Ilham Bouimetarhan  Matthieu Carre  Laurence Vidal  Abdelfattah Benkaddour  Majda Nourelbait  Rachid Cheddadi 《第四纪科学杂志》2016,31(2):93-102

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Effects of convective available potential energy,temperature and humidity on the variability of thunderstorm frequency over Bangladesh     

Wahiduzzaman  Md  Ali  Md. Arfan  Luo  Jing-Jia  Wang  Yu  Uddin  Md. Jalal  Shahid  Shamsuddin  Islam  A. R. M. Towfiqul  Mondal  Sanjit Kumar  Siddiki  Ubaydur Rahaman  Bilal  Muhammad  Qiu  Zhongfeng  Dambul  Ramzah  Eibek  Kutubuddin  Haque  Md. Emdadul 《Theoretical and Applied Climatology》2022,147(1-2):325-346

Theoretical and Applied Climatology - The present study examines the effects of convective available potential energy (CAPE), temperature and humidity on the spatiotemporal variation of...  相似文献   

Implementing landslide path dependency in landslide susceptibility modelling     

Jalal Samia  Arnaud Temme  Arnold K. Bregt  Jakob Wallinga  John Stuiver  Fausto Guzzetti  Francesca Ardizzone  Mauro Rossi 《Landslides》2018,15(11):2129-2144

Landslide susceptibility modelling—a crucial step towards the assessment of landslide hazard and risk—has hitherto not included the local, transient effects of previous landslides on susceptibility. In this contribution, we implement such transient effects, which we term “landslide path dependency”, for the first time. Two landslide path dependency variables are used to characterise transient effects: a variable reflecting how likely it is that an earlier landslide will have a follow-up landslide and a variable reflecting the decay of transient effects over time. These two landslide path dependency variables are considered in addition to a large set of conditioning attributes conventionally used in landslide susceptibility. Three logistic regression models were trained and tested fitted to landslide occurrence data from a multi-temporal landslide inventory: (1) a model with only conventional variables, (2) a model with conventional plus landslide path dependency variables, and (3) a model with only landslide path dependency variables. We compare the model performances, differences in the number, coefficient and significance of the selected variables, and the differences in the resulting susceptibility maps. Although the landslide path dependency variables are highly significant and have impacts on the importance of other variables, the performance of the models and the susceptibility maps do not substantially differ between conventional and conventional plus path dependent models. The path dependent landslide susceptibility model, with only two explanatory variables, has lower model performance, and differently patterned susceptibility map than the two other models. A simple landslide susceptibility model using only DEM-derived variables and landslide path dependency variables performs better than the path dependent landslide susceptibility model, and almost as well as the model with conventional plus landslide path dependency variables—while avoiding the need for hard-to-measure variables such as land use or lithology. Although the predictive power of landslide path dependency variables is lower than those of the most important conventional variables, our findings provide a clear incentive to further explore landslide path dependency effects and their potential role in landslide susceptibility modelling.  相似文献   

Land use change assessment in coastal mangrove forests of Iran utilizing satellite imagery and CA–Markov algorithms to monitor and predict future change     

Hana Etemadi  Joseph M. Smoak  Jalal Karami 《Environmental Earth Sciences》2018,77(5):208

Mangrove forest stores large organic carbon stocks in a setting that is highly vulnerable to climate change and direct anthropogenic influences. As such there is a need to elucidate the causes and consequences of land use change on these ecosystems that have high value in terms of ecosystem services. We examine the areal pattern of land types in a coastal region located in southern Iran over a period of 14 years to predict future loss and gain in land types to the year 2025. We applied a CA–Markov model to simulate and predict mangrove forest change. Landsat satellite images from 2000 to 2014 were used to analyze the land cover changes between soil, open water and mangroves. Major changes during this period were observed in soil and water which could be attributed to rising sea level. Furthermore, the mangrove area in the more seaward position was converted to open water due to sea-level rise. A cellular automata model was then used to predict the land cover changes that would occur by the year 2025. Results demonstrated that approximately 21 ha of mangrove area will be converted to open water, while mangroves are projected to expand by approximately 28 ha in landward direction. These changes need to be delineated to better inform precise mitigation and adaptation measures.  相似文献   

Synthesis of biochar from sugarcane filter-cake and its impacts on physiological performance of lettuce (<Emphasis Type="Italic">Lettuce sativa</Emphasis>) grown on cadmium contaminated soil     

Anum Sehar  Rukhsanda Aziz  Muhammad Tariq Rafiq  Muhammad Mazhar Hussain  Muhammad Rizwan  Adiba Khan Sehrish  Muhammad Khalid Rafiq  Jalal ud Din  Qaiser Hussain  Mohammad I. Al-Wabel  Shafaqat Ali 《Arabian Journal of Geosciences》2018,11(20):634

Soil contamination with cadmium has become major concern all over the world because of its adverse impacts on ecosystem health and agricultural land. Soil amendment with biochar may have varied effects on physical and chemical properties of soil. The objective of the study was to explore the impact of sugarcane filter-cake biochar on physiological performance and growth of lettuce in an aged soil. Four different doses (0, 1.5%, 3%, and 5%) of biochar were used in the soil and conditioned for 1 month. After this, lettuce seedlings were grown in the soil. The results showed that the biochar treatment improved the fresh and dry biomass of leaves and roots as well as plant height while diminished the bioavailability of cadmium from the soil. As compared to control, biochar significantly enhanced the chlorophyll content in lettuce leaves. Due to the biochar amendment, the oxidative stress decreased in lettuce shoots over the control. As compared to control, concentration of cadmium in lettuce significantly decreased after the application of biochar. It was concluded that biochar could mitigate the toxicity of cadmium in lettuce by altering the biochemical and physiological processes in cadmium contaminated soil.  相似文献   

Application of artificial intelligence techniques for predicting the flyrock distance caused by blasting operation   总被引：2，自引：0，他引：2  

Ebrahim Ghasemi  Hasel Amini  Mohammad Ataei  Reza Khalokakaei 《Arabian Journal of Geosciences》2014,7(1):193-202

Flyrock arising from blasting operations is one of the crucial and complex problems in mining industry and its prediction plays an important role in the minimization of related hazards. In past years, various empirical methods were developed for the prediction of flyrock distance using statistical analysis techniques, which have very low predictive capacity. Artificial intelligence (AI) techniques are now being used as alternate statistical techniques. In this paper, two predictive models were developed by using AI techniques to predict flyrock distance in Sungun copper mine of Iran. One of the models employed artificial neural network (ANN), and another, fuzzy logic. The results showed that both models were useful and efficient whereas the fuzzy model exhibited high performance than ANN model for predicting flyrock distance. The performance of the models showed that the AI is a good tool for minimizing the uncertainties in the blasting operations.  相似文献